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Mathematics Faculty: Publications since January 2020

List all publications in the database.    :chronological  alphabetical  combined  bibtex listing:

Agarwal, Pankaj K.

  1. Agarwal, PK; Hu, X; Sintos, S; Yang, J, Dynamic enumeration of similarity joins, Leibniz International Proceedings in Informatics, Lipics, vol. 198 (July, 2021) [doi]  [abs]
  2. Agarwal, PK; Steiger, A, An output-sensitive algorithm for computing the union of cubes and fat boxes in 3D, Leibniz International Proceedings in Informatics, Lipics, vol. 198 (July, 2021), ISBN 9783959771955 [doi]  [abs]
  3. Agarwal, PK; Kaplan, H; Sharir, M, Union of Hypercubes and 3D Minkowski Sums with Random Sizes, Discrete & Computational Geometry, vol. 65 no. 4 (June, 2021), pp. 1136-1165 [doi]  [abs]
  4. Gao, J; Sintos, S; Agarwal, PK; Yang, J, Durable top-k instant-stamped temporal records with user-specified scoring functions, Proceedings International Conference on Data Engineering, vol. 2021-April (April, 2021), pp. 720-731 [doi]  [abs]
  5. Gao, J; Xu, Y; Agarwal, PK; Yang, J, Efficiently Answering Durability Prediction Queries, Proceedings of the Acm Sigmod International Conference on Management of Data (January, 2021), pp. 591-604 [doi]  [abs]
  6. Agarwal, PK; Aronov, B; Geft, T; Halperin, D, On two-handed planar assembly partitioning with connectivity constraints, Proceedings of the Annual Acm Siam Symposium on Discrete Algorithms (January, 2021), pp. 1740-1756, ISBN 9781611976465  [abs]
  7. Agarwal, PK; Sharir, M; Steiger, A, Decomposing the complement of the union of cubes in three dimensions, Proceedings of the Annual Acm Siam Symposium on Discrete Algorithms (January, 2021), pp. 1425-1444, ISBN 9781611976465  [abs]
  8. Agarwal, PK; Chang, HC; Munagala, K; Taylor, E; Welzl, E, Clustering under perturbation stability in near-linear time, Leibniz International Proceedings in Informatics, Lipics, vol. 182 (December, 2020) [doi]  [abs]
  9. Lowe, A; Svendsen, SC; Agarwal, PK; Arge, L, 1D and 2D Flow Routing on a Terrain, Gis: Proceedings of the Acm International Symposium on Advances in Geographic Information Systems (November, 2020), pp. 5-14 [doi]  [abs]
  10. Lowe, A; Agarwal, PK; Rav, M, Flood-risk analysis on terrains, Communications of the Acm, vol. 63 no. 9 (September, 2020), pp. 94-102 [doi]
  11. Agarwal, PK; Sintos, S; Steiger, A, Efficient Indexes for Diverse Top-k Range Queries, Proceedings of the Acm Sigact Sigmod Sigart Symposium on Principles of Database Systems (June, 2020), pp. 213-227, ISBN 9781450371087 [doi]  [abs]
  12. Agarwal, PK; Chang, HC; Suri, S; Xiao, A; Xue, J, Dynamic geometric set cover and hitting set, Leibniz International Proceedings in Informatics, Lipics, vol. 164 (June, 2020) [doi]  [abs]
  13. Raghvendra, S; Agarwal, PK, A Near-linear Time ϵ-Approximation Algorithm for Geometric Bipartite Matching, Journal of the Acm, vol. 67 no. 3 (May, 2020) [doi]  [abs]
  14. Agarwal, PK; Pan, J, Near-Linear Algorithms for Geometric Hitting Sets and Set Covers, Discrete & Computational Geometry, vol. 63 no. 2 (March, 2020), pp. 460-482 [doi]  [abs]
  15. Sintos, S; Agarwal, PK; Yang, J, Selecting data to clean for fact checking: Minimizing uncertainty vs. maximizing surprise, Proceedings of the Vldb Endowment, vol. 12 no. 13 (January, 2020), pp. 2408-2421 [doi]  [abs]

Agazzi, Andrea

  1. Salazar, M; Paccagnan, D; Agazzi, A; Heemels, WPMH, Urgency-aware optimal routing in repeated games through artificial currencies, European Journal of Control (January, 2021) [doi]  [abs]
  2. AGAZZI, A; MATTINGLY, JC, SEEMINGLY STABLE CHEMICAL KINETICS CAN BE STABLE, MARGINALLY STABLE, OR UNSTABLE, Communications in Mathematical Sciences, vol. 18 no. 6 (January, 2020), pp. 1605-1642, International Press of Boston [doi]  [abs]
  3. Agazzi, A; Lu, J, Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime., Corr, vol. abs/2010.11858 (2020)

Akin, Victoria S

  1. Akin, V, An algebraic characterization of the point-pushing subgroup, Journal of Algebra, vol. 541 (January, 2020), pp. 98-125 [doi]  [abs]

Aquino, Wilkins

  1. Khodayi-Mehr, R; Urban, MW; Zavlanos, MM; Aquino, W, Plane wave elastography: a frequency-domain ultrasound shear wave elastography approach., Physics in Medicine and Biology, vol. 66 no. 12 (June, 2021) [doi]  [abs]
  2. Bunting, G; Miller, ST; Walsh, TF; Dohrmann, CR; Aquino, W, Novel strategies for modal-based structural material identification, Mechanical Systems and Signal Processing, vol. 149 (February, 2021) [doi]  [abs]
  3. Sanders, C; Norato, J; Walsh, T; Aquino, W, An error-in-constitutive equations strategy for topology optimization for frequency-domain dynamics, Computer Methods in Applied Mechanics and Engineering, vol. 372 (December, 2020) [doi]  [abs]
  4. Ghavami, S; Babaniyi, O; Adabi, S; Rosen, D; Alizad, A; Aquino, W; Fatemi, M, Ultrasound elastography using a regularized modified error in constitutive equations (MECE) approach: a comprehensive phantom study., Physics in Medicine and Biology, vol. 65 no. 22 (November, 2020), pp. 225026 [doi]  [abs]
  5. Chen, MJ; Aquino, W; Walsh, TF; Reu, PL; Johnson, KL; Rouse, JW; Jared, BH; Bishop, JE, A Generalized Stress Inversion Approach with Application to Residual Stress Estimation, Journal of Applied Mechanics, vol. 87 no. 11 (November, 2020) [doi]  [abs]
  6. Calkins, L; Khodayi-Mehr, R; Aquino, W; Zavlanos, MM, Sensor Planning for Model-Based Acoustic Source Identification, Proceedings of the American Control Conference, vol. 2020-July (July, 2020), pp. 2679-2684, ISBN 9781538682661 [doi]  [abs]

Arlotto, Alessandro

  1. Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem with equal rewards, Stochastic Systems, vol. 10 no. 2 (June, 2020), pp. 170-191 [doi]  [abs]

Beale, J. Thomas

  1. Beale, JT, Solving partial differential equations on closed surfaces with planar cartesian grids, Siam Journal on Scientific Computing, vol. 42 no. 2 (January, 2020), pp. A1052-A1070 [doi]  [abs]

Bendich, Paul L

  1. Solomon, E; Bendich, P, Geometric fusion via joint delay embeddings, Proceedings of 2020 23rd International Conference on Information Fusion, Fusion 2020 (July, 2020) [doi]  [abs]
  2. Yao, L; Bendich, P, Graph Spectral Embedding for Parsimonious Transmission of Multivariate Time Series, Ieee Aerospace Conference Proceedings (March, 2020), ISBN 9781728127347 [doi]  [abs]
  3. Blasch, E; Grewe, LL; Waltz, EL; Bendich, P; Pavlovic, V; Kadar, I; Chong, CY, Machine learning in/with information fusion for infrastructure understanding, panel summary, Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics, vol. 11423 (January, 2020), ISBN 9781510636231 [doi]  [abs]

Bertozzi, Andrea L

  1. J. B. Greer and A. L. Bertozzi, H-1 solutions of a class of fourth order nonlinear equations for image processing, Discrete And Continuous Dynamical Systems, vol. 10 no. 1-2 (2004), pp. 349 -- 366

Bookman, Jack

  1. Schott, S; Slate Young, E; Bookman, J; Peterson, B, Evaluating a Large-Scale Multi-Institution Project: Challenges Faced and Lessons Learned, The Journal of Mathematics and Science: Collaborative Explorations (Jmsce), vol. 16 no. 1 (2020) [doi]  [abs]

Bray, Clark

  1. Bray, C; Butscher, A; Rubinstein-Salzedo, S, Algebraic Topology (June, 2021), pp. 209 pages, SPRINGER, ISBN 3030706079  [abs]

Bryant, Robert   (search)

  1. Bryant, RL; Foulon, P; Ivanov, SV; Matveev, VS; Ziller, W, Geodesic behavior for Finsler metrics of constant positive flag curvature on S2, Journal of Differential Geometry, vol. 117 no. 1 (January, 2021), pp. 1-22 [doi]  [abs]
  2. Acharya, BS; Bryant, RL; Salamon, S, A circle quotient of a G2 cone, Differential Geometry and Its Applications, vol. 73 (December, 2020) [doi]  [abs]
  3. Bryant, RL; Clelland, JN, Flat metrics with a prescribed derived coframing, Symmetry, Integrability and Geometry: Methods and Applications, vol. 16 (January, 2020) [doi]  [abs]

Calderbank, Robert

  1. Nguyen, DM; Calderbank, R; Deligiannis, N, Geometric Matrix Completion With Deep Conditional Random Fields., Ieee Transactions on Neural Networks and Learning Systems, vol. 31 no. 9 (September, 2020), pp. 3579-3593 [doi]  [abs]

Ciocanel, Maria-Veronica

  1. Mallory, K; Rubin Abrams, J; Schwartz, A; Ciocanel, M-V; Volkening, A; Sandstede, B, Influenza spread on context-specific networks lifted from interaction-based diary data., Royal Society Open Science, vol. 8 no. 1 (January, 2021), pp. 191876, The Royal Society [doi]  [abs]
  2. Ciocanel, M-V; Topaz, CM; Santorella, R; Sen, S; Smith, CM; Hufstetler, A, JUSTFAIR: Judicial System Transparency through Federal Archive Inferred Records., Plos One, vol. 15 no. 10 (October, 2020), pp. e0241381-e0241381 [doi]  [abs]
  3. Ciocanel, M-V; Fricks, J; Kramer, PR; McKinley, SA, Renewal Reward Perspective on Linear Switching Diffusion Systems in Models of Intracellular Transport., Bulletin of Mathematical Biology, vol. 82 no. 10 (September, 2020), pp. 126 [doi]  [abs]
  4. Topaz, CM; Ciocanel, V; Cohen, P; Ott, M; Rodriguez, N, Institute for the Quantitative Study of Inclusion, Diversity, and Equity (QSIDE), Notices of the American Mathematical Society, vol. 67 no. 02 (February, 2020), pp. 1-1, American Mathematical Society (AMS) [doi]
  5. Ciocanel, M-V; Jung, P; Brown, A, A Mechanism for Neurofilament Transport Acceleration through Nodes of Ranvier, Cell Regulation, vol. 31 no. 7 (January, 2020), American Society for Cell Biology [doi]  [abs]
  6. Adams, H; Ciocanel, M-V; Topaz, C; Ziegelmeier, L, Topological Data Analysis of Collective Motion, Siam News (January, 2020), SIAM News

Cook, Nicholas A   (search)

  1. Cook, N; Dembo, A, Large deviations of subgraph counts for sparse Erdős–Rényi graphs, Advances in Mathematics, vol. 373 (October, 2020) [doi]  [abs]
  2. Cook, N; Zeitouni, O, Maximum of the Characteristic Polynomial for a Random Permutation Matrix, Communications on Pure and Applied Mathematics, vol. 73 no. 8 (August, 2020), pp. 1660-1731 [doi]  [abs]

Daubechies, Ingrid

  1. Cheng, C; Daubechies, I; Dym, N; Lu, J, Stable phase retrieval from locally stable and conditionally connected measurements, Applied and Computational Harmonic Analysis, vol. 55 (November, 2021), pp. 440-465 [doi]  [abs]
  2. Fornasier, M; Vybíral, J; Daubechies, I, Robust and resource efficient identification of shallow neural networks by fewest samples, Information and Inference, vol. 10 no. 2 (June, 2021), pp. 625-695 [doi]  [abs]
  3. Fulwood, EL; Shan, S; Winchester, JM; Kirveslahti, H; Ravier, R; Kovalsky, S; Daubechies, I; Boyer, DM, Insights from macroevolutionary modelling and ancestral state reconstruction into the radiation and historical dietary ecology of Lemuriformes (Primates, Mammalia)., Bmc Ecology and Evolution, vol. 21 no. 1 (April, 2021), pp. 60 [doi]  [abs]
  4. Daubechies, I; DeVore, R; Foucart, S; Hanin, B; Petrova, G, Nonlinear Approximation and (Deep) ReLU Networks, Constructive Approximation (January, 2021) [doi]  [abs]
  5. Pu, W; Sober, B; Daly, N; Higgitt, C; Daubechies, I; Rodrigues, MRD, A connected auto-encoders based approach for image separation with side information: With applications to art investigation, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2020-May (May, 2020), pp. 2213-2217, ISBN 9781509066315 [doi]  [abs]

Ding, Xiucai

  1. Ding, X; Zhou, Z, Estimation and inference for precision matrices of nonstationary time series, The Annals of Statistics, vol. 48 no. 4 (August, 2020), pp. 2455-2477, Institute of Mathematical Statistics [doi]
  2. Ding, X, High dimensional deformed rectangular matrices with applications in matrix denoising, Bernoulli, vol. 26 no. 1 (February, 2020), pp. 387-417, Bernoulli Society for Mathematical Statistics and Probability [doi]

Dolbow, John E.

  1. Talamini, B; Tupek, MR; Stershic, AJ; Hu, T; Foulk, JW; Ostien, JT; Dolbow, JE, Attaining regularization length insensitivity in phase-field models of ductile failure, Computer Methods in Applied Mechanics and Engineering, vol. 384 (October, 2021) [doi]  [abs]
  2. Geelen, R; Plews, J; Dolbow, J, Scale-bridging with the extended/generalized finite element method for linear elastodynamics, Computational Mechanics, vol. 68 no. 2 (August, 2021), pp. 295-310 [doi]  [abs]
  3. Hu, G; Talamini, B; Stershic, AJ; Tupek, MR; Dolbow, JE, A Variational Phase-Field Model For Ductile Fracture with Coalescence Dissipation (January, 2021) [doi]  [abs]
  4. Hu, T; Guilleminot, J; Dolbow, JE, A phase-field model of fracture with frictionless contact and random fracture properties: Application to thin-film fracture and soil desiccation, Computer Methods in Applied Mechanics and Engineering, vol. 368 (August, 2020) [doi]  [abs]
  5. Geelen, R; Plews, J; Tupek, M; Dolbow, J, An extended/generalized phase-field finite element method for crack growth with global-local enrichment, International Journal for Numerical Methods in Engineering, vol. 121 no. 11 (June, 2020), pp. 2534-2557 [doi]  [abs]
  6. Jiang, W; Spencer, BW; Dolbow, JE, Ceramic nuclear fuel fracture modeling with the extended finite element method, Engineering Fracture Mechanics, vol. 223 (January, 2020) [doi]  [abs]
  7. Guilleminot, J; Dolbow, JE, Data-driven enhancement of fracture paths in random composites, Mechanics Research Communications, vol. 103 (January, 2020) [doi]  [abs]

Donald, Bruce R.

