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Mathematics Faculty: Publications since January 2021

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]

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, vol. 62 (November, 2021), pp. 22-32 [doi]  [abs]

Aquino, Wilkins

  1. Sanders, C; Bonnet, M; Aquino, W, An adaptive eigenfunction basis strategy to reduce design dimension in topology optimization, International Journal for Numerical Methods in Engineering, vol. 122 no. 24 (December, 2021), pp. 7452-7481 [doi]  [abs]
  2. Hobbs, KT; Choe, N; Aksenov, LI; Reyes, L; Aquino, W; Routh, JC; Hokanson, JA, Machine Learning for Urodynamic Detection of Detrusor Overactivity., Urology (October, 2021) [doi]  [abs]
  3. 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]
  4. 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]

Bendich, Paul L

  1. 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-+

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

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, S.-S. Chern's study of almost-complex structures on the six-sphere, edited by Bryant, R; Cheng, SY; Griffiths, P; Ma, X; Ni, L; Wallach, N, International Journal of Mathematics, vol. 32 no. 12 (November, 2021), World Scientific Publishing [arXiv:1405.3405]  [abs]
  2. Bryant, R; Foulon, P; Ivanov, S; Matveev, VS; Ziller, W, Geodesic behavior for Finsler metrics of constant positive flag curvature on S^2, Journal of Differential Geometry, vol. 117 no. 1 (January, 2021), pp. 1-22 [doi]  [abs]

Cheng, Xiuyuan

  1. Zhao, J; Jaffe, A; Li, H; Lindenbaum, O; Sefik, E; Jackson, R; Cheng, X; Flavell, RA; Kluger, Y, Detection of differentially abundant cell subpopulations in scRNA-seq data., Proceedings of the National Academy of Sciences of the United States of America, vol. 118 no. 22 (June, 2021) [doi]  [abs]
  2. Zhang, Y; Cheng, X; Reeves, G, Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples, 24th International Conference on Artificial Intelligence and Statistics (Aistats), vol. 130 (2021)

Ciocanel, Maria-Veronica

  1. Gandhi, P; Ciocanel, M-V; Niklas, K; Dawes, AT, Identification of approximate symmetries in biological development, Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, vol. 379 no. 2213 (December, 2021), The Royal Society [doi]  [abs]
  2. Smith, CM; Goldrosen, N; Ciocanel, M-V; Santorella, R; Topaz, CM; Sen, S, Racial Disparities in Criminal Sentencing Vary Considerably across Federal Judges (August, 2021), SOCARXIV
  3. Ciocanel, M-V; Chandrasekaran, A; Mager, C; Ni, Q; Papoian, G; Dawes, A, Actin reorganization throughout the cell cycle mediated by motor proteins (July, 2021)  [abs]
  4. Ciocanel, M-V; Juenemann, R; Dawes, AT; McKinley, SA, Topological Data Analysis Approaches to Uncovering the Timing of Ring Structure Onset in Filamentous Networks, Bulletin of Mathematical Biology, vol. 83 no. 3 (March, 2021), Springer Science and Business Media LLC [doi]  [abs]
  5. 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]

Cook, Nicholas A   (search)

  1. Cook, NA; Nguyen, HH, Universality of the minimum modulus for random trigonometric polynomials, Discrete Analysis, vol. 40 (October, 2021), pp. 46 pages, Discrete Analysis  [abs]
  2. Cook, NA; Dembo, A; Pham, HT, Regularity method and large deviation principles for the Erdős--Rényi hypergraph (February, 2021)  [abs]
  3. Cook, N; Hachem, W; Najim, J; Renfrew, D, Non-Hermitian Random Matrices with a Variance Profile (II): Properties and Examples, Journal of Theoretical Probability (January, 2021) [doi]  [abs]

Dasgupta, Samit

  1. Dasgupta, S; Kakde, M, On constant terms of Eisenstein series, Acta Arithmetica, vol. 200 no. 2 (January, 2021), pp. 119-147 [doi]