  1. L. Wang and R. Mettu and B. R. Donald, An Algebraic Geometry Approach to Protein Backbone Structure Determination from NMR Data, in Proceedings of the IEEE Computational Systems Bioinformatics Conference (CSB) (2005), pp. 235--246, Stanford, CA
  2. B. R. Donald and C. Levey and C. McGray and I. Paprotny and D. Rus, A Steerable, Untethered, 250 $\times$ 60 $\mu$m MEMS Mobile Micro-Robot, in Proceedings of the 12th {\it International Symposium of Robotics Research (ISRR)} (2005), San Francisco, CA.
  3. C. Langmead and B. R. Donald, A Framework for Automated NMR Resonance Assignments and 3D Structural Homology Detection (2004), Ventura, CA ({\poster} {\it The Gordon Conference on Computational Methods in Biomolecular NMR}.)
  4. L. Wang and R. Mettu and R. Lilien and A. Yan and B. R. Donald, Exact Solutions for Internuclear Vectors and Dihedral Angles from Two RDCs and Their Application in a Systematic Search Algorithm for Determining Protein Backbone Structure (2004), Ventura, CA ({\poster} {\it The Gordon Conference on Computational Methods in Biomolecular NMR}.)
  5. Ryan H. Lilien and Mohini Sridharan and Bruce R. Donald, {Identification of Novel Small Molecule Inhibitors of Core-Binding Factor Dimerization by Computational Screening against NMR Molecular Ensembles} no. TR2004-492 (2004), Hanover, NH [pdf]
  6. B. R. Donald, Plenary lecture: Algorithmic Challenges in Structural Molecular Biology and Proteomics, in Proceedings of the Sixth International Workshop on the Algorithmic Foundations of Robotics (WAFR) (2004), pp. 1--10, University of Utrecht, Utrecht/Zeist, The Netherlands
  7. C. Langmead and B. R. Donald, High-Throughput 3D homology Detection via NMR Resonance Assignment, in Currents in Computational Molecular Biology, 2004, Eighth Annual International Conference on Research in Computational Molecular Biology (RECOMB), edited by A. Gramada and P. Bourne (2004), pp. 522, San Diego
  8. A. Yan and C. Langmead and B. R. Donald, A Probability-Based Similarity Measure for Saupe Alignment Tensors with Applications to Residual Dipolar Couplings in NMR Structural Biology, in Currents in Computational Molecular Biology, 2004, Eighth Annual International Conference on Research in Computational Molecular Biology (RECOMB), edited by A. Gramada and P. Bourne (2004), pp. 437--438, San Diego
  9. L. Wang and B. R. Donald, Analysis of a Systematic Search-Based Algorithm for Determining Protein Backbone Structure from a Minimal Number of Residual Dipolar Couplings, in Proceedings of the IEEE Computational Systems Bioinformatics Conference (CSB) (2004), pp. 319--330, Stanford, CA
  10. A. Anderson and R. Lilien and V. Popov and B. R. Donald, Ensembles of Active Site Conformations Allow Structure-Based Redesign and Drug Design (2003), New Orleans ({\poster} {\it 225th American Chemical Society National Meeting}.)
  11. Christopher J. Langmead and Bruce R. Donald, {An Improved Nuclear Vector Replacement Algorithm for Nuclear Magnetic Resonance Assignment} no. TR2004-494 (2003), Hanover, NH [pdf]
  12. B. R. Donald and C. Levey and C. McGray and D. Rus and M. Sinclair, Untethered Micro-Actuators for Autonomous Micro-robot Locomotion: Design, Fabrication, Control, and Performance, in Proceedings of the 11th {\it International Symposium of Robotics Research} (2003), Siena, Italy
  13. R. Lilien and A. Anderson and B. Donald, Modeling Protein Flexibility for Structure-Based Active Site Redesign, in Currents in Computational Molecular Biology, The Sixth Annual International Conference on Research in Computational Molecular Biology (RECOMB), edited by L. Florea and others (2002), pp. 122-123, Washington DC
  14. C. J. Langmead and B. R. Donald, Time-frequency Analysis of Protein NMR Data (2000) ({\poster} {\it The 8th Int'l Conf. on Intelligent Sys. for Mol. Biol. ({ISMB-2000})}.)
  15. C. Bailey-Kellogg and A. Widge and J. J. {Kelley III} and M. J. Berardi and J. H. Bushweller and B. R. Donald, The NOESY Jigsaw: Automated Protein Secondary Structure and Main-Chain Assignment from Sparse, Unassigned NMR Data (2000) ({\poster} {\it The 8th Int'l Conf. on Intelligent Sys. for Mol. Biol. ({ISMB-2000})}.)
  16. R. Lilien and M. Sridharan and X. Huang and J. H. Bushweller and B. R. Donald, Computational Screening Studies for Core Binding Factor Beta: Use of Multiple Conformations to Model Receptor Flexibility (2000) ({\poster} {\it The 8th Int'l Conf. on Intelligent Sys. for Mol. Biol. ({ISMB-2000})}.)
  17. C. Bailey-Kellogg and A. Widge and J. J. {Kelley III} and M. J. Berardi and J. H. Bushweller and B. R. Donald, The NOESY Jigsaw: Automated Protein Secondary Structure and Main-Chain Assignment from Sparse, Unassigned NMR Data, in The Fourth Annual International Conference on Research in Computational Molecular Biology ({RECOMB-2000}) (2000), pp. 33--44
  18. C. Bailey-Kellogg and F. Zhao and B. R. Donald, Spatial Aggregation in Scientific Data Mining, in Proceedings of the First SIAM Conference on Computational Science and Engineering (2000), Washington, DC
  19. K.-F.~B{\"o}hringer and B.~R.~Donald and N.~C.~MacDonald, {\em Programmable Vector Fields for Distributed Manipulation, with Applications to MEMS Actuator Arrays and Vibratory Parts Feeders}, International Journal of Robotics Research, vol. 18 no. 2 (1999)
  20. K.-F.~B{\"o}hringer and B.~R.~Donald and F.~Lamiraux and L.~Kavraki, Part Orientation with One or Two Stable Equilibria Using Programmable Force Fields, in IEEE International Conference on Robotics and Automation, Workshop on Distributed Manipulation (1999)
  21. K.-F. B{\"o}hringer and B. R. Donald and F. Lamiraux and L. Kavraki, Part Orientation with One or Two Stable Equilibria Using Programmable Vector Fields, in IEEE International Conference on Robotics and Automation, Workshop on Distributed Manipulation (1999), Detroit
  22. K.-F.~B{\"o}hringer and B.~R.~Donald and F.~Lamiraux and L.~Kavraki, A Single Universal Force Field Can Uniquely Pose Any Part Up To Symmetry, in 9th International Symposium of Robotics Research (ISRR) (1999)
  23. B. R. Donald and L. Gariepy and D. Rus, Experiments in Constrained Prehensile Manipulation: Distributed Manipulation with Ropes, in IEEE International Conference on Robotics and Automation, Workshop on Distributed Manipulation (1999), Detroit
  24. J.~Suh and B.~Darling and K.-F.~B{\"o}hringer and B.~R.~Donald and H.~Baltes and G.~Kovacs, CMOS Integrated Organic Ciliary Actuator Array as a General-Purpose Micromanipulation Tool, in IEEE International Conference on Robotics and Automation, Workshop on Distributed Manipulation (1999)
  25. K.-F. B{\"o}hringer and B. R. Donald, Algorithmic MEMS, in Proceedings of the 3rd International Workshop on the Algorithmic Foundations of Robotics WAFR (1998), Houston, TX
  26. A. Briggs and B. R. Donald, Robust Geometric Algorithms for Sensor Planning, in Proceedings of the International Workshop on the Algorithmic Foundations of Robotics WAFR (1996), Toulouse, France
  27. B. R. Donald and J. Jennings and D. Rus, Cooperating Autonomous Mobile Robots: Theory and Experiments (1994), MIT, Cambridge, MA (Poster, {\it NSF Design and Manufacturing Grantees Conference}.)
  28. B. R. Donald and D. Pai, The Motion of Planar Compliantly-Connected Rigid Bodies in Contact, with Applications to Automatic Fastening, International Journal of Robotics Research, vol. 12 no. 4 (1993), pp. 307--338
  29. R. Brown and P. Chew and B. R. Donald, Mobile Robots, Map-making, Shape Metrics, and Localization, in Proceedings of the International Association of Science and Technology for Development (IASTED) International Conference on Robotics and Manufacturing (1993), Oxford, England
  30. J. Jennings and D. Rus and B. R. Donald, Experimental Information Invariants for Cooperating Autonomous Mobile Robots, in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Dynamically Interacting Robots (1993), Chambery, France
  31. B. R. Donald and J. Jennings and D. Rus, Towards a Theory of Information Invariants for Cooperating Autonomous Mobile Robots, in Proceedings of the International Symposium of Robotics Research ISRR (1993), Hidden Valley, PA
  32. B. R. Donald, Robot Motion Planning, IEEE Trans. on Robotics and Automation, vol. 8 no. 2 (1992)
  33. J. Canny and B. R. Donald and G. Ressler, A Rational Rotation Method for Robust Geometric Algorithms, in Proc. ACM Symposium on Computational Geometry (1992), pp. 251--260, Berlin
  34. J. Jennings and B. R. Donald, Programming Autonomous Agents: A theory of Perceptual Equivalence, in Proceedings of the 1st AAAI Fall Symposium on Sensory Aspects of Robotic Intelligence (1991), Asilomar, CA
  35. B. R. Donald and P. Xavier, A Provably Good Approximation Algorithm for Optimal-Time Trajectory Planning, in Proc. IEEE International Conference on Robotics and Automation (1989), pp. 958--964, Scottsdale, AZ
  36. B. R. Donald, The Complexity of Planar Compliant Motion Planning with Uncertainty, in Proc. 4th ACM Symposium on Computational Geometry (1988), pp. 309--318, Urbana. IL
  37. B. R. Donald, A Theory of Error Detection and Recovery: Robot Motion Planning with Uncertainty in the Geometric Models of the Robot and Environment, in Proceedings of the International Workshop on Geometric Reasoning (1986), Oxford University, England

Dunson, David B.   (search)

  1. Dunson, DB; Wu, HT; Wu, N, Spectral convergence of graph Laplacian and heat kernel reconstruction in L from random samples, Applied and Computational Harmonic Analysis, vol. 55 (November, 2021), pp. 282-336 [doi]  [abs]
  2. Aliverti, E; Lum, K; Johndrow, JE; Dunson, DB, Removing the influence of group variables in high-dimensional predictive modelling, Journal of the Royal Statistical Society: Series a (Statistics in Society), vol. 184 no. 3 (July, 2021), pp. 791-811 [doi]  [abs]
  3. Moran, KR; Turner, EL; Dunson, D; Herring, AH, Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data., Journal of the Royal Statistical Society. Series C, Applied Statistics, vol. 70 no. 3 (June, 2021), pp. 532-557 [doi]  [abs]
  4. Lee, K; Lin, L; Dunson, D, Maximum pairwise bayes factors for covariance structure testing, Electronic Journal of Statistics, vol. 15 no. 2 (January, 2021), pp. 4384-4419 [doi]  [abs]
  5. Ferrari, F; Dunson, DB, Bayesian Factor Analysis for Inference on Interactions, Journal of the American Statistical Association, vol. 116 no. 535 (January, 2021), pp. 1521-1532 [doi]  [abs]
  6. Jauch, M; Hoff, PD; Dunson, DB, Monte Carlo Simulation on the Stiefel Manifold via Polar Expansion, Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 30 no. 3 (January, 2021), pp. 622-631 [doi]  [abs]
  7. Paganin, S; Herring, AH; Olshan, AF; Dunson, DB, Centered Partition Processes: Informative Priors for Clustering (with Discussion), Bayesian Analysis, vol. 16 no. 1 (January, 2021), pp. 301-370 [doi]  [abs]
  8. Sen, D; Sachs, M; Lu, J; Dunson, DB, Efficient posterior sampling for high-dimensional imbalanced logistic regression., Biometrika, vol. 107 no. 4 (December, 2020), pp. 1005-1012 [doi]  [abs]
  9. Mukhopadhyay, M; Li, D; Dunson, DB, Estimating densities with non-linear support by using Fisher–Gaussian kernels, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 82 no. 5 (December, 2020), pp. 1249-1271 [doi]  [abs]
  10. Ferrari, F; Dunson, DB, IDENTIFYING MAIN EFFECTS AND INTERACTIONS AMONG EXPOSURES USING GAUSSIAN PROCESSES., The Annals of Applied Statistics, vol. 14 no. 4 (December, 2020), pp. 1743-1758 [doi]  [abs]
  11. Li, D; Dunson, D, Classification via local manifold approximation, vol. 107 no. 4 (December, 2020), pp. 1013-1020 [doi]  [abs]
  12. Roy, A; Dunson, DB, Nonparametric graphical model for counts., Journal of Machine Learning Research, vol. 21 (December, 2020)  [abs]
  13. Legramanti, S; Durante, D; Dunson, DB, Bayesian cumulative shrinkage for infinite factorizations., Biometrika, vol. 107 no. 3 (September, 2020), pp. 745-752 [doi]  [abs]
  14. Dunson, D; Papamarkou, T, Discussions, International Statistical Review, vol. 88 no. 2 (August, 2020), pp. 321-324 [doi]
  15. Binette, O; Pati, D; Dunson, DB, Bayesian closed surface fitting through tensor products, Journal of Machine Learning Research, vol. 21 (July, 2020), pp. 1-26  [abs]
  16. Aliverti, E; Tilson, JL; Filer, DL; Babcock, B; Colaneri, A; Ocasio, J; Gershon, TR; Wilhelmsen, KC; Dunson, DB, Projected t-SNE for batch correction., Bioinformatics (Oxford, England), vol. 36 no. 11 (June, 2020), pp. 3522-3527 [doi]  [abs]
  17. Nishimura, A; Dunson, DB; Lu, J, Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods, Biometrika, vol. 107 no. 2 (June, 2020), pp. 365-380 [doi]  [abs]
  18. Dunson, DB; Johndrow, JE, The Hastings algorithm at fifty, Biometrika, vol. 107 no. 1 (March, 2020), pp. 1-23 [doi]  [abs]
  19. Duan, LL; Young, AL; Nishimura, A; Dunson, DB, Bayesian constraint relaxation., Biometrika, vol. 107 no. 1 (March, 2020), pp. 191-204 [doi]  [abs]
  20. Tikhonov, G; Duan, L; Abrego, N; Newell, G; White, M; Dunson, D; Ovaskainen, O, Computationally efficient joint species distribution modeling of big spatial data., Ecology, vol. 101 no. 2 (February, 2020), pp. e02929 [doi]  [abs]
  21. Li, M; Dunson, DB, Comparing and weighting imperfect models using D-probabilities., Journal of the American Statistical Association, vol. 115 no. 531 (January, 2020), pp. 1349-1360 [doi]  [abs]
  22. Mukhopadhyay, M; Dunson, DB, Targeted Random Projection for Prediction From High-Dimensional Features, Journal of the American Statistical Association, vol. 115 no. 532 (January, 2020), pp. 1998-2010 [doi]  [abs]
  23. Jauch, M; Hoff, PD; Dunson, DB, Random orthogonal matrices and the Cayley transform, Bernoulli, vol. 26 no. 2 (January, 2020), pp. 1560-1586 [doi]  [abs]
  24. Nishimura, A; Dunson, D, Recycling Intermediate Steps to Improve Hamiltonian Monte Carlo, Bayesian Analysis, vol. 15 no. 4 (January, 2020), pp. 1087-1108 [doi]  [abs]
  25. Tam, E; Dunson, D, Fiedler regularization: Learning neural networks with graph sparsity, 37th International Conference on Machine Learning, Icml 2020, vol. PartF168147-12 (January, 2020), pp. 9288-9297, ISBN 9781713821120  [abs]
  26. Talbot, A; Dunson, D; Dzirasa, K; Carlson, D, Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity, Arxiv Preprint Arxiv:2004.05209, vol. abs/2004.05209 (2020)

Durrett, Richard T.

  1. Tung, H-R; Durrett, R, Signatures of neutral evolution in exponentially growing tumors: A theoretical perspective., Plos Computational Biology, vol. 17 no. 2 (February, 2021), pp. e1008701 [doi]  [abs]
  2. Agarwal, P; Simper, M; Durrett, R, The q-voter model on the torus, Electronic Journal of Probability, vol. 26 (January, 2021) [doi]  [abs]
  3. Huang, X; Durrett, R, Motion by mean curvature in interacting particle systems, Probability Theory and Related Fields (January, 2021) [doi]  [abs]
  4. Borowiak, M; Ning, F; Pei, J; Zhao, S; Tung, H-R; Durrett, R, Controlling the spread of COVID-19 on college campuses., Mathematical Biosciences and Engineering, vol. 18 no. 1 (December, 2020), pp. 551-563 [doi]  [abs]
  5. Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice, Stochastic Processes and Their Applications, vol. 130 no. 2 (February, 2020), pp. 488-502 [doi]  [abs]
  6. Huang, X; Durrett, R, The contact process on periodic trees, Electronic Communications in Probability, vol. 25 (January, 2020) [doi]  [abs]
  7. Durrett, R; Junge, M; Tang, S, Coexistence in chase-escape, Electronic Communications in Probability, vol. 25 (January, 2020) [doi]  [abs]

Dym, Nadav

  1. Cheng, C; Daubechies, I; Dym, N; Lu, J, Stable phase retrieval from locally stable and conditionally connected measurements, Applied and Computational Harmonic Analysis, vol. 55 (November, 2021), pp. 440-465 [doi]  [abs]
  2. Dym, N; Sober, B; Daubechies, I, Expression of Fractals Through Neural Network Functions, Ieee Journal on Selected Areas in Information Theory, vol. 1 no. 1 (May, 2020), pp. 57-66, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  3. Dym, N; Maron, H, On the Universality of Rotation Equivariant Point Cloud Networks., Corr, vol. abs/2010.02449 (2020)

Gao, Yuan   (search)

  1. Dong, H; Gao, Y, Existence and uniqueness of bounded stable solutions to the Peierls–Nabarro model for curved dislocations, Calculus of Variations and Partial Differential Equations, vol. 60 no. 2 (April, 2021) [doi]  [abs]
  2. Gao, Y; Lu, XY; Wang, C, Regularity and monotonicity for solutions to a continuum model of epitaxial growth with nonlocal elastic effects, Advances in Calculus of Variations (January, 2021) [doi]  [abs]
  3. Gao, Y; Liu, JG, Gradient flow formulation and second order numerical method for motion by mean curvature and contact line dynamics on rough surface, Interfaces and Free Boundaries, vol. 23 no. 1 (January, 2021), pp. 103-158 [doi]  [abs]
  4. Gao, Y; Liu, JG, Large Time Behavior, Bi-Hamiltonian Structure, and Kinetic Formulation for a Complex Burgers Equation, Quarterly of Applied Mathematics, vol. 79 no. 1 (May, 2020), pp. 120-123, American Mathematical Society (AMS) [doi]  [abs]
  5. Gao, Y; Liu, JG; Luo, T; Xiang, Y, Revisit of the peierls-nabarro model for edge dislocations in Hilbert space, Discrete and Continuous Dynamical Systems Series B, vol. 22 no. 11 (January, 2020) [doi]  [abs]
  6. Gao, Y; Liu, J-G, Long time behavior of dynamic solution to Peierls–Nabarro dislocation model, Methods and Applications of Analysis, vol. 27 no. 2 (2020), pp. 161-198, International Press of Boston [doi]

Ge, Rong

  1. Azar, Y; Ganesh, A; Ge, R; Panigrahi, D, Online Service with Delay, Acm Transactions on Algorithms, vol. 17 no. 3 (August, 2021) [doi]  [abs]
  2. Jin, C; Netrapalli, P; Ge, R; Kakade, SM; Jordan, MI, On Nonconvex Optimization for Machine Learning, Journal of the Acm, vol. 68 no. 2 (March, 2021) [doi]  [abs]
  3. Ge, R; Lee, H; Lu, J, Estimating normalizing constants for log-concave distributions: Algorithms and lower bounds, Proceedings of the Annual Acm Symposium on Theory of Computing (June, 2020), pp. 579-586 [doi]  [abs]
  4. Frandsen, A; Ge, R, Optimization landscape of Tucker decomposition, Mathematical Programming (January, 2020) [doi]  [abs]
  5. Ge, R; Ma, T, On the optimization landscape of tensor decompositions, Mathematical Programming (January, 2020) [doi]  [abs]
  6. Wang, X; Wu, C; Lee, JD; Ma, T; Ge, R, Beyond lazy training for over-parameterized tensor decomposition, Advances in Neural Information Processing Systems, vol. 2020-December (January, 2020)  [abs]
  7. Cheng, Y; Diakonikolas, I; Ge, R; Soltanolkotabi, M, High-dimensional robust mean estimation via gradient descent, 37th International Conference on Machine Learning, Icml 2020, vol. PartF168147-3 (January, 2020), pp. 1746-1756, ISBN 9781713821120  [abs]
  8. Anand, K; Ge, R, Customizing ML predictions for online algorithms, 37th International Conference on Machine Learning, Icml 2020, vol. PartF168147-1 (January, 2020), pp. 280-290, ISBN 9781713821120  [abs]

Getz, Jayce R.

  1. Getz, JR; Liu, B, A refined Poisson summation formula for certain Braverman-Kazhdan spaces, Science China Mathematics, vol. 64 no. 6 (June, 2021), pp. 1127-1156 [doi]  [abs]
  2. Getz, JR, A summation formula for the Rankin-Selberg monoid and a nonabelian trace formula, American Journal of Mathematics, vol. 142 no. 5 (October, 2020), pp. 1371-1407 [doi]  [abs]

Goldberg, Amy

  1. Kim, J; Edge, MD; Goldberg, A; Rosenberg, NA, Skin deep: The decoupling of genetic admixture levels from phenotypes that differed between source populations., American Journal of Physical Anthropology, vol. 175 no. 2 (June, 2021), pp. 406-421 [doi]  [abs]
  2. Korunes, KL; Goldberg, A, Human genetic admixture., Plos Genetics, vol. 17 no. 3 (March, 2021), pp. e1009374 [doi]  [abs]
  3. Hamid, I; Korunes, KL; Beleza, S; Goldberg, A, Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde., Elife, vol. 10 (January, 2021) [doi]  [abs]
  4. Agusto, F; Goldberg, A; Ortega, O; Ponce, J; Zaytseva, S; Sindi, S; Blower, S, How Do Interventions Impact Malaria Dynamics Between Neighboring Countries? A Case Study with Botswana and Zimbabwe, in Association for Women in Mathematics Series, vol. 22 (January, 2021), pp. 83-109 [doi]  [abs]
  5. Hamid, I; Korunes, K; Beleza, S; Goldberg, A, Rapid adaptation to malaria facilitated by admixture in the human population of Cabo Verde (September, 2020) [doi]  [abs]
  6. Goldberg, A; Rastogi, A; Rosenberg, NA, Assortative mating by population of origin in a mechanistic model of admixture., Theoretical Population Biology, vol. 134 (August, 2020), pp. 129-146 [doi]  [abs]
  7. Kemp, ME; Mychajliw, AM; Wadman, J; Goldberg, A, 7000 years of turnover: historical contingency and human niche construction shape the Caribbean's Anthropocene biota., Proceedings of the Royal Society B: Biological Sciences, vol. 287 no. 1927 (May, 2020), pp. 20200447 [doi]  [abs]

Hahn, Heekyoung

  1. Hahn, H, Poles of triple product $L$-functions involving monomial represenations, International Journal of Number Theory, vol. 17 no. 2 (March, 2021), pp. 479-486, World Scientific Publishing [doi]  [abs]

Hain, Richard   (search)

  1. Hain, R, Hodge theory of the Goldman bracket, Geometry & Topology, vol. 24 no. 4 (November, 2020), pp. 1841-1906, Mathematical Sciences Publishers [doi]
  2. Hain, R; Matsumoto, M, Universal Mixed Elliptic Motives, Journal of the Institute of Mathematics of Jussieu, vol. 19 no. 3 (May, 2020), pp. 663-766 [arxiv:1512.03975], [doi]  [abs]
  3. Hain, R, Notes on the universal elliptic KZB connection, Pure and Applied Mathematics Quarterly, vol. 16 no. 2 (January, 2020), pp. 229-312 [doi]  [abs]

Harer, John

  1. Denny, TN; Andrews, L; Bonsignori, M; Cavanaugh, K; Datto, MB; Deckard, A; DeMarco, CT; DeNaeyer, N; Epling, CA; Gurley, T; Haase, SB; Hallberg, C; Harer, J; Kneifel, CL; Lee, MJ; Louzao, R; Moody, MA; Moore, Z; Polage, CR; Puglin, J; Spotts, PH; Vaughn, JA; Wolfe, CR, Implementation of a Pooled Surveillance Testing Program for Asymptomatic SARS-CoV-2 Infections on a College Campus - Duke University, Durham, North Carolina, August 2-October 11, 2020., Mmwr. Morbidity and Mortality Weekly Report, vol. 69 no. 46 (November, 2020), pp. 1743-1747 [doi]  [abs]
  2. Smith, LM; Motta, FC; Chopra, G; Moch, JK; Nerem, RR; Cummins, B; Roche, KE; Kelliher, CM; Leman, AR; Harer, J; Gedeon, T; Waters, NC; Haase, SB, An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum., Science (New York, N.Y.), vol. 368 no. 6492 (May, 2020), pp. 754-759 [doi]  [abs]

Haskins, Mark

  1. Foscolo, L; Haskins, M; Nordström, J, Infinitely many new families of complete cohomogeneity one G2-manifolds: G2analogues of the Taub-NUT and Eguchi-Hanson spaces, Journal of the European Mathematical Society, vol. 23 no. 7 (January, 2021), pp. 2153-2220 [doi]  [abs]

He, Siming

  1. He, S; Kiselev, A, Boundary layer models of the Hou-Luo scenario, Journal of Differential Equations, vol. 298 (October, 2021), pp. 182-204 [doi]  [abs]
  2. He, S; Tadmor, E, A game of alignment: Collective behavior of multi-species, Annales De L'Institut Henri Poincare (C) Non Linear Analysis, vol. 38 no. 4 (July, 2021), pp. 1031-1053 [doi]  [abs]
  3. He, S; Kiselev, A, Small-scale creation for solutions of the sqg equation, Duke Mathematical Journal, vol. 170 no. 5 (January, 2021), pp. 1027-1041, Duke University Press [doi]  [abs]
  4. Gong, Y; He, S, On the 8π-critical-mass threshold of a Patlak-Keller-Segel-Navier-Stokes system, Siam Journal on Mathematical Analysis, vol. 53 no. 3 (January, 2021), pp. 2925-2956 [doi]  [abs]
  5. Bedrossian, J; He, S, Inviscid Damping and Enhanced Dissipation of the Boundary Layer for 2D Navier–Stokes Linearized Around Couette Flow in a Channel, Communications in Mathematical Physics, vol. 379 no. 1 (October, 2020), pp. 177-226 [doi]  [abs]

Hebbar, Pratima

  1. Fernando, K; Hebbar, P, Higher order asymptotics for large deviations-Part II, Stochastics and Dynamics, vol. 21 no. 5 (August, 2021) [doi]  [abs]
  2. Fernando, K; Hebbar, P, Higher order asymptotics for large deviations - Part i, Asymptotic Analysis, vol. 121 no. 3-4 (January, 2021), pp. 219-257, IOS Press [doi]  [abs]
  3. Hebbar, P, Differential Equations For Scientists and Engineers, Physics Today, vol. 73 no. 7 (July, 2020), pp. 54-55, AIP Publishing [doi]
  4. Hebbar, P; Koralov, L; Nolen, J, Asymptotic behavior of branching diffusion processes in periodic media, Electronic Journal of Probability, vol. 25 (January, 2020), pp. 1-40 [doi]  [abs]

Herschlag, Gregory J.

  1. Herschlag, G; Lee, S; Vetter, JS; Randles, A, Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm, Ieee Transactions on Parallel and Distributed Systems, vol. 32 no. 10 (October, 2021), pp. 2400-2414 [doi]  [abs]
  2. Herschlag, G; Kang, HS; Luo, J; Graves, CV; Bangia, S; Ravier, R; Mattingly, JC, Quantifying Gerrymandering in North Carolina, Statistics and Public Policy, vol. 7 no. 1 (January, 2020), pp. 30-38, Informa UK Limited [doi]  [abs]
  3. Carter, D; Hunter, Z; Teague, D; Herschlag, G; Mattingly, J, Optimal Legislative County Clustering in North Carolina, Statistics and Public Policy, vol. 7 no. 1 (January, 2020), pp. 19-29 [doi]  [abs]

Kazaras, Demetre P

  1. Basilio, J.; Kazaras, D.; Sormani, C., An intrinsic flat limit of Riemannian manifolds with no geodesics, Geom. Dedicata, vol. 204 (2020), pp. 265-284  [abs]
  2. D. Kazaras, D. Ruberman and N. Saveliev, On positive scalar curvature cobordisms and the conformal Laplacian on end-periodic manifolds, Communications in Analysis and Geometry, vol. to appear, accepted 2019 (2020)  [abs]
  3. D.Kazaras, C. Sormani and students David Afrifa, Victoria Antonetti, Moshe Dinowitz, Hindy Drillick, Maziar Farahzad, Shanell George, Aleah Lydeatte Hepburn, Leslie Trang Huynh, Emilio Minichiello, Julinda Mujo Pillati, Srivishnupreeth Rendla, Ajmain Yamin, Smocked metric spaces and their tangent cones (2020)  [abs]
  4. Sven Hirsch, Demetre Kazaras, Marcus Khuri, Spacetime Harmonic Functions and the Mass of 3-Dimensional Asymptotically Flat Initial Data for the Einstein Equations, Journal of Differential Geometry (2020)  [abs]
  5. Demetre Kazaras, Desingularizing positive scalar curvature 4-manifolds (2020)  [abs]
  6. Hubert L. Bray, Demetre P. Kazaras, Marcus A. Khuri, Daniel L. Stern, Harmonic Functions and The Mass of 3-Dimensional Asymptotically Flat Riemannian Manifolds (2020)  [abs]

Kim, Woojin

  1. Kim, W; Mémoli, F, Spatiotemporal Persistent Homology for Dynamic Metric Spaces, Discrete & Computational Geometry, vol. 66 no. 3 (October, 2021), pp. 831-875 [doi]  [abs]
  2. Cai, C; Kim, W; Memoli, F; Wang, Y, Elder-rule-staircodes for augmented metric spaces, Siam Journal on Applied Algebra and Geometry, vol. 5 no. 3 (January, 2021), pp. 417-454, ISBN 9783959771436 [doi]  [abs]
  3. Kim, W; Mémoli, F; Smith, Z, Analysis of Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence, Abel Symposia, vol. 15 (January, 2020), pp. 371-389, ISBN 9783030434076 [doi]  [abs]

Kiselev, Alexander A.

  1. He, S; Kiselev, A, Boundary layer models of the Hou-Luo scenario, Journal of Differential Equations, vol. 298 (October, 2021), pp. 182-204 [doi]  [abs]
  2. He, S; Kiselev, A, Small-scale creation for solutions of the sqg equation, Duke Mathematical Journal, vol. 170 no. 5 (January, 2021), pp. 1027-1041, Duke University Press [doi]  [abs]
  3. Kiselev, AA, Small Scale Creation in Active Scalars, Lecture Notes in Mathematics, vol. 2272 (2020), pp. 123-159, ISBN 978-3-030-54898-8 [doi]

Layton, Anita T.

  1. Ahmed, S; Layton, AT, Sex-specific computational models for blood pressure regulation in the rat., American Journal of Physiology. Renal Physiology, vol. 318 no. 4 (April, 2020), pp. F888-F900 [doi]  [abs]
  2. Edwards, A; Palm, F; Layton, AT, A model of mitochondrial O2 consumption and ATP generation in rat proximal tubule cells., American Journal of Physiology. Renal Physiology, vol. 318 no. 1 (January, 2020), pp. F248-F259 [doi]  [abs]

Lee, Holden

  1. Ge, R; Lee, H; Lu, J, Estimating normalizing constants for log-concave distributions: Algorithms and lower bounds, Proceedings of the Annual Acm Symposium on Theory of Computing (June, 2020), pp. 579-586 [doi]  [abs]

Levine, Adam S.

  1. Baldwin, JA; Dowlin, N; Levine, AS; Lidman, T; Sazdanovic, R, Khovanov homology detects the figure-eight knot, Bulletin of the London Mathematical Society, vol. 53 no. 3 (June, 2021), pp. 871-876 [doi]  [abs]
  2. Celoria, D; Golla, M; Levine, AS, Heegaard floer homology and concordance bounds on the Thurston norm, Transactions of the American Mathematical Society, vol. 373 no. 1 (January, 2020), pp. 295-318 [doi]  [abs]

Li, Bowen

  1. Ammari, H; Li, B; Zou, J, Mathematical Analysis of Electromagnetic Plasmonic Metasurfaces, Multiscale Modeling & Simulation, vol. 18 no. 2 (January, 2020), pp. 758-797, Society for Industrial & Applied Mathematics (SIAM) [doi]
  2. Ammari, H; Li, B; Zou, J, Superresolution in Recovering Embedded Electromagnetic Sources in High Contrast Media, Siam Journal on Imaging Sciences, vol. 13 no. 3 (January, 2020), pp. 1467-1510, Society for Industrial & Applied Mathematics (SIAM) [doi]

Li, Yingzhou

  1. Li, Y; Cheng, X; Lu, J, Butterfly-net: Optimal function representation based on convolutional neural networks, Communications in Computational Physics, vol. 28 no. 5 (November, 2020), pp. 1838-1885, Global Science Press [doi]  [abs]
  2. Yu, VWZ; Campos, C; Dawson, W; García, A; Havu, V; Hourahine, B; Huhn, WP; Jacquelin, M; Jia, W; Keçeli, M; Laasner, R; Li, Y; Lin, L; Lu, J; Moussa, J; Roman, JE; Vázquez-Mayagoitia, Á; Yang, C; Blum, V, ELSI — An open infrastructure for electronic structure solvers, Computer Physics Communications, vol. 256 (November, 2020), pp. 107459-107459, Elsevier BV [doi]  [abs]
  3. Gu, H; Shi, G; Chen, HC; Xie, S; Li, Y; Tong, H; Yang, C; Zhu, C; Mefford, JT; Xia, H; Chueh, WC; Chen, HM; Zhang, L, Strong Catalyst-Support Interactions in Electrochemical Oxygen Evolution on Ni-Fe Layered Double Hydroxide, Acs Energy Letters, vol. 5 no. 10 (October, 2020), pp. 3185-3194 [doi]  [abs]
  4. Li, Y; Lu, J, Optimal Orbital Selection for Full Configuration Interaction (OptOrbFCI): Pursuing the Basis Set Limit under a Budget., Journal of Chemical Theory and Computation, vol. 16 no. 10 (October, 2020), pp. 6207-6221 [doi]  [abs]
  5. Oliveira, MJT; Papior, N; Pouillon, Y; Blum, V; Artacho, E; Caliste, D; Corsetti, F; de Gironcoli, S; Elena, AM; García, A; García-Suárez, VM; Genovese, L; Huhn, WP; Huhs, G; Kokott, S; Küçükbenli, E; Larsen, AH; Lazzaro, A; Lebedeva, IV; Li, Y; López-Durán, D; López-Tarifa, P; Lüders, M; Marques, MAL; Minar, J; Mohr, S; Mostofi, AA; O'Cais, A; Payne, MC; Ruh, T; Smith, DGA; Soler, JM; Strubbe, DA; Tancogne-Dejean, N; Tildesley, D; Torrent, M; Yu, VW-Z, The CECAM electronic structure library and the modular software development paradigm., The Journal of Chemical Physics, vol. 153 no. 2 (July, 2020), pp. 024117, AIP Publishing [doi]  [abs]
  6. Li, Y; Lu, J; Mao, A, Variational training of neural network approximations of solution maps for physical models, Journal of Computational Physics, vol. 409 (May, 2020) [doi]  [abs]
  7. Hu, W; Liu, J; Li, Y; Ding, Z; Yang, C; Yang, J, Accelerating Excitation Energy Computation in Molecules and Solids within Linear-Response Time-Dependent Density Functional Theory via Interpolative Separable Density Fitting Decomposition., Journal of Chemical Theory and Computation, vol. 16 no. 2 (February, 2020), pp. 964-973 [doi]  [abs]
  8. Li, L; Li, Y; Liu, JG; Liu, Z; Lu, J, A stochastic version of stein variational gradient descent for efficient sampling, Communications in Applied Mathematics and Computational Science, vol. 15 no. 1 (January, 2020), pp. 37-63, Mathematical Sciences Publishers [doi]  [abs]
  9. CHEN, Z; LI, Y; LU, J, Tensor ring decomposition: Optimization landscape and one-loop convergence of alternating least squares, Siam Journal on Matrix Analysis and Applications, vol. 41 no. 3 (January, 2020), pp. 1416-1442, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  10. Zhu, C; Zhang, Z; Zhong, L; Hsu, CS; Xu, X; Li, Y; Zhao, S; Chen, S; Yu, J; Wu, M; Gao, P; Li, S; Chen, HM; Liu, K; Zhang, L, Product-Specific Active Site Motifs of Cu for Electrochemical CO2 Reduction, Chem (January, 2020) [doi]  [abs]