Daubechies, Ingrid

  1. Fulwood, EL; Shan, S; Winchester, JM; Gao, T; Kirveslahti, H; Daubechies, I; Boyer, DM, Reconstructing dietary ecology of extinct strepsirrhines (Primates, Mammalia) with new approaches for characterizing and analyzing tooth shape, Paleobiology, vol. 47 no. 4 (November, 2021), pp. 612-631 [doi]  [abs]
  2. 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]
  3. Daubechies, I, Wavelets at your service, in The Art And Practice Of Mathematics: Interviews At The Institute For Mathematical Sciences, National University Of Singapore, 2010-2020 (June, 2021), pp. 48-57, ISBN 9789811219580
  4. 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]
  5. 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]
  6. Daubechies, I; DeVore, R; Foucart, S; Hanin, B; Petrova, G, Nonlinear Approximation and (Deep) ReLU Networks, Constructive Approximation (January, 2021) [doi]  [abs]

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]

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. Liu, M; Zhang, Z; Dunson, DB, Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets., Neuroimage, vol. 245 (December, 2021), pp. 118750 [doi]  [abs]
  2. 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]
  3. Moran, KR; Dunson, D; Wheeler, MW; Herring, AH, Bayesian joint Modeling of chemical structure and dose response curves, The Annals of Applied Statistics, vol. 15 no. 3 (September, 2021), pp. 1405-1430 [doi]  [abs]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]

Durrett, Richard T.

  1. Huang, X; Durrett, R, Motion by mean curvature in interacting particle systems, Probability Theory and Related Fields, vol. 181 no. 1-3 (November, 2021), pp. 489-532 [doi]  [abs]
  2. Durrett, RT, Mathematical modeling in ecology, genetics and cancer research, in The Art And Practice Of Mathematics: Interviews At The Institute For Mathematical Sciences, National University Of Singapore, 2010-2020 (June, 2021), pp. 74-82, ISBN 9789811219580
  3. 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]
  4. Agarwal, P; Simper, M; Durrett, R, The q-voter model on the torus, Electronic Journal of Probability, vol. 26 (January, 2021) [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]

Faigenbaum-Golovin, Shira

  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. Daubechies, I; DeVore, RA; Dym, N; Faigenbaum-Golovin, S; Kovalsky, SZ; Lin, K-C; Park, J; Petrova, G; Sober, B, Neural Network Approximation of Refinable Functions., Corr, vol. abs/2107.13191 (2021)

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]

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]

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]

Goldberg, Amy

  1. Ai, H; Zhang, M; Yang, B; Goldberg, A; Li, W; Ma, J; Brandt, D; Zhang, Z; Nielsen, R; Huang, L, Human-Mediated Admixture and Selection Shape the Diversity on the Modern Swine (Sus scrofa) Y Chromosomes., Molecular Biology and Evolution, vol. 38 no. 11 (October, 2021), pp. 5051-5065 [doi]  [abs]
  2. 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]
  3. Korunes, KL; Goldberg, A, Human genetic admixture., Plos Genetics, vol. 17 no. 3 (March, 2021), pp. e1009374 [doi]  [abs]
  4. 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]
  5. 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]

Hahn, Heekyoung

  1. Hahn, H, Poles of triple product L-functions involving monomial representations, International Journal of Number Theory, vol. 17 no. 02 (March, 2021), pp. 479-486, World Scientific Publishing [doi]  [abs]

Hain, Richard   (search)

  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
  2. Hain, R, Hodge theory of the Turaev cobracket and the Kashiwara-Vergne problem, Journal of the European Mathematical Society, vol. 23 no. 12 (January, 2021), pp. 3889-3933 [doi]  [abs]
  3. Hain, R, Johnson homomorphisms, Ems Surveys in Mathematical Sciences, vol. 7 no. 1 (January, 2021), pp. 33-116 [doi]  [abs]

Haskins, Mark

  1. Haskins, M; Nordström, J, Cohomogeneity-one solitons in Laplacian flow: local, smoothly-closing and steady solitons (December, 2021)  [abs]
  2. FOSCOLO, L; HASKINS, M; NORDSTRÖM, J, Complete noncompact g2-manifolds from asymptotically conical calabi-yau 3-folds, Duke Mathematical Journal, vol. 170 no. 15 (October, 2021), pp. 3323-3416 [doi]  [abs]
  3. 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, Enhanced dissipation, hypoellipticity for passive scalar equations with fractional dissipation, Journal of Functional Analysis, vol. 282 no. 3 (February, 2022) [doi]  [abs]
  2. 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]
  3. Gong, Y; He, S; Kiselev, A, Random search in fluid flow aided by chemotaxis (July, 2021)  [abs]
  4. 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]
  5. 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]
  6. 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]

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]

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. Autry, EA; Carter, D; Herschlag, GJ; Hunter, Z; Mattingly, JC, Metropolized Multiscale Forest