Liu, Jian-Guo

  1. Lafata, KJ; Chang, Y; Wang, C; Mowery, YM; Vergalasova, I; Niedzwiecki, D; Yoo, DS; Liu, J-G; Brizel, DM; Yin, F-F, Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers., Med Phys, vol. 48 no. 7 (July, 2021), pp. 3767-3777 [doi]  [abs]
  2. Hu, J; Liu, JG; Xie, Y; Zhou, Z, A structure preserving numerical scheme for Fokker-Planck equations of neuron networks: Numerical analysis and exploration, Journal of Computational Physics, vol. 433 (May, 2021) [doi]  [abs]
  3. Liu, JG; Wang, J; Zhao, Y; Zhou, Z, Field model for complex ionic fluids: Analytical properties and numerical investigation, Communications in Computational Physics, vol. 30 no. 3 (January, 2021), pp. 874-902 [doi]  [abs]
  4. Liu, JG; Xu, X, Existence and incompressible limit of a tissue growth model with autophagy, Siam Journal on Mathematical Analysis, vol. 53 no. 5 (January, 2021), pp. 5215-5242 [doi]  [abs]
  5. Li, Q; Liu, JG; Shu, R, Sensitivity analysis of burgers' equation with shocks, Siam/Asa Journal on Uncertainty Quantification, vol. 8 no. 4 (January, 2021), pp. 1493-1521, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs] [reputed journal]
  6. Gao, Y; Liu, JG, Gradient flow formulation and second order numerical method for motion by mean curvature and contact line dynamics on rough surface, Interfaces and Free Boundaries, vol. 23 no. 1 (January, 2021), pp. 103-158 [doi]  [abs]
  7. Gao, Y; Jin, G; Liu, J-G, Inbetweening auto-animation via Fokker-Planck dynamics and thresholding, Inverse Problems & Imaging, vol. 15 no. 5 (2021), pp. 843-843, American Institute of Mathematical Sciences (AIMS) [doi]  [abs]
  8. Huang, H; Liu, JG; Pickl, P, On the Mean-Field Limit for the Vlasov–Poisson–Fokker–Planck System, Journal of Statistical Physics, vol. 181 no. 5 (December, 2020), pp. 1915-1965 [doi]  [abs] [author's comments] [high impact paper]
  9. Gao, Y; Liu, JG; Lu, J; Marzuola, JL, Analysis of a continuum theory for broken bond crystal surface models with evaporation and deposition effects, Nonlinearity, vol. 33 no. 8 (August, 2020), pp. 3816-3845 [doi]  [abs] [reputed journal]
  10. Jin, S; Li, L; Liu, JG, Convergence of the random batch method for interacting particles with disparate species and weights, Siam Journal on Numerical Analysis, vol. 59 no. 2 (March, 2020), pp. 746-768 [doi]  [abs]
  11. Jin, S; Li, L; Liu, JG, Random Batch Methods (RBM) for interacting particle systems, Journal of Computational Physics, vol. 400 (January, 2020) [doi]  [abs] [author's comments] [high impact paper]
  12. Feng, Y; Gao, T; Li, L; Liu, JG; Lu, Y, Uniform-in-time weak error analysis for stochastic gradient descent algorithms via diffusion approximation, Communications in Mathematical Sciences, vol. 18 no. 1 (January, 2020), pp. 163-188 [doi]  [abs] [reputed journal]
  13. Degond, P; Engel, M; Liu, JG; Pego, RL, A markov jump process modelling animal group size statistics, Communications in Mathematical Sciences, vol. 18 no. 1 (January, 2020), pp. 55-89 [doi]  [abs] [reputed journal]
  14. Li, L; Li, Y; Liu, JG; Liu, Z; Lu, J, A stochastic version of stein variational gradient descent for efficient sampling, Communications in Applied Mathematics and Computational Science, vol. 15 no. 1 (January, 2020), pp. 37-63, Mathematical Sciences Publishers [doi]  [abs] [reputed journal]
  15. Li, L; Liu, JG, Large time behaviors of upwind schemes and B-schemes for fokker-planck equations on R by jump processes, Mathematics of Computation, vol. 89 no. 325 (January, 2020), pp. 2283-2320, American Mathematical Society (AMS) [doi]  [abs] [reputed journal]
  16. Gao, Y; Liu, JG; Luo, T; Xiang, Y, Revisit of the peierls-nabarro model for edge dislocations in Hilbert space, Discrete and Continuous Dynamical Systems Series B, vol. 22 no. 11 (January, 2020) [doi]  [abs] [reputed journal]
  17. LIU, JG; WANG, J, GLOBAL EXISTENCE FOR NERNST-PLANCK-NAVIER-STOKES SYSTEM IN RN, Communications in Mathematical Sciences, vol. 18 no. 6 (January, 2020), pp. 1743-1754 [doi]  [abs] [reputed journal]
  18. LIU, JIANGUO; XU, X, A CLASS OF FUNCTIONAL INEQUALITIES AND THEIR APPLICATIONS TO FOURTH-ORDER NONLINEAR PARABOLIC EQUATIONS, Communications in Mathematical Sciences, vol. 18 no. 7 (January, 2020), pp. 1911-1948, International Press of Boston [doi]  [abs] [reputed journal]
  19. Gao, Y; Liu, J-G, Long time behavior of dynamic solution to Peierls–Nabarro dislocation model, Methods and Applications of Analysis, vol. 27 no. 2 (2020), pp. 161-198, International Press of Boston [doi] [reputed journal]
  20. Gao, Y; Liu, J-G, A note on parametric Bayesian inference via gradient flows, Annals of Mathematical Sciences and Applications, vol. 5 no. 2 (2020), pp. 261-282, International Press of Boston [doi] [reputed journal]

Lu, Jianfeng

  1. Cheng, C; Daubechies, I; Dym, N; Lu, J, Stable phase retrieval from locally stable and conditionally connected measurements, Applied and Computational Harmonic Analysis, vol. 55 (November, 2021), pp. 440-465 [doi]  [abs]
  2. Li, L; Goodrich, C; Yang, H; Phillips, KR; Jia, Z; Chen, H; Wang, L; Zhong, J; Liu, A; Lu, J; Shuai, J; Brenner, MP; Spaepen, F; Aizenberg, J, Microscopic origins of the crystallographically preferred growth in evaporation-induced colloidal crystals., Proceedings of the National Academy of Sciences of the United States of America, vol. 118 no. 32 (August, 2021) [doi]  [abs]
  3. An, D; Cheng, SY; Head-Gordon, T; Lin, L; Lu, J, Convergence of stochastic-extended Lagrangian molecular dynamics method for polarizable force field simulation, Journal of Computational Physics, vol. 438 (August, 2021) [doi]  [abs]
  4. Khoo, Y; Lu, J; Ying, L, Solving parametric PDE problems with artificial neural networks, European Journal of Applied Mathematics, vol. 32 no. 3 (June, 2021), pp. 421-435 [doi]  [abs]
  5. Yang, S; Cai, Z; Lu, J, Inclusion-exclusion principle for open quantum systems with bosonic bath, New Journal of Physics, vol. 23 no. 6 (June, 2021) [doi]  [abs]
  6. Lu, J; Steinerberger, S, Optimal Trapping for Brownian Motion: a Nonlinear Analogue of the Torsion Function, Potential Analysis, vol. 54 no. 4 (April, 2021), pp. 687-698 [doi]  [abs]
  7. Coffman, AJ; Lu, J; Subotnik, JE, A grid-free approach for simulating sweep and cyclic voltammetry., The Journal of Chemical Physics, vol. 154 no. 16 (April, 2021), pp. 161101 [doi]  [abs]
  8. Thicke, K; Watson, AB; Lu, J, Computing edge states without hard truncation, Siam Journal on Scientific Computing, vol. 43 no. 2 (March, 2021), pp. B323-B353 [doi]  [abs]
  9. Stubbs, KD; Watson, AB; Lu, J, Iterated projected position algorithm for constructing exponentially localized generalized Wannier functions for periodic and nonperiodic insulators in two dimensions and higher, Physical Review B, vol. 103 no. 7 (February, 2021) [doi]  [abs]
  10. Khoo, Y; Lu, J; Ying, L, Efficient construction of tensor ring representations from sampling, Multiscale Modeling & Simulation, vol. 19 no. 3 (January, 2021) [doi]  [abs]
  11. Chen, K; Li, Q; Lu, J; Wright, SJ, A low-rank schwarz method for radiative transfer equation with heterogeneous scattering coefficient, Multiscale Modeling & Simulation, vol. 19 no. 2 (January, 2021), pp. 775-801 [doi]  [abs]
  12. Lu, J; Otto, F, Optimal Artificial Boundary Condition for Random Elliptic Media, Foundations of Computational Mathematics (January, 2021) [doi]  [abs]
  13. Li, L; Lu, J; Mattingly, JC; Wang, L, Numerical methods for stochastic differential equations based on Gaussian mixture, Communications in Mathematical Sciences, vol. 19 no. 6 (2021), pp. 1549-1577, International Press of Boston [doi]
  14. Cao, Y; Lu, J; Wang, L, Complexity of randomized algorithms for underdamped Langevin dynamics, Communications in Mathematical Sciences, vol. 19 no. 7 (2021), pp. 1827-1853, International Press of Boston [doi]
  15. Ding, Z; Li, Q; Lu, J; Wright, SJ, Random Coordinate Underdamped Langevin Monte Carlo, 24th International Conference on Artificial Intelligence and Statistics (Aistats), vol. 130 (2021)
  16. Han, J; Lu, J; Zhou, M, Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach, Journal of Computational Physics, vol. 423 (December, 2020) [doi]  [abs]
  17. Lu, J; Lu, Y; Zhou, Z, Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics, Journal of Computational Physics, vol. 423 (December, 2020) [doi]  [abs]
  18. Sen, D; Sachs, M; Lu, J; Dunson, DB, Efficient posterior sampling for high-dimensional imbalanced logistic regression., Biometrika, vol. 107 no. 4 (December, 2020), pp. 1005-1012, Oxford University Press (OUP) [doi]  [abs]
  19. Cai, Z; Lu, J; Yang, S, Inchworm Monte Carlo Method for Open Quantum Systems, Communications on Pure and Applied Mathematics, vol. 73 no. 11 (November, 2020), pp. 2430-2472 [doi]  [abs]
  20. Yu, VWZ; Campos, C; Dawson, W; García, A; Havu, V; Hourahine, B; Huhn, WP; Jacquelin, M; Jia, W; Keçeli, M; Laasner, R; Li, Y; Lin, L; Lu, J; Moussa, J; Roman, JE; Vázquez-Mayagoitia, Á; Yang, C; Blum, V, ELSI — An open infrastructure for electronic structure solvers, Computer Physics Communications, vol. 256 (November, 2020), pp. 107459-107459, Elsevier BV [doi]  [abs]
  21. Lu, J; Steinerberger, S, Synchronization of Kuramoto oscillators in dense networks, Nonlinearity, vol. 33 no. 11 (November, 2020), pp. 5905-5918 [doi]  [abs]
  22. Li, Y; Cheng, X; Lu, J, Butterfly-net: Optimal function representation based on convolutional neural networks, Communications in Computational Physics, vol. 28 no. 5 (November, 2020), pp. 1838-1885, Global Science Press [doi]  [abs]
  23. Li, Y; Lu, J, Optimal Orbital Selection for Full Configuration Interaction (OptOrbFCI): Pursuing the Basis Set Limit under a Budget., Journal of Chemical Theory and Computation, vol. 16 no. 10 (October, 2020), pp. 6207-6221 [doi]  [abs]
  24. Li, W; Lu, J; Wang, L, Fisher information regularization schemes for Wasserstein gradient flows, Journal of Computational Physics, vol. 416 (September, 2020) [doi]  [abs]
  25. Gao, Y; Liu, JG; Lu, J; Marzuola, JL, Analysis of a continuum theory for broken bond crystal surface models with evaporation and deposition effects, Nonlinearity, vol. 33 no. 8 (August, 2020), pp. 3816-3845 [doi]  [abs]
  26. Ge, R; Lee, H; Lu, J, Estimating normalizing constants for log-concave distributions: Algorithms and lower bounds, Proceedings of the Annual Acm Symposium on Theory of Computing (June, 2020), pp. 579-586 [doi]  [abs]
  27. Nishimura, A; Dunson, DB; Lu, J, Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods, Biometrika, vol. 107 no. 2 (June, 2020), pp. 365-380 [doi]  [abs]
  28. Li, Y; Lu, J; Mao, A, Variational training of neural network approximations of solution maps for physical models, Journal of Computational Physics, vol. 409 (May, 2020), pp. 109338-109338, Elsevier BV [doi]  [abs]
  29. Lu, J; Sachs, M; Steinerberger, S, Quadrature Points via Heat Kernel Repulsion, Constructive Approximation, vol. 51 no. 1 (February, 2020), pp. 27-48 [doi]  [abs]
  30. Lu, J; Steinerberger, S, A dimension-free hermite-hadamard inequality via gradient estimates for the torsion function, Proceedings of the American Mathematical Society, vol. 148 no. 2 (January, 2020), pp. 673-679 [doi]  [abs]
  31. Chen, K; Li, Q; Lu, J; Wright, SJ, Randomized sampling for basis function construction in generalized finite element methods, Multiscale Modeling & Simulation, vol. 18 no. 2 (January, 2020), pp. 1153-1177, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  32. Lu, J; Wang, Z, The full configuration interaction quantum monte carlo method through the lens of inexact power iteration, Siam Journal on Scientific Computing, vol. 42 no. 1 (January, 2020), pp. B1-B29 [doi]  [abs]
  33. Li, L; Li, Y; Liu, JG; Liu, Z; Lu, J, A stochastic version of stein variational gradient descent for efficient sampling, Communications in Applied Mathematics and Computational Science, vol. 15 no. 1 (January, 2020), pp. 37-63, Mathematical Sciences Publishers [doi]  [abs]
  34. Lu, J; Watson, AB; Weinstein, MI, Dirac operators and domain walls, Siam Journal on Mathematical Analysis, vol. 52 no. 2 (January, 2020), pp. 1115-1145 [doi]  [abs]
  35. CHEN, Z; LI, Y; LU, J, Tensor ring decomposition: Optimization landscape and one-loop convergence of alternating least squares, Siam Journal on Matrix Analysis and Applications, vol. 41 no. 3 (January, 2020), pp. 1416-1442, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  36. Chen, K; Li, Q; Lu, J; Wright, SJ, Random sampling and efficient algorithms for multiscale pdes, Siam Journal on Scientific Computing, vol. 42 no. 5 (January, 2020), pp. A2974-A3005 [doi]  [abs]
  37. An, J; Lu, J; Ying, L, Stochastic modified equations for the asynchronous stochastic gradient descent, Information and Inference, vol. 9 no. 4 (January, 2020), pp. 851-873 [doi]  [abs]
  38. Lu, Y; Ma, C; Lu, J; Ying, L, A mean-field analysis of deep resnet and beyond: Towards provable optimization via overparameterization from depth, 37th International Conference on Machine Learning, Icml 2020, vol. PartF168147-9 (January, 2020), pp. 6382-6392  [abs]
  39. Lu, Y; Lu, J, A universal approximation theorem of deep neural networks for expressing probability distributions, Advances in Neural Information Processing Systems, vol. 2020-December (January, 2020)  [abs]
  40. Agazzi, A; Lu, J, Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime., Corr, vol. abs/2010.11858 (2020), OpenReview.net

Luo, Xiaoyutao

  1. Cheskidov, A; Luo, X, Nonuniqueness of Weak Solutions for the Transport Equation at Critical Space Regularity, Annals of Pde, vol. 7 no. 1 (June, 2021), Springer Science and Business Media LLC [doi]  [abs]
  2. Cheskidov, A; Luo, X, Energy equality for the Navier–Stokes equations in weak-in-time Onsager spaces, Nonlinearity, vol. 33 no. 4 (April, 2020), pp. 1388-1403, IOP Publishing [doi]

Maggioni, Mauro

  1. E Causevic and R~R Coifman and R Isenhart and A Jacquin and E~R John and M Maggioni and L~S Prichep and F~J Warner, QEEG-based classification with wavelet packets and microstate features for triage applications in the ER (2005)
  2. GL Davis and Mauro Maggioni and FJ Warner and FB Geshwind and AC Coppi and RA DeVerse and RR Coifman, Hyper-spectral Analysis of normal and malignant colon tissue microarray sections using a novel DMD system (2004) (Poster, Optical Imaging NIH workshop, to app. in proc..)
  3. Ronald R Coifman and Mauro Maggioni, Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms no. YALE/DCS/TR-1289 (2004)

Mattingly, Jonathan C.   (search)

  1. Gao, Y; Kirkpatrick, K; Marzuola, J; Mattingly, J; Newhall, KA, LIMITING BEHAVIORS OF HIGH DIMENSIONAL STOCHASTIC SPIN ENSEMBLES*, Communications in Mathematical Sciences, vol. 19 no. 2 (January, 2021), pp. 453-494 [doi]  [abs]
  2. Li, L; Lu, J; Mattingly, JC; Wang, L, Numerical methods for stochastic differential equations based on Gaussian mixture, Communications in Mathematical Sciences, vol. 19 no. 6 (2021), pp. 1549-1577, International Press of Boston [doi]
  3. Gao, Y; Marzuola, JL; Mattingly, JC; Newhall, KA, Nonlocal stochastic-partial-differential-equation limits of spatially correlated noise-driven spin systems derived to sample a canonical distribution., Physical Review. E, vol. 102 no. 5-1 (November, 2020), pp. 052112 [doi]  [abs]
  4. Lu, Y; Mattingly, JC, Geometric ergodicity of Langevin dynamics with Coulomb interactions, Nonlinearity, vol. 33 no. 2 (January, 2020), pp. 675-699, IOP Publishing [doi]  [abs]
  5. Carter, D; Hunter, Z; Teague, D; Herschlag, G; Mattingly, J, Optimal Legislative County Clustering in North Carolina, Statistics and Public Policy, vol. 7 no. 1 (January, 2020), pp. 19-29 [doi]  [abs]
  6. Herschlag, G; Kang, HS; Luo, J; Graves, CV; Bangia, S; Ravier, R; Mattingly, JC, Quantifying Gerrymandering in North Carolina, Statistics and Public Policy, vol. 7 no. 1 (January, 2020), pp. 30-38, Informa UK Limited [doi]  [abs]
  7. Chikina, M; Frieze, A; Mattingly, JC; Pegden, W, Separating Effect From Significance in Markov Chain Tests, Statistics and Public Policy, vol. 7 no. 1 (January, 2020), pp. 101-114 [doi]  [abs]
  8. AGAZZI, A; MATTINGLY, JC, SEEMINGLY STABLE CHEMICAL KINETICS CAN BE STABLE, MARGINALLY STABLE, OR UNSTABLE, Communications in Mathematical Sciences, vol. 18 no. 6 (January, 2020), pp. 1605-1642, International Press of Boston [doi]  [abs]

McPhail-Snyder, Calvin

  1. McPhail-Snyder, C; Miller, KA, Planar diagrams for local invariants of graphs in surfaces, Journal of Knot Theory and Its Ramifications, vol. 29 no. 01 (January, 2020), pp. 1950093-1950093, World Scientific Pub Co Pte Lt [doi]  [abs]

Mukherjee, Sayan

  1. Berchuck, S; Jammal, A; Mukherjee, S; Somers, T; Medeiros, FA, Impact of anxiety and depression on progression to glaucoma among glaucoma suspects., The British Journal of Ophthalmology, vol. 105 no. 9 (September, 2021), pp. 1244-1249 [doi]  [abs]
  2. Silverman, JD; Bloom, RJ; Jiang, S; Durand, HK; Dallow, E; Mukherjee, S; David, LA, Measuring and mitigating PCR bias in microbiota datasets., Plos Computational Biology, vol. 17 no. 7 (July, 2021), pp. e1009113 [doi]  [abs]
  3. Wang, B; Sudijono, T; Kirveslahti, H; Gao, T; Boyer, DM; Mukherjee, S; Crawford, L, A statistical pipeline for identifying physical features that differentiate classes of 3D shapes, The Annals of Applied Statistics, vol. 15 no. 2 (June, 2021), pp. 638-661 [doi]  [abs]
  4. Zhang, X; Bashizade, R; Wang, Y; Mukherjee, S; Lebeck, AR, Statistical robustness of Markov chain Monte Carlo accelerators, International Conference on Architectural Support for Programming Languages and Operating Systems Asplos (April, 2021), pp. 959-974, ISBN 9781450383172 [doi]  [abs]
  5. Johnston, RA; Vullioud, P; Thorley, J; Kirveslahti, H; Shen, L; Mukherjee, S; Karner, CM; Clutton-Brock, T; Tung, J, Morphological and genomic shifts in mole-rat 'queens' increase fecundity but reduce skeletal integrity., Elife, vol. 10 (April, 2021) [doi]  [abs]
  6. Bryan, J; Mandan, A; Kamat, G; Gottschalk, WK; Badea, A; Adams, KJ; Thompson, JW; Colton, CA; Mukherjee, S; Lutz, MW; Alzheimer's Disease Neuroimaging Initiative,, Likelihood ratio statistics for gene set enrichment in Alzheimer's disease pathways., Alzheimers Dement, vol. 17 no. 4 (April, 2021), pp. 561-573 [doi]  [abs]
  7. Li, W; Hannig, J; Mukherjee, S, Subspace clustering through sub-clusters, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  8. Silverman, JD; Roche, K; Mukherjee, S; David, LA, Naught all zeros in sequence count data are the same., Computational and Structural Biotechnology Journal, vol. 18 (2020), pp. 2789-2798 [doi]  [abs]

Nelson, Anna C

  1. Nelson, AC; Kelley, MA; Haynes, LM; Leiderman, K, Mathematical models of fibrin polymerization: Past, present, and future, Current Opinion in Biomedical Engineering (September, 2021), pp. 100350-100350, Elsevier BV [doi]
  2. Nelson, AC; Keener, JP; Fogelson, AL, Kinetic model of two-monomer polymerization., Physical Review. E, vol. 101 no. 2-1 (February, 2020), pp. 022501 [doi]  [abs]

Ng, Lenhard L.

  1. Ng, L; Rutherford, D; Shende, V; Sivek, S; Zaslow, E, Augmentations are Sheaves, Geometry and Topology, vol. 24 no. 5 (December, 2020), pp. 2149-2286, Mathematical Sciences Publishers [doi]  [abs]

Nolen, James H.

  1. Lim, TS; Lu, Y; Nolen, JH, Quantitative propagation of chaos in a bimolecular chemical reaction-diffusion model, Siam Journal on Mathematical Analysis, vol. 52 no. 2 (January, 2020), pp. 2098-2133 [doi]  [abs]
  2. Hebbar, P; Koralov, L; Nolen, J, Asymptotic behavior of branching diffusion processes in periodic media, Electronic Journal of Probability, vol. 25 (January, 2020), pp. 1-40 [doi]  [abs]
  3. COHN, S; IYER, G; NOLEN, J; PEGO, RL, ANOMALOUS DIFFUSION IN COMB-SHAPED DOMAINS AND GRAPHS, Communications in Mathematical Sciences, vol. 18 no. 7 (January, 2020), pp. 1815-1862, International Press of Boston [doi]  [abs]
  4. Berestycki, J; Brunet, E; Nolen, J; Penington, S, A free boundary problem arising from branching Brownian motion with selection, Transactions of the American Mathematical Society (2020), pp. 1-1, American Mathematical Society [doi]
  5. Berestycki, J; Brunet, E; Nolen, J; Penington, S, Brownian bees in the infinite swarm limit (2020)

Orizaga, Saulo

  1. Orizaga, S; Riahi, DN; Soto, JR, Drug delivery in catheterized arterial blood flow with atherosclerosis, Results in Applied Mathematics, vol. 7 (August, 2020), pp. 100117-100117, Elsevier BV [doi]  [abs]

Payne, Alec J

  1. Mramor, A; Payne, A, Ancient and eternal solutions to mean curvature flow from minimal surfaces, Mathematische Annalen, vol. 380 no. 1-2 (June, 2021), pp. 569-591, Springer Science and Business Media LLC [doi]  [abs]

Pfister, Henry

  1. Rengaswamy, N; Seshadreesan, KP; Guha, S; Pfister, HD, Belief propagation with quantum messages for quantum-enhanced classical communications, Npj Quantum Information, vol. 7 no. 1 (December, 2021) [doi]  [abs]
  2. Srinivasavaradhan, SR; Gopi, S; Pfister, HD; Yekhanin, S, Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage, Ieee International Symposium on Information Theory Proceedings, vol. 2021-July (July, 2021), pp. 2453-2458 [doi]  [abs]
  3. Rengaswamy, N; Pfister, HD, On the Duality between the BSC and Quantum PSC, Ieee International Symposium on Information Theory Proceedings, vol. 2021-July (July, 2021), pp. 2232-2237, ISBN 9781538682098 [doi]  [abs]
  4. Pfister, HD; Tal, I, Polar Codes for Channels with Insertions, Deletions, and Substitutions, Ieee International Symposium on Information Theory Proceedings, vol. 2021-July (July, 2021), pp. 2554-2559 [doi]  [abs]
  5. Buchberger, A; Hager, C; Pfister, HD; Schmalen, L; Graell I Amat, A, Pruning and Quantizing Neural Belief Propagation Decoders, Ieee Journal on Selected Areas in Communications, vol. 39 no. 7 (July, 2021), pp. 1957-1966 [doi]  [abs]
  6. Butler, RM; Hager, C; Pfister, HD; Liga, G; Alvarado, A, Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation, Journal of Lightwave Technology, vol. 39 no. 4 (February, 2021), pp. 949-959 [doi]  [abs]
  7. Buchberger, A; Häger, C; Pfister, HD; Schmalen, L; I Amat, AG, Learned decimation for neural belief propagation decoders (invited paper), 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2021-June (January, 2021), pp. 8273-8277 [doi]  [abs]
  8. Hager, C; Pfister, HD, Physics-Based Deep Learning for Fiber-Optic Communication Systems, Ieee Journal on Selected Areas in Communications, vol. 39 no. 1 (January, 2021), pp. 280-294 [doi]  [abs]
  9. Can, T; Rengaswamy, N; Calderbank, R; Pfister, HD, Kerdock Codes Determine Unitary 2-Designs, Ieee Transactions on Information Theory, vol. 66 no. 10 (October, 2020), pp. 6104-6120, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  10. Rengaswamy, N; Calderbank, R; Newman, M; Pfister, HD, On Optimality of CSS Codes for Transversal T, Ieee Journal on Selected Areas in Information Theory, vol. 1 no. 2 (August, 2020), pp. 499-514, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  11. Coskun, MC; Pfister, HD, Bounds on the List Size of Successive Cancellation List Decoding, Spcom 2020 International Conference on Signal Processing and Communications (July, 2020), ISBN 9781728188959 [doi]  [abs]
  12. Oliari, V; Goossens, S; Hager, C; Liga, G; Butler, RM; Hout, MVD; Heide, SVD; Pfister, HD; Okonkwo, C; Alvarado, A, Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration, Journal of Lightwave Technology, vol. 38 no. 12 (June, 2020), pp. 3114-3124, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  13. Rengaswamy, N; Calderbank, R; Newman, M; Pfister, HD, Classical Coding Problem from Transversal T Gates, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 1891-1896 [doi]  [abs]
  14. Brandsen, S; Lian, M; Stubbs, KD; Rengaswamy, N; Pfister, HD, Adaptive Procedures for Discriminating Between Arbitrary Tensor-Product Quantum States, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 1933-1938 [doi]  [abs]
  15. Buchberger, A; Hager, C; Pfister, HD; Schmalen, L; I Amat, AG, Pruning Neural Belief Propagation Decoders, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 338-342 [doi]  [abs]
  16. Coskun, MC; Neu, J; Pfister, HD, Successive Cancellation Inactivation Decoding for Modified Reed-Muller and eBCH Codes, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 437-442 [doi]  [abs]
  17. Rengaswamy, N; Seshadreesan, KP; Guha, S; Pfister, HD, Quantum Advantage via Qubit Belief Propagation, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 1824-1829, ISBN 9781728164328 [doi]  [abs]
  18. Lian, M; Hager, C; Pfister, HD, Decoding Reed-Muller Codes Using Redundant Code Constraints, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 42-47, ISBN 9781728164328 [doi]  [abs]
  19. Brandsen, S; Stubbs, KD; Pfister, HD, Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 1897-1902, ISBN 9781728164328 [doi]  [abs]
  20. Thangaraj, A; Pfister, HD, Efficient Maximum-Likelihood Decoding of Reed-Muller RM(m-3,m) Codes, Ieee International Symposium on Information Theory Proceedings, vol. 2020-June (June, 2020), pp. 263-268, ISBN 9781728164328 [doi]  [abs]
  21. Häger, C; Pfister, HD; Bütler, RM; Liga, G; Alvarado, A, Model-based machine learning for joint digital backpropagation and PMD compensation, Optics Infobase Conference Papers, vol. Part F174-OFC 2020 (January, 2020), ISBN 9781943580712 [doi]  [abs]
  22. Rengaswamy, N; Calderbank, R; Kadhe, S; Pfister, HD, Logical Clifford Synthesis for Stabilizer Codes, Ieee Transactions on Quantum Engineering, vol. 1 (2020), pp. 1-17, Institute of Electrical and Electronics Engineers (IEEE) [doi]

Pierce, Lillian B.

  1. Pierce, LB, On Superorthogonality, The Journal of Geometric Analysis, vol. 31 no. 7 (July, 2021), pp. 7096-7183 [doi]  [abs]
  2. Gressman, PT; Guo, S; Pierce, LB; Roos, J; Yung, PL, Reversing a Philosophy: From Counting to Square Functions and Decoupling, The Journal of Geometric Analysis, vol. 31 no. 7 (July, 2021), pp. 7075-7095 [doi]  [abs]
  3. Pierce, LB; Beckner, W; Dafni, G; Fefferman, C; Ionescu, A; Kearn, V; Kenig, CE; Knapp, AW; Krantz, SG; Lanzani, L; Nagel, A; Phong, DH; Ricci, F; Rothschild, L; Shakarchi, R; Sogge, C; Stein, J; Stein, K; Tao, T; Wainger, S; Widom, H, Elias M. Stein (1931–2018), Notices of the American Mathematical Society, vol. 68 no. 04 (April, 2021), pp. 1-1, American Mathematical Society (AMS) [doi]
  4. Pierce, LB; Turnage-Butterbaugh, CL; Wood, MM, On a conjecture for $\ell$-torsion in class groups of number fields: from the perspective of moments, Mathematical Research Letters, vol. 28 no. 2 (2021), pp. 575-621, International Press  [abs]
  5. An, C; Chu, R; Pierce, LB, Counterexamples for high-degree generalizations of the Schrödinger maximal operator (2021)  [abs]
  6. Pierce, LB, ON BOURGAIN’S COUNTEREXAMPLE for the SCHRÖDINGER MAXIMAL FUNCTION, The Quarterly Journal of Mathematics, vol. 71 no. 4 (December, 2020), pp. 1309-1344, Oxford University Press (OUP) [doi]  [abs]
  7. Fefferman, C; Ionescu, A; Tao, T; Wainger, S, Analysis and applications: The mathematical work of Elias Stein, Bulletin of the American Mathematical Society, vol. 57 no. 4 (March, 2020), pp. 523-594, American Mathematical Society (AMS) [doi]
  8. Alaifari, R; Cheng, X; Pierce, LB; Steinerberger, S, On matrix rearrangement inequalities, Proceedings of the American Mathematical Society, vol. 148 no. 5 (January, 2020), pp. 1835-1848, American Mathematical Society (AMS) [doi]  [abs]
  9. Pierce, LB; Xu, J, Burgess bounds for short character sums evaluated at forms, Algebra & Number Theory, vol. 14 no. 7 (January, 2020), pp. 1911-1951 [doi]  [abs]
  10. Pierce, LB; Turnage-Butterbaugh, CL; Wood, MM, An effective Chebotarev density theorem for families of number fields, with an application to $\ell$-torsion in class groups, Inventiones Mathematicae, vol. 219 no. 2 (2020), pp. 707-778, SPRINGER [doi]  [abs]
  11. Pierce, LB, Burgess bounds for short character sums evaluated at forms II: the mixed case (2020)  [abs]

Pollack, Aaron

  1. Pollack, A, The Fourier expansion of modular forms on quaternionic exceptional groups, Duke Mathematical Journal, vol. 169 no. 7 (May, 2020), pp. 1209-1280, Duke University Press [doi]

Porter, Curtis W.

  1. Porter, C, 3-folds CR-embedded in 5-dimensional real hyperquadrics, Journal of Geometry and Physics, vol. 163 (May, 2021) [doi]  [abs]

Randles, Amanda

  1. Gounley, J; Vardhan, M; Draeger, EW; Valero-Lara, P; Moore, SV; Randles, A, Propagation Pattern for Moment Representation of the Lattice Boltzmann Method, Ieee Transactions on Parallel and Distributed Systems, vol. 33 no. 3 (March, 2022), pp. 642-653 [doi]  [abs]
  2. Bazarin, RLM; Philippi, PC; Randles, A; Hegele, LA, Moments-based method for boundary conditions in the lattice Boltzmann framework: A comparative analysis for the lid driven cavity flow, Computers & Fluids, vol. 230 (November, 2021) [doi]  [abs]
  3. Herschlag, G; Lee, S; Vetter, JS; Randles, A, Analysis of GPU Data Access Patterns on Complex Geometries for the D3Q19 Lattice Boltzmann Algorithm, Ieee Transactions on Parallel and Distributed Systems, vol. 32 no. 10 (October, 2021), pp. 2400-2414 [doi]  [abs]
  4. Puleri, DF; Balogh, P; Randles, A, Computational models of cancer cell transport through the microcirculation., Biomechanics and Modeling in Mechanobiology, vol. 20 no. 4 (August, 2021), pp. 1209-1230 [doi]  [abs]
  5. Balogh, P; Gounley, J; Roychowdhury, S; Randles, A, A data-driven approach to modeling cancer cell mechanics during microcirculatory transport., Scientific Reports, vol. 11 no. 1 (July, 2021), pp. 15232 [doi]  [abs]
  6. Randles, A; Wirsching, H-G; Dean, JA; Cheng, Y-K; Emerson, S; Pattwell, SS; Holland, EC; Michor, F, Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma., Nature Biomedical Engineering, vol. 5 no. 4 (April, 2021), pp. 346-359 [doi]  [abs]
  7. Vardhan, M; Gounley, J; Chen, SJ; Chi, EC; Kahn, AM; Leopold, JA; Randles, A, Non-invasive characterization of complex coronary lesions., Scientific Reports, vol. 11 no. 1 (April, 2021), pp. 8145 [doi]  [abs]
  8. Feiger, B; Adebiyi, A; Randles, A, Multiscale modeling of blood flow to assess neurological complications in patients supported by venoarterial extracorporeal membrane oxygenation., Computers in Biology and Medicine, vol. 129 (February, 2021), pp. 104155 [doi]  [abs]
  9. Feiger, B; Lorenzana, E; Ranney, D; Bishawi, M; Doberne, J; Vekstein, A; Voigt, S; Hughes, C; Randles, A, Predicting aneurysmal degeneration of type B aortic dissection with computational fluid dynamics, Proceedings of the 12th Acm Conference on Bioinformatics, Computational Biology, and Health Informatics, Bcb 2021 (January, 2021), ISBN 9781450384506 [doi]  [abs]
  10. Kaplan, M; Kneifel, C; Orlikowski, V; Dorff, J; Newton, M; Howard, A; Shinn, D; Bishawi, M; Chidyagwai, S; Balogh, P; Randles, A, Cloud Computing for COVID-19: Lessons Learned from Massively Parallel Models of Ventilator Splitting, Computing in Science & Engineering, vol. 22 no. 6 (November, 2020), pp. 37-47 [doi]  [abs]
  11. Pepona, M; Balogh, P; Puleri, DF; Hynes, WF; Robertson, C; Dubbin, K; Alvarado, J; Moya, ML; Randles, A, Investigating the Interaction Between Circulating Tumor Cells and Local Hydrodynamics via Experiment and Simulations., Cellular and Molecular Bioengineering, vol. 13 no. 5 (October, 2020), pp. 527-540 [doi]  [abs]
  12. Jang, L; Alvarado, J; Pepona, M; Wasson, E; Nash, L; Ortega, J; Randles, A; Maitland, D; Moya, M; Hynes, WF, Three-dimensional bioprinting of aneurysm-bearing tissue structure for endovascular deployment of embolization coils., Biofabrication (September, 2020) [doi]  [abs]
  13. Hynes, WF; Pepona, M; Robertson, C; Alvarado, J; Dubbin, K; Triplett, M; Adorno, JJ; Randles, A; Moya, ML, Examining metastatic behavior within 3D bioprinted vasculature for the validation of a 3D computational flow model., Science Advances, vol. 6 no. 35 (August, 2020), pp. eabb3308 [doi]  [abs]
  14. Cherian, J; Dabagh, M; Srinivasan, VM; Chen, S; Johnson, J; Wakhloo, A; Gupta, V; Macho, J; Randles, A; Kan, P, Balloon-Mounted Stents for Treatment of Refractory Flow Diverting Device Wall Malapposition., Operative Neurosurgery, vol. 19 no. 1 (July, 2020), pp. 37-42 [doi]  [abs]
  15. Ames, J; Puleri, DF; Balogh, P; Gounley, J; Draeger, EW; Randles, A, Multi-GPU Immersed Boundary Method Hemodynamics Simulations., Journal of Computational Science, vol. 44 (July, 2020) [doi]  [abs]
  16. Puleri, DF; Roychowdhury, S; Ames, J; Randles, A, Computational Framework to Evaluate the Hydrodynamics of Cell Scaffold Geometries., Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference, vol. 2020 (July, 2020), pp. 2299-2302, ISBN 9781728119908 [doi]  [abs]
  17. Roychowdhury, S; Gounley, J; Randles, A, Evaluating the Influence of Hemorheological Parameters on Circulating Tumor Cell Trajectory and Simulation Time, Proceedings of the Platform for Advanced Scientific Computing Conference, Pasc 2020 (June, 2020), ISBN 9781450379939 [doi]  [abs]
  18. Feiger, B; Gounley, J; Adler, D; Leopold, JA; Draeger, EW; Chaudhury, R; Ryan, J; Pathangey, G; Winarta, K; Frakes, D; Michor, F; Randles, A, Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks., Scientific Reports, vol. 10 no. 1 (June, 2020), pp. 9508 [doi]  [abs]
  19. Feiger, B; Kochar, A; Gounley, J; Bonadonna, D; Daneshmand, M; Randles, A, Determining the impacts of venoarterial extracorporeal membrane oxygenation on cerebral oxygenation using a one-dimensional blood flow simulator., Journal of Biomechanics, vol. 104 (May, 2020), pp. 109707 [doi]  [abs]
  20. Shi, H; Ames, J; Randles, A, Harvis: an interactive virtual reality tool for hemodynamic modification and simulation, Journal of Computational Science, vol. 43 (May, 2020) [doi]  [abs]
  21. Dabagh, M; Gounley, J; Randles, A, Localization of Rolling and Firm-Adhesive Interactions Between Circulating Tumor Cells and the Microvasculature Wall., Cellular and Molecular Bioengineering, vol. 13 no. 2 (April, 2020), pp. 141-154 [doi]  [abs]

Reed, Michael C.

  1. Hersey, M; Samaranayake, S; Berger, SN; Tavakoli, N; Mena, S; Nijhout, HF; Reed, MC; Best, J; Blakely, RD; Reagan, LP; Hashemi, P, Inflammation-Induced Histamine Impairs the Capacity of Escitalopram to Increase Hippocampal Extracellular Serotonin., The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, vol. 41 no. 30 (July, 2021), pp. 6564-6577 [doi]  [abs]
  2. Reed, M; Kim, R, A mathematical model of circadian rhythms and dopamine., Theoretical Biology & Medical Modelling, vol. 18 no. 1 (February, 2021), pp. 8, BioMed Central [doi]  [abs]
  3. Kim, R; Reed, M, A mathematical model of circadian rhythms and dopamine, Theoretical Biology & Medical Modelling (January, 2021), BioMed Central
  4. Best, J; Duncan, W; Sadre-Marandi, F; Hashemi, P; Nijhout, HF; Reed, M, Autoreceptor control of serotonin dynamics., Bmc Neuroscience, vol. 21 no. 1 (September, 2020), pp. 40 [doi]  [abs]
  5. Abdalla, A; West, A; Jin, Y; Saylor, RA; Qiang, B; Peña, E; Linden, DJ; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, Fast serotonin voltammetry as a versatile tool for mapping dynamic tissue architecture: I. Responses at carbon fibers describe local tissue physiology., Journal of Neurochemistry, vol. 153 no. 1 (April, 2020), pp. 33-50 [doi]  [abs]

Regan, Margaret H.

  1. Fabbri, R; Duff, T; Fan, H; Regan, MH; da Costa de Pinho, D; Tsigaridas, E; Wampler, CW; Hauenstein, JD; Giblin, PJ; Kimia, B; Leykin, A; Pajdla, T, TRPLP – Trifocal Relative Pose From Lines at Points, 2020 Ieee/Cvf Conference on Computer Vision and Pattern Recognition (Cvpr) (June, 2020), IEEE [doi]
  2. Hauenstein, JD; Regan, MH, Real monodromy action, Applied Mathematics and Computation, vol. 373 (May, 2020), pp. 124983-124983, Elsevier BV [doi]
  3. Hauenstein, J; Regan, M, Evaluating and differentiating a polynomial using a pseudo-witness set, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12097 (2020), pp. 61-69, Springer-Verlag, ISBN 9783030521998 [doi]

Robles, Colleen M

  1. Green, M; Kim, YJ; Laza, R; Robles, C, The LLV decomposition of hyper-Kähler cohomology (the known cases and the general conjectural behavior), Mathematische Annalen (January, 2021) [doi]  [abs]
  2. Han, X; Robles, C, Hodge Representations, Experimental Results, vol. 1 (2020), Cambridge University Press (CUP) [doi]  [abs]

Rossman, Benjamin

  1. Rossman, B, Shrinkage of decision lists and DNF formulas, Leibniz International Proceedings in Informatics, Lipics, vol. 185 (February, 2021), ISBN 9783959771771 [doi]  [abs]
  2. Kush, D; Rossman, B, Tree-depth and the formula complexity of subgraph isomorphism, Annual Symposium on Foundations of Computer Science (Proceedings), vol. 2020-November (November, 2020), pp. 31-42, ISBN 9781728196213 [doi]  [abs]
  3. Rossman, B, Thresholds in the Lattice of Subspaces of Fqn, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12118 LNCS (January, 2020), pp. 504-515, ISBN 9783030617912 [doi]  [abs]
  4. Cavalar, BP; Kumar, M; Rossman, B, Monotone Circuit Lower Bounds from Robust Sunflowers, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12118 LNCS (January, 2020), pp. 311-322, ISBN 9783030617912 [doi]  [abs]

Rudin, Cynthia D.

  1. Afnan, MAM; Rudin, C; Conitzer, V; Savulescu, J; Mishra, A; Liu, Y; Afnan, M, Ethical Implementation of Artificial Intelligence to Select Embryos in in Vitro Fertilization, Aies 2021 Proceedings of the 2021 Aaai/Acm Conference on Ai, Ethics, and Society (July, 2021), pp. 316-326, ISBN 9781450384735 [doi]  [abs]
  2. Chen, C; Lin, K; Rudin, C; Shaposhnik, Y; Wang, S; Wang, T, A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations, Decision Support Systems (January, 2021) [doi]  [abs]
  3. Wang, T; Morucci, M; Awan, MU; Liu, Y; Roy, S; Rudin, C; Volfovsky, A, FLAME: A fast large-scale almost matching exactly approach to causal inference, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  4. Traca, S; Rudin, C; Yan, W, Regulating greed over time in multi-armed bandits, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  5. Koyyalagunta, D; Sun, A; Draelos, RL; Rudin, C, Playing codenames with language graphs and word embeddings, Journal of Artificial Intelligence Research, vol. 71 (January, 2021), pp. 319-346 [doi]  [abs]
  6. Chen, Z; Bei, Y; Rudin, C, Concept whitening for interpretable image recognition, Nature Machine Intelligence, vol. 2 no. 12 (December, 2020), pp. 772-782 [doi]  [abs]
  7. Dong, J; Rudin, C, Exploring the cloud of variable importance for the set of all good models, Nature Machine Intelligence, vol. 2 no. 12 (December, 2020), pp. 810-824 [doi]  [abs]
  8. Wang, T; Ye, W; Geng, D; Rudin, C, Towards Practical Lipschitz Bandits, Fods 2020 Proceedings of the 2020 Acm Ims Foundations of Data Science Conference (October, 2020), pp. 129-138, ISBN 9781450381031 [doi]  [abs]
  9. Menon, S; Damian, A; Hu, S; Ravi, N; Rudin, C, PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models, Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition (January, 2020), pp. 2434-2442 [doi]  [abs]
  10. Wang, T; Rudin, C, Bandits for bmo functions, 37th International Conference on Machine Learning, Icml 2020, vol. PartF168147-13 (January, 2020), pp. 9938-9948, ISBN 9781713821120  [abs]
  11. Lin, J; Zhong, C; Hu, D; Rudin, C; Seltzer, M, Generalized and scalable optimal sparse decision trees, 37th International Conference on Machine Learning, Icml 2020, vol. PartF168147-8 (January, 2020), pp. 6106-6116, ISBN 9781713821120  [abs]
  12. Awan, MU; Morucci, M; Orlandi, V; Roy, S; Rudin, C; Volfovsky, A, Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference., Corr, vol. abs/2003.00964 (2020)

Ryser, Marc D.

  1. Murgas, KA; Ma, Y; Shahidi, LK; Mukherjee, S; Allen, AS; Shibata, D; Ryser, MD, A Bayesian Hierarchical Model to Estimate DNA Methylation Conservation in Colorectal Tumors., Bioinformatics (Oxford, England) (September, 2021) [doi]  [abs]
  2. van Seijen, M; Lips, EH; Fu, L; Giardiello, D; van Duijnhoven, F; de Munck, L; Elshof, LE; Thompson, A; Sawyer, E; Ryser, MD; Hwang, ES; Schmidt, MK; Elkhuizen, PHM; Grand Challenge PRECISION Consortium, ; Wesseling, J; Schaapveld, M, Long-term risk of subsequent ipsilateral lesions after surgery with or without radiotherapy for ductal carcinoma in situ of the breast., Br J Cancer (August, 2021) [doi]  [abs]
  3. Chan, L; Fridman, I; Grant, J; Hwang, ES; Weinfurt, K; Ryser, MD, USING PAIRWISE SIMULATED OUTCOMES TO IMPROVE THE UNDERSTANDING OF THE STATISTICAL DIFFERENCES BETWEEN TWO RISK DISTRIBUTIONS, Medical Decision Making : an International Journal of the Society for Medical Decision Making, vol. 41 no. 4 (May, 2021), pp. E284-E286
  4. Fridman, I; Chan, L; Grant, J; Fish, L; Falkovic, M; Brioux, J; Pollak, KI; Weinfurt, K; Hwang, S; Ryser, MD, A WEB-BASED PERSONALIZED DECISION TOOL FOR PATIENTS DIAGNOSED WITH DUCTAL CARCINOMA IN SITU: DEVELOPMENT, CONTENT EVALUATION, AND USABILITY TESTING, Medical Decision Making : an International Journal of the Society for Medical Decision Making, vol. 41 no. 4 (May, 2021), pp. E78-E80
  5. Butt, J; Blot, WJ; Visvanathan, K; Le Marchand, L; Wilkens, LR; Chen, Y; Sesso, HD; Teras, L; Ryser, MD; Hyslop, T; Wassertheil-Smoller, S; Tinker, LF; Potter, JD; Song, M; Berndt, SI; Waterboer, T; Pawlita, M; Epplein, M, Auto-antibodies to p53 and the Subsequent Development of Colorectal Cancer in a U.S. Prospective Cohort Consortium., Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, vol. 29 no. 12 (December, 2020), pp. 2729-2734 [doi]  [abs]
  6. Brouwer, AF; He, K; Chinn, SB; Mondul, AM; Chapman, CH; Ryser, MD; Banerjee, M; Eisenberg, MC; Meza, R; Taylor, JMG, Time-varying survival effects for squamous cell carcinomas at oropharyngeal and nonoropharyngeal head and neck sites in the United States, 1973-2015., Cancer, vol. 126 no. 23 (December, 2020), pp. 5137-5146 [doi]  [abs]
  7. Rodriguez-Homs, LG; Hammill, BG; Ryser, MD; Phillips, HR; Mosca, PJ, Relationship Between HCAHPS Scores and Survey Response Rate Is Linked to Hospital Size., J Patient Exp, vol. 7 no. 6 (December, 2020), pp. 1543-1548 [doi]  [abs]
  8. Ryser, MD; Sorribes, IC; Greenwald, M; Wu, E; Hall, A; Mallo, D; King, LM; Hardman, T; Simpson, L; Maley, CC; Marks, JR; Shibata, D; Hwang, ES, Inferring the evolutionary dynamics of ductal carcinoma in situ through multi-regional sequencing and mathematical modeling., Cancer Research, vol. 80 no. 21 (November, 2020)
  9. Epplein, M; Le Marchand, L; Cover, TL; Song, M; Blot, WJ; Peek, RM; Teras, LR; Visvanathan, K; Chen, Y; Sesso, HD; Zeleniuch-Jacquotte, A; Berndt, SI; Potter, JD; Ryser, MD; Haiman, CA; Wassertheil-Smoller, S; Tinker, LF; Waterboer, T; Butt, J, Association of Combined Sero-Positivity to Helicobacter pylori and Streptococcus gallolyticus with Risk of Colorectal Cancer., Microorganisms, vol. 8 no. 11 (October, 2020) [doi]  [abs]
  10. Shehata, MN; Rahbar, H; Flanagan, MR; Kilgore, MR; Lee, CI; Ryser, MD; Lowry, KP, Risk for Upgrade to Malignancy After Breast Core Needle Biopsy Diagnosis of Lobular Neoplasia: A Systematic Review and Meta-Analysis., Journal of the American College of Radiology : Jacr, vol. 17 no. 10 (October, 2020), pp. 1207-1219 [doi]  [abs]
  11. Williamson, T; Ryser, MD; Ubel, PA; Abdelgadir, J; Spears, CA; Liu, B; Komisarow, J; Lemmon, ME; Elsamadicy, A; Lad, SP, Withdrawal of Life-supporting Treatment in Severe Traumatic Brain Injury., Jama Surg, vol. 155 no. 8 (August, 2020), pp. 723-731 [doi]  [abs]
  12. Chootipongchaivat, S; van Ravesteyn, NT; Li, X; Huang, H; Weedon-Fekjær, H; Ryser, MD; Weaver, DL; Burnside, ES; Heckman-Stoddard, BM; de Koning, HJ; Lee, SJ, Modeling the natural history of ductal carcinoma in situ based on population data., Breast Cancer Res, vol. 22 no. 1 (May, 2020), pp. 53 [doi]  [abs]
  13. Ryser, MD; Hendrix, L; Thomas, SM; Lynch, T; McCarthy, A; Mohammed, Z; Francescatti, AB; Frank, ES; Partridge, AH; Thompson, AM; Hyslop, T; Hwang, E-SS, Ipsilateral invasive cancer risk after diagnosis with ductal carcinoma in situ (DCIS): Comparison of patients with and without index surgery., Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, vol. 38 no. 15 (May, 2020)
  14. Rozenblatt-Rosen, O; Regev, A; Oberdoerffer, P; Nawy, T; Hupalowska, A; Rood, JE; Ashenberg, O; Cerami, E; Coffey, RJ; Demir, E; Ding, L; Esplin, ED; Ford, JM; Goecks, J; Ghosh, S; Gray, JW; Guinney, J; Hanlon, SE; Hughes, SK; Hwang, ES; Iacobuzio-Donahue, CA; Jané-Valbuena, J; Johnson, BE; Lau, KS; Lively, T; Mazzilli, SA; Pe'er, D; Santagata, S; Shalek, AK; Schapiro, D; Snyder, MP; Sorger, PK; Spira, AE; Srivastava, S; Tan, K; West, RB; Williams, EH; Human Tumor Atlas Network,, The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution., Cell, vol. 181 no. 2 (April, 2020), pp. 236-249 [doi]  [abs]
  15. Ryser, MD; Mallo, D; Hall, A; Hardman, T; King, LM; Tatishchev, S; Sorribes, IC; Maley, CC; Marks, JR; Hwang, ES; Shibata, D, Minimal barriers to invasion during human colorectal tumor growth., Nature Communications, vol. 11 no. 1 (March, 2020), pp. 1280 [doi]  [abs]
  16. Ryser, MD; Hwang, ES, Response to Habel and Buist., J Natl Cancer Inst, vol. 112 no. 2 (February, 2020), pp. 216-217 [doi]
  17. Fridman, I; Kumaresan, V; Vijendra, P; Seshadri, P; Garland, S; Kim, G; Fagerlin, A; Ubel, PA; Ryser, MD, INFORMATION PROCESSING AND PATIENT DECISION MAKING: A BIG DATA APPROACH TO TREATMENT CHOICE IN PROSTATE CANCER PATIENTS, Medical Decision Making : an International Journal of the Society for Medical Decision Making, vol. 40 no. 1 (January, 2020), pp. E183-E184, SAGE PUBLICATIONS INC
  18. Williamson, T; Ryser, MD; Abdelgadir, J; Lemmon, M; Barks, MC; Zakare, R; Ubel, PA, Surgical decision making in the setting of severe traumatic brain injury: A survey of neurosurgeons., Plos One, vol. 15 no. 3 (2020), pp. e0228947 [doi]  [abs]

Sachs, Matthias Ernst

  1. Lu, J; Sachs, M; Steinerberger, S, Quadrature Points via Heat Kernel Repulsion, Constructive Approximation, vol. 51 no. 1 (February, 2020), pp. 27-48 [doi]  [abs]
  2. Leimkuhler, B; Sachs, M; Stoltz, G, Hypocoercivity Properties of Adaptive Langevin Dynamics, Siam Journal on Applied Mathematics, vol. 80 no. 3 (January, 2020), pp. 1197-1222, Society for Industrial & Applied Mathematics (SIAM) [doi]

Saper, Leslie

  1. Cox, D; Esnault, H; Hain, R; Harris, M; Ji, L; Saito, M-H; Saper, L, Remembering Steve Zucker, edited by Cox, D; Harris, M; Ji, L, Notices of the American Mathematical Society, vol. 68 no. 7 (August, 2021), pp. 1156-1172, American Mathematical Society

Sapiro, Guillermo

  1. Perochon, S; Di Martino, M; Aiello, R; Baker, J; Carpenter, K; Chang, Z; Compton, S; Davis, N; Eichner, B; Espinosa, S; Flowers, J; Franz, L; Gagliano, M; Harris, A; Howard, J; Kollins, SH; Perrin, EM; Raj, P; Spanos, M; Walter, B; Sapiro, G; Dawson, G, A scalable computational approach to assessing response to name in toddlers with autism., The Journal of Child Psychology and Psychiatry and Allied Disciplines, vol. 62 no. 9 (September, 2021), pp. 1120-1131 [doi]  [abs]
  2. Chang, Z; Di Martino, JM; Aiello, R; Baker, J; Carpenter, K; Compton, S; Davis, N; Eichner, B; Espinosa, S; Flowers, J; Franz, L; Harris, A; Howard, J; Perochon, S; Perrin, EM; Krishnappa Babu, PR; Spanos, M; Sullivan, C; Walter, BK; Kollins, SH; Dawson, G; Sapiro, G, Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder., Jama Pediatr, vol. 175 no. 8 (August, 2021), pp. 827-836 [doi]  [abs]
  3. Bovery, M; Dawson, G; Hashemi, J; Sapiro, G, A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and Its Application in Autism Spectrum Disorder, Ieee Transactions on Affective Computing, vol. 12 no. 3 (July, 2021), pp. 722-731, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  4. Emani, PS; Warrell, J; Anticevic, A; Bekiranov, S; Gandal, M; McConnell, MJ; Sapiro, G; Aspuru-Guzik, A; Baker, JT; Bastiani, M; Murray, JD; Sotiropoulos, SN; Taylor, J; Senthil, G; Lehner, T; Gerstein, MB; Harrow, AW, Quantum computing at the frontiers of biological sciences., Nature Methods, vol. 18 no. 7 (July, 2021), pp. 701-709 [doi]
  5. Dong, H; Wang, Z; Qiu, Q; Sapiro, G, Using text to teach image retrieval, Ieee Computer Society Conference on Computer Vision and Pattern Recognition Workshops (June, 2021), pp. 1643-1652, ISBN 9781665448994 [doi]  [abs]
  6. Solomon, O; Palnitkar, T; Patriat, R; Braun, H; Aman, J; Park, MC; Vitek, J; Sapiro, G; Harel, N, Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI., Human Brain Mapping, vol. 42 no. 9 (June, 2021), pp. 2862-2879 [doi]  [abs]
  7. Carpenter, KLH; Hahemi, J; Campbell, K; Lippmann, SJ; Baker, JP; Egger, HL; Espinosa, S; Vermeer, S; Sapiro, G; Dawson, G, Digital Behavioral Phenotyping Detects Atypical Pattern of Facial Expression in Toddlers with Autism., Autism Res, vol. 14 no. 3 (March, 2021), pp. 488-499 [doi]  [abs]
  8. Hashemi, J; Dawson, G; Carpenter, KLH; Campbell, K; Qiu, Q; Espinosa, S; Marsan, S; Baker, JP; Egger, HL; Sapiro, G, Computer Vision Analysis for Quantification of Autism Risk Behaviors, Ieee Transactions on Affective Computing, vol. 12 no. 1 (January, 2021), pp. 215-226, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  9. Krishnappababu, PR; Di Martino, M; Chang, Z; Perochon, SP; Carpenter, KLH; Compton, S; Espinosa, S; Dawson, G; Sapiro, G, Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder, Ieee Transactions on Affective Computing (January, 2021) [doi]  [abs]
  10. Achddou, R; Di Martino, JM; Sapiro, G, Nested learning for multi-level classification, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2021-June (January, 2021), pp. 2815-2819 [doi]  [abs]
  11. Di Martino, JM; Qiu, Q; Sapiro, G, Rethinking Shape From Shading for Spoofing Detection., Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society, vol. 30 (January, 2021), pp. 1086-1099 [doi]  [abs]
  12. Chang, Z; Chen, Z; Stephen, CD; Schmahmann, JD; Wu, H-T; Sapiro, G; Gupta, AS, Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning., Scientific Reports, vol. 10 no. 1 (October, 2020), pp. 18641 [doi]  [abs]
  13. Major, S; Campbell, K; Espinosa, S; Baker, JP; Carpenter, KL; Sapiro, G; Vermeer, S; Dawson, G, Impact of a digital Modified Checklist for Autism in Toddlers-Revised on likelihood and age of autism diagnosis and referral for developmental evaluation., Autism, vol. 24 no. 7 (October, 2020), pp. 1629-1638 [doi]  [abs]
  14. Tenenbaum, EJ; Carpenter, KLH; Sabatos-DeVito, M; Hashemi, J; Vermeer, S; Sapiro, G; Dawson, G, A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder., Autism Res, vol. 13 no. 8 (August, 2020), pp. 1373-1382 [doi]  [abs]
  15. Simhal, AK; Carpenter, KLH; Nadeem, S; Kurtzberg, J; Song, A; Tannenbaum, A; Sapiro, G; Dawson, G, Measuring robustness of brain networks in autism spectrum disorder with Ricci curvature., Scientific Reports, vol. 10 no. 1 (July, 2020), pp. 10819 [doi]  [abs]
  16. Martino, JMD; Suzacq, F; Delbracio, M; Qiu, Q; Sapiro, G, Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method., Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 42 no. 7 (July, 2020), pp. 1582-1593 [doi]  [abs]
  17. Asiedu, MN; Skerrett, E; Sapiro, G; Ramanujam, N, Combining multiple contrasts for improving machine learning-based classification of cervical cancers with a low-cost point-of-care Pocket colposcope., Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference, vol. 2020 (July, 2020), pp. 1148-1151, ISBN 9781728119908 [doi]  [abs]
  18. Isaev, DY; Major, S; Murias, M; Carpenter, KLH; Carlson, D; Sapiro, G; Dawson, G, Relative Average Look Duration and its Association with Neurophysiological Activity in Young Children with Autism Spectrum Disorder., Scientific Reports, vol. 10 no. 1 (February, 2020), pp. 1912 [doi]  [abs]
  19. Dawson, G; Campbell, K; Hashemi, J; Lippmann, SJ; Smith, V; Carpenter, K; Egger, H; Espinosa, S; Vermeer, S; Baker, J; Sapiro, G, Author Correction: Atypical postural control can be detected via computer vision analysis in toddlers with autism spectrum disorder., Scientific Reports, vol. 10 no. 1 (January, 2020), pp. 616 [doi]  [abs]
  20. Giryes, R; Sapiro, G; Bronstein, AM, Erratum: Deep neural networks with random Gaussian weights: A universal classification strategy? (IEEE Transactions on Signal Processing (2016) 64:13 (3444-3457) DOI: 10.1109/TSP.2016.2546221), Ieee Transactions on Signal Processing, vol. 68 (January, 2020), pp. 529-531 [doi]  [abs]
  21. Cohen, G; Sapiro, G; Giryes, R, Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors, Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition (January, 2020), pp. 14441-14450 [doi]  [abs]
  22. Martinez, N; Bertran, M; Sapiro, G, Minimax pareto fairness: A multi objective perspective, 37th International Conference on Machine Learning, Icml 2020, vol. PartF168147-9 (January, 2020), pp. 6711-6720, ISBN 9781713821120  [abs]
  23. Wang, Z; Cheng, X; Sapiro, G; Qiu, Q, A dictionary approach to domain-invariant learning in deep networks, Advances in Neural Information Processing Systems, vol. 2020-December (January, 2020)  [abs]
  24. Bertran, M; Martinezf, N; Phielipp, M; Sapiro, G, Instance-based generalization in reinforcement learning, Advances in Neural Information Processing Systems, vol. 2020-December (January, 2020)  [abs]

Schott, Sarah

  1. Schott, S; Slate Young, E; Bookman, J; Peterson, B, Evaluating a Large-Scale Multi-Institution Project: Challenges Faced and Lessons Learned, The Journal of Mathematics and Science: Collaborative Explorations (Jmsce), vol. 16 no. 1 (2020) [doi]  [abs]

Smith, David A.

  1. David A. Smith, My Life in Essays (March 11, 2021) [Life%20in%20Essays%203-10-21.pdf]  [abs]

Sober, Barak

  1. Faigenbaum-Golovin, S; Shaus, A; Sober, B; Gerber, Y; Turkel, E; Piasetzky, E; Finkelstein, I, Literacy in Judah and Israel algorithmic and forensic examination of the Arad and Samaria Ostraca, Near Eastern Archaeology, vol. 84 no. 2 (June, 2021), pp. 148-158 [doi]  [abs]
  2. Sober, B; Aizenbud, Y; Levin, D, Approximation of functions over manifolds: A Moving Least-Squares approach, Journal of Computational and Applied Mathematics, vol. 383 (February, 2021), pp. 113140-113140, Elsevier BV [doi]  [abs]
  3. Dym, N; Sober, B; Daubechies, I, Expression of Fractals Through Neural Network Functions, Ieee Journal on Selected Areas in Information Theory, vol. 1 no. 1 (May, 2020), pp. 57-66, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  4. Pu, W; Sober, B; Daly, N; Higgitt, C; Daubechies, I; Rodrigues, MRD, A connected auto-encoders based approach for image separation with side information: With applications to art investigation, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2020-May (May, 2020), pp. 2213-2217, ISBN 9781509066315 [doi]  [abs]
  5. Faigenbaum-Golovin, S; Shaus, A; Sober, B; Turkel, E; Piasetzky, E; Finkelstein, I, Algorithmic handwriting analysis of the Samaria inscriptions illuminates bureaucratic apparatus in biblical Israel., Plos One, vol. 15 no. 1 (January, 2020), pp. e0227452 [doi]  [abs]
  6. Shaus, A; Gerber, Y; Faigenbaum-Golovin, S; Sober, B; Piasetzky, E; Finkelstein, I, Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level., Plos One, vol. 15 no. 9 (January, 2020), pp. e0237962 [doi]  [abs]

Solomon, Yitzchak E.

  1. Oudot, S; Solomon, E, Barcode embeddings for metric graphs, Algebraic & Geometric Topology, vol. 21 no. 3 (August, 2021), pp. 1209-1266, Mathematical Sciences Publishers [doi]
  2. Solomon, E; Wagner, A; Bendich, P, A Fast and Robust Method for Global Topological Functional Optimization, 24th International Conference on Artificial Intelligence and Statistics (Aistats), vol. 130 (2021), pp. 109-+

Soltani, Mohammadreza

  1. Cannella, C; Soltani, M; Tarokh, V, Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows., Iclr (2021), OpenReview.net

Sorribes Rodriguez, Inmaculada C

  1. Ryser, MD; Mallo, D; Hall, A; Hardman, T; King, LM; Tatishchev, S; Sorribes, IC; Maley, CC; Marks, JR; Hwang, ES; Shibata, D, Minimal barriers to invasion during human colorectal tumor growth., Nature Communications, vol. 11 no. 1 (March, 2020), pp. 1280, Springer Science and Business Media LLC [doi]  [abs]
  2. Sorribes, IC; Handelman, SK; Jain, HV, Mitigating temozolomide resistance in glioblastoma via DNA damage-repair inhibition., Journal of the Royal Society, Interface, vol. 17 no. 162 (January, 2020), pp. 20190722, The Royal Society [doi]  [abs]

Tarokh, Vahid

  1. Kojima, S; Maruta, K; Feng, Y; Ahn, CJ; Tarokh, V, CNN-Based Joint SNR and Doppler Shift Classification Using Spectrogram Images for Adaptive Modulation and Coding, Ieee Transactions on Communications, vol. 69 no. 8 (August, 2021), pp. 5152-5167 [doi]  [abs]
  2. Feng, Y; Wongkamthong, C; Soltani, M; Ng, Y; Gogineni, S; Kang, B; Pezeshki, A; Calderbank, R; Rangaswamy, M; Tarokh, V, Knowledge-Aided Data-Driven Radar Clutter Representation, Ieee National Radar Conference Proceedings, vol. 2021-May (May, 2021), ISBN 9781728176093 [doi]  [abs]
  3. Ding, J; Diao, E; Zhou, J; Tarokh, V, On Statistical Efficiency in Learning, Ieee Transactions on Information Theory, vol. 67 no. 4 (April, 2021), pp. 2488-2506 [doi]  [abs]
  4. Soltani, M; Wu, S; Li, Y; Ravier, R; Ding, J; Tarokh, V, Compressing Deep Networks Using Fisher Score of Feature Maps, Data Compression Conference Proceedings, vol. 2021-March (March, 2021), pp. 371, ISBN 9780738112275 [doi]  [abs]
  5. Yang, H; Jing, D; Tarokh, V; Bewley, G; Ferrari, S, Flow parameter estimation based on on-board measurements of air vehicle traversing turbulent flows, Aiaa Scitech 2021 Forum (January, 2021), pp. 1-10, ISBN 9781624106095  [abs]
  6. Le, CP; Soltani, M; Ravier, R; Tarokh, V, Task-aware neural architecture search, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2021-June (January, 2021), pp. 4090-4094 [doi]  [abs]
  7. Angjelichinoski, M; Soltani, M; Choi, J; Pesaran, B; Tarokh, V, Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data., Ieee Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society, vol. 29 (January, 2021), pp. 1058-1067 [doi]  [abs]
  8. Xing, J; Fischer, D; Labh, N; Piersma, R; Lee, BC; Xia, YA; Sahai, T; Tarokh, V, Talaria: A Framework for Simulation of Permissioned Blockchains for Logistics and Beyond., Corr, vol. abs/2103.02260 (2021)
  9. Le, CP; Soltani, M; Ravier, RJ; Tarokh, V, Neural Architecture Search From Task Similarity Measure., Corr, vol. abs/2103.00241 (2021)
  10. Hasan, A; Elkhalil, K; Pereira, JM; Farsiu, S; Blanchet, JH; Tarokh, V, Deep Extreme Value Copulas for Estimation and Sampling., Corr, vol. abs/2102.09042 (2021)
  11. Le, CP; Soltani, M; Ravier, RJ; Standley, T; Savarese, S; Tarokh, V, Neural Architecture Search From Fréchet Task Distance., Corr, vol. abs/2103.12827 (2021)
  12. Ding, J; Diao, E; Zhou, J; Tarokh, V, On Statistical Efficiency in Learning., Ieee Trans. Inf. Theory, vol. 67 (2021), pp. 2488-2506 [doi]
  13. Chan, CH; Tarokh, V; Xiong, M, Convergence Rate of Empirical Spectral Distribution of Random Matrices From Linear Codes., Ieee Trans. Inf. Theory, vol. 67 (2021), pp. 1080-1087 [doi]
  14. Ng, Y; Hasan, A; Elkhalil, K; Tarokh, V, Generative Archimedean Copulas., Corr, vol. abs/2102.11351 (2021)
  15. Cannella, C; Tarokh, V, Semi-Empirical Objective Functions for MCMC Proposal Optimization., Corr, vol. abs/2106.02104 (2021)
  16. Diao, E; Ding, J; Tarokh, V, Gradient Assisted Learning., Corr, vol. abs/2106.01425 (2021)
  17. Diao, E; Ding, J; Tarokh, V, SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients., Corr, vol. abs/2106.01432 (2021)
  18. Yanchenko, AK; Soltani, M; Ravier, RJ; Mukherjee, S; Tarokh, V, Towards Explainable Convolutional Features for Music Audio Modeling., Corr, vol. abs/2106.00110 (2021)
  19. Cannella, C; Soltani, M; Tarokh, V, Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows., Iclr (2021), OpenReview.net
  20. Angjelichinoski, M; Soltani, M; Choi, J; Pesaran, B; Tarokh, V, Deep James-Stein Neural Networks for Brain-Computer Interfaces, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2020-May (May, 2020), pp. 1339-1343, IEEE, ISBN 9781509066315 [doi]  [abs]
  21. Diao, E; Ding, J; Tarokh, V, DRASIC: Distributed recurrent autoencoder for scalable image compression, Data Compression Conference Proceedings, vol. 2020-March (March, 2020), pp. 3-12, IEEE [doi]  [abs]
  22. Angjelichinoski, M; Choi, J; Banerjee, T; Pesaran, B; Tarokh, V, Cross-subject decoding of eye movement goals from local field potentials., Journal of Neural Engineering, vol. 17 no. 1 (February, 2020), pp. 016067 [doi]  [abs]
  23. Zhou, Y; Wang, Z; Ji, K; Liang, Y; Tarokh, V, Proximal gradient algorithm with momentum and flexible parameter restart for nonconvex optimization, Ijcai International Joint Conference on Artificial Intelligence, vol. 2021-January (January, 2020), pp. 1445-1451  [abs]
  24. Jeong, S; Li, X; Yang, J; Li, Q; Tarokh, V, Sparse representation-based denoising for high-resolution brain activation and functional connectivity modeling: A task fMRI study, Ieee Access, vol. 8 (January, 2020), pp. 36728-36740 [doi]  [abs]
  25. Wu, S; Diao, E; Ding, J; Tarokh, V, Deep Clustering of Compressed Variational Embeddings., edited by Bilgin, A; Marcellin, MW; Serra-Sagristà, J; Storer, JA, Dcc (2020), pp. 399-399, IEEE, ISBN 978-1-7281-6457-1
  26. Diao, E; Ding, J; Tarokh, V, DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression., edited by Bilgin, A; Marcellin, MW; Serra-Sagristà, J; Storer, JA, Dcc (2020), pp. 3-12, IEEE, ISBN 978-1-7281-6457-1
  27. Zhou, Y; Wang, Z; Ji, K; Liang, Y; Tarokh, V, Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization., edited by Bessiere, C, Ijcai (2020), pp. 1445-1451, ijcai.org
  28. Hasan, A; Pereira, JM; Farsiu, S; Tarokh, V, Learning latent stochastic differential equations with variational auto-encoders., Corr, vol. abs/2007.06075 (2020)
  29. Angjelichinoski, M; Pesaran, B; Tarokh, V, Deep Cross-Subject Mapping of Neural Activity., Corr, vol. abs/2007.06407 (2020)
  30. Le, CP; Zhou, Y; Ding, J; Tarokh, V, Supervised Encoding for Discrete Representation Learning., Icassp (2020), pp. 3447-3451, IEEE, ISBN 978-1-5090-6631-5
  31. Ng, Y; Pereira, JM; Garagic, D; Tarokh, V, Robust Marine Buoy Placement for Ship Detection Using Dropout K-Means., Icassp (2020), pp. 3757-3761, IEEE, ISBN 978-1-5090-6631-5
  32. Cannella, C; Soltani, M; Tarokh, V, Projected Latent Markov Chain Monte Carlo: Conditional Inference with Normalizing Flows., Corr, vol. abs/2007.06140 (2020)
  33. Ravier, RJ; Soltani, M; Alfaiate, MAD; Garagic, D; Tarokh, V, An Interpretable Baseline for Time Series Classification Without Intensive Learning., Corr, vol. abs/2007.06682 (2020)
  34. Cannella, C; Ding, J; Soltani, M; Zhou, Y; Tarokh, V, Perception-Distortion Trade-Off with Restricted Boltzmann Machines., Icassp (2020), pp. 4022-4026, IEEE, ISBN 978-1-5090-6631-5
  35. Hasan, A; Pereira, JM; Ravier, RJ; Farsiu, S; Tarokh, V, Learning Partial Differential Equations From Data Using Neural Networks., Icassp (2020), pp. 3962-3966, IEEE, ISBN 978-1-5090-6631-5
  36. Elkhalil, K; Hasan, A; Ding, J; Farsiu, S; Tarokh, V, Fisher Auto-Encoders., edited by Banerjee, A; Fukumizu, K, Corr, vol. abs/2007.06120 (2020), pp. 352-360, PMLR
  37. Wang, J; Xue, M; Culhane, R; Diao, E; Ding, J; Tarokh, V, Speech Emotion Recognition with Dual-Sequence LSTM Architecture., Icassp (2020), pp. 6474-6478, IEEE, ISBN 978-1-5090-6631-5
  38. Diao, E; Ding, J; Tarokh, V, HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients., Corr, vol. abs/2010.01264 (2020)

Vafaee, Faramarz

  1. Ballinger, W; Hsu, C; Mackey, W; Ni, Y; Ochse, T; Vafaee, F, The prism manifold realization problem, Algebraic & Geometric Topology, vol. 20 no. 2 (April, 2020), pp. 757-816, Mathematical Sciences Publishers [doi]

Venakides, Stephanos

  1. Venakides, S; Komineas, S; Melcher, C, Chiral skyrmions of large radius, Physica D: Nonlinear Phenomena, vol. 418 (April, 2021), Elsevier [doi]  [abs]
  2. Komineas, S; Melcher, C; Venakides, S, The profile of chiral skyrmions of small radius, Nonlinearity, vol. 33 no. 7 (July, 2020), pp. 3395-3408, IOP Publishing [doi]  [abs]

Viel, Shira

  1. Barcelo, H; Bernstein, M; Bockting-Conrad, S; McNicholas, E; Nyman, K; Viel, S, Algebraic voting theory & representations of Sm≀Sn, Advances in Applied Mathematics, vol. 120 (September, 2020) [doi]  [abs]

Wagner, Alexander Y

  1. Wagner, A, Nonembeddability of persistence diagrams with $p>2$ Wasserstein metric, Proceedings of the American Mathematical Society, vol. 149 no. 6 (March, 2021), pp. 2673-2677, American Mathematical Society (AMS) [doi]
  2. Solomon, E; Wagner, A; Bendich, P, A Fast and Robust Method for Global Topological Functional Optimization, 24th International Conference on Artificial Intelligence and Statistics (Aistats), vol. 130 (2021), pp. 109-+
  3. Bubenik, P; Wagner, A, Embeddings of persistence diagrams into Hilbert spaces, Journal of Applied and Computational Topology, vol. 4 no. 3 (September, 2020), pp. 339-351, Springer Science and Business Media LLC [doi]

Wang, Min

  1. Wang, Y; Cheung, SW; Chung, ET; Efendiev, Y; Wang, M, Deep multiscale model learning, Journal of Computational Physics, vol. 406 (April, 2020), pp. 109071-109071, Elsevier BV [doi]  [abs]
  2. Wang, M; Cheung, SW; Chung, ET; Vasilyeva, M; Wang, Y, Generalized multiscale multicontinuum model for fractured vuggy carbonate reservoirs, Journal of Computational and Applied Mathematics, vol. 366 (March, 2020), pp. 112370-112370, Elsevier BV [doi]
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Watson, Alexander

  1. Lu, J; Watson, AB; Weinstein, MI, Dirac operators and domain walls, Siam Journal on Mathematical Analysis, vol. 52 no. 2 (January, 2020), pp. 1115-1145 [doi]  [abs]

Wickelgren, Kirsten G.

  1. Pauli, S; Wickelgren, K, Applications to A1 -enumerative geometry of the A1 -degree, Research in Mathematical Sciences, vol. 8 no. 2 (June, 2021) [doi]  [abs]
  2. Pauli, S; Wickelgren, K, Applications to A(1)-enumerative geometry of the A(1)-degree, Research in Mathematical Sciences, vol. 8 no. 2 (June, 2021) [doi]
  3. Srinivasan, P; Wickelgren, K, An arithmetic count of the lines meeting four lines in P3, Transactions of the American Mathematical Society, vol. 374 no. 5 (May, 2021), pp. 3427-3451 [doi]  [abs]
  4. Bachmann, T; Wickelgren, K, EULER CLASSES: SIX-FUNCTORS FORMALISM, DUALITIES, INTEGRALITY and LINEAR SUBSPACES of COMPLETE INTERSECTIONS, Journal of the Institute of Mathematics of Jussieu (January, 2021) [doi]  [abs]
  5. Leo Kass, J; Wickelgren, K, An arithmetic count of the lines on a smooth cubic surface, Compositio Mathematica (January, 2021), pp. 677-709 [doi]  [abs]
  6. Kass, JL; Wickelgren, K, A classical proof that the algebraic homotopy class of a rational function is the residue pairing, Linear Algebra and Its Applications, vol. 595 (June, 2020), pp. 157-181 [doi]  [abs]
  7. Bethea, C; Kass, JL; Wickelgren, K, Examples of wild ramification in an enriched riemann–hurwitz formula, Surveys on Discrete and Computational Geometry: Twenty Years Later, vol. 745 (January, 2020), pp. 69-82 [doi]  [abs]

Witelski, Thomas P.   (search)

  1. Zhu, H; Zhang, P; Zhong, Z; Xia, J; Rich, J; Mai, J; Su, X; Tian, Z; Bachman, H; Rufo, J; Gu, Y; Kang, P; Chakrabarty, K; Witelski, TP; Huang, TJ, Acoustohydrodynamic tweezers via spatial arrangement of streaming vortices., Science Advances, vol. 7 no. 2 (January, 2021) [doi]  [abs]
  2. Nakad, M; Witelski, T; Domec, JC; Sevanto, S; Katul, G, Taylor dispersion in osmotically driven laminar flows in phloem, Journal of Fluid Mechanics, vol. 913 (January, 2021), Cambridge University Press (CUP) [doi]  [abs]
  3. Aguareles, M; Chapman, SJ; Witelski, T, Dynamics of spiral waves in the complex Ginzburg–Landau equation in bounded domains, Physica D: Nonlinear Phenomena, vol. 414 (December, 2020) [doi]  [abs]
  4. Ji, H; Witelski, T, Steady states and dynamics of a thin-film-type equation with non-conserved mass, European Journal of Applied Mathematics, vol. 31 no. 6 (December, 2020), pp. 968-1001, Cambridge University Press (CUP) [doi]  [abs]
  5. Liu, W; Witelski, TP, Steady states of thin film droplets on chemically heterogeneous substrates, Ima Journal of Applied Mathematics, vol. 85 no. 6 (November, 2020), pp. 980-1020, Oxford University Press (OUP) [doi]  [abs]
  6. Dijksman, JA; Mukhopadhyay, S; Gaebler, C; Witelski, TP; Behringer, RP, Erratum: Obtaining self-similar scalings in focusing flows [Phys. Rev. E 92, 043016 (2015)]., Physical Review. E, vol. 101 no. 5-2 (May, 2020), pp. 059902 [doi]  [abs]
  7. Witelski, TP, Nonlinear dynamics of dewetting thin films, Aims Mathematics, vol. 5 no. 5 (January, 2020), pp. 4229-4259 [doi]  [abs]

Wu, Hau-Tieng

  1. Dunson, DB; Wu, HT; Wu, N, Spectral convergence of graph Laplacian and heat kernel reconstruction in L from random samples, Applied and Computational Harmonic Analysis, vol. 55 (November, 2021), pp. 282-336 [doi]  [abs]
  2. Sourisseau, M; Wang, YG; Womersley, RS; Wu, HT; Yu, WH, Improve concentration of frequency and time (ConceFT) by novel complex spherical designs, Applied and Computational Harmonic Analysis, vol. 54 (September, 2021), pp. 137-144, Elsevier BV [doi]  [abs]
  3. Wu, HT; Lai, TL; Haddad, GG; Muotri, A, Oscillatory Biomedical Signals: Frontiers in Mathematical Models and Statistical Analysis, Frontiers in Applied Mathematics and Statistics, vol. 7 (July, 2021) [doi]  [abs]
  4. DiPietro, JA; Raghunathan, RS; Wu, H-T; Bai, J; Watson, H; Sgambati, FP; Henderson, JL; Pien, GW, Fetal heart rate during maternal sleep., Developmental Psychobiology, vol. 63 no. 5 (July, 2021), pp. 945-959 [doi]  [abs]
  5. Steinerberger, S; Wu, H-T, On Zeroes of Random Polynomials and an Application to Unwinding, International Mathematics Research Notices, vol. 2021 no. 13 (June, 2021), pp. 10100-10117, Oxford University Press (OUP) [doi]  [abs]
  6. Wu, H-T; Alian, A; Shelley, K, A new approach to complicated and noisy physiological waveforms analysis: peripheral venous pressure waveform as an example., Journal of Clinical Monitoring and Computing, vol. 35 no. 3 (May, 2021), pp. 637-653 [doi]  [abs]
  7. Liu, T-C; Liu, Y-W; Wu, H-T, Denoising click-evoked otoacoustic emission signals by optimal shrinkage., The Journal of the Acoustical Society of America, vol. 149 no. 4 (April, 2021), pp. 2659, Acoustical Society of America (ASA) [doi]  [abs]
  8. Chung, YM; Hu, CS; Lo, YL; Wu, HT, A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification, Frontiers in Physiology, vol. 12 (March, 2021) [doi]  [abs]
  9. Malik, J; Loring, Z; Piccini, JP; Wu, H-T, Interpretable morphological features for efficient single-lead automatic ventricular ectopy detection., J Electrocardiol, vol. 65 (March, 2021), pp. 55-63 [doi]  [abs]
  10. Liu, G-R; Lin, T-Y; Wu, H-T; Sheu, Y-C; Liu, C-L; Liu, W-T; Yang, M-C; Ni, Y-L; Chou, K-T; Chen, C-H; Wu, D; Lan, C-C; Chiu, K-L; Chiu, H-Y; Lo, Y-L, Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm., Journal of Clinical Sleep Medicine : Jcsm : Official Publication of the American Academy of Sleep Medicine, vol. 17 no. 2 (February, 2021), pp. 159-166 [doi]  [abs]
  11. Huang, Y-C; Lin, T-Y; Wu, H-T; Chang, P-J; Lo, C-Y; Wang, T-Y; Kuo, C-HS; Lin, S-M; Chung, F-T; Lin, H-C; Hsieh, M-H; Lo, Y-L, Cardiorespiratory coupling is associated with exercise capacity in patients with chronic obstructive pulmonary disease., Bmc Pulmonary Medicine, vol. 21 no. 1 (January, 2021), pp. 22 [doi]  [abs]
  12. Colominas, MA; Wu, HT, Decomposing non-stationary signals with time-varying wave-shape functions, Ieee Transactions on Signal Processing, vol. 69 (January, 2021), pp. 5094-5104 [doi]  [abs]
  13. Frasch, MG; Shen, C; Wu, H-T; Mueller, A; Neuhaus, E; Bernier, RA; Kamara, D; Beauchaine, TP, Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?, Journal of Autism and Developmental Disorders, vol. 51 no. 1 (January, 2021), pp. 346-356 [doi]  [abs]
  14. Wang, H-HS; Cahill, D; Panagides, J; Nelson, CP; Wu, H-T; Estrada, C, Pattern recognition algorithm to identify detrusor overactivity on urodynamics., Neurourology and Urodynamics, vol. 40 no. 1 (January, 2021), pp. 428-434 [doi]  [abs]
  15. Ding, X; Wu, HT, On the Spectral Property of Kernel-Based Sensor Fusion Algorithms of High Dimensional Data, Ieee Transactions on Information Theory, vol. 67 no. 1 (January, 2021), pp. 640-670, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  16. Meynard, A; Wu, HT, An Efficient Forecasting Approach to Reduce Boundary Effects in Real-Time Time-Frequency Analysis, Ieee Transactions on Signal Processing, vol. 69 (January, 2021), pp. 1653-1663 [doi]  [abs]
  17. Tan, C; Zhang, L; Wu, HT; Qian, T, A novel feature representation approach for single-lead heartbeat classification based on adaptive Fourier decomposition, International Journal of Wavelets, Multiresolution and Information Processing (January, 2021) [doi]  [abs]
  18. Liu, GR; Lo, YL; Sheu, YC; Wu, HT, Explore Intrinsic Geometry of Sleep Dynamics and Predict Sleep Stage by Unsupervised Learning Techniques, in Springer Optimization and Its Applications, vol. 168 (January, 2021), pp. 279-324 [doi]  [abs]
  19. Su, P-C; Soliman, EZ; Wu, H-T, Robust T-End Detection via T-End Signal Quality Index and Optimal Shrinkage., Sensors (Basel, Switzerland), vol. 20 no. 24 (December, 2020) [doi]  [abs]
  20. Chang, Z; Chen, Z; Stephen, CD; Schmahmann, JD; Wu, H-T; Sapiro, G; Gupta, AS, Accurate detection of cerebellar smooth pursuit eye movement abnormalities via mobile phone video and machine learning., Scientific Reports, vol. 10 no. 1 (October, 2020), pp. 18641 [doi]  [abs]
  21. Chang, H-C; Wu, H-T; Huang, P-C; Ma, H-P; Lo, Y-L; Huang, Y-H, Portable Sleep Apnea Syndrome Screening and Event Detection Using Long Short-Term Memory Recurrent Neural Network., Sensors (Basel, Switzerland), vol. 20 no. 21 (October, 2020) [doi]  [abs]
  22. Frasch, MG; Lobmaier, SM; Stampalija, T; Desplats, P; Pallarés, ME; Pastor, V; Brocco, MA; Wu, H-T; Schulkin, J; Herry, CL; Seely, AJE; Metz, GAS; Louzoun, Y; Antonelli, MC, Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis., Neuroscience and Biobehavioral Reviews, vol. 117 (October, 2020), pp. 165-183 [doi]  [abs]
  23. Wu, HT, Current state of nonlinear-type time–frequency analysis and applications to high-frequency biomedical signals, Current Opinion in Systems Biology, vol. 23 (October, 2020), pp. 8-21 [doi]  [abs]
  24. Huang, Y-C; Alian, A; Lo, Y-L; Shelley, K; Wu, H-T, Reconsider phase reconstruction in chronobiological research from the modern signal processing perspective (September, 2020) [doi]  [abs]
  25. Chang, C-H; Fang, Y-L; Wang, Y-J; Wu, H-T; Lin, Y-T, Differentiation of skin incision and laparoscopic trocar insertion via quantifying transient bradycardia measured by electrocardiogram., Journal of Clinical Monitoring and Computing, vol. 34 no. 4 (August, 2020), pp. 753-762 [doi]  [abs]
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Wu, Nan

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Xie, Jichun

  1. DiMarco, AV; Qin, X; McKinney, B; Lupo, R; Xie, J; Owzar, K; Alvarez, J, APOBEC mutagenesis as a driver of tumor evolution by promoting tumor recurrence and modulating tumor-immune system interactions in a syngeneic murine model of breast cancer., Cancer Immunology Research, vol. 9 no. 2 (February, 2021)
  2. Mathews, AM; Wysham, NG; Xie, J; Qin, X; Giovacchini, CX; Ekström, M; MacIntyre, NR, Hypercapnia in Advanced Chronic Obstructive Pulmonary Disease: A Secondary Analysis of the National Emphysema Treatment Trial., Chronic Obstructive Pulmonary Diseases, vol. 7 no. 4 (October, 2020), pp. 336-345 [doi]  [abs]
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Zhao, Hongkai

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  2. Zhao, H; Li, J, Scalable Incremental Nonconvex Optimization Approach for Phase Retrieval, Journal of Scientific Computing (2021), Springer (part of Springer Nature)
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  5. Li, J; Zhao, H, Solving phase retrieval via graph projection splitting, Inverse Problems, vol. 36 no. 5 (May, 2020), pp. 055003-055003, IOP Publishing [doi]
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  7. Zhao, H; Lai, R; Xiang, R, Efficient and robust shape correspondence via sparsity-enforced quadratic assignment, Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1 (2020), pp. 9510-9519, IEEE [doi]

Zhong, Yimin

  1. Zhao, H; Zhong, Y, Quantitative PAT with simplified P N approximation, Inverse Problems, vol. 37 no. 5 (May, 2021) [doi]  [abs]
  2. Zhong, Y; Zhao, H, A Fast Algorithm for Time-Dependent Radiative Transport Equation Based on Integral Formulation, Csiam Transactions on Applied Mathematics, vol. 1 no. 2 (June, 2020), pp. 346-364, Global Science Press [doi]
  3. Li, W; Yang, Y; Zhong, Y, Inverse transport problem in fluorescence ultrasound modulated optical tomography with angularly averaged measurements, Inverse Problems, vol. 36 no. 2 (January, 2020) [doi]  [abs]

 

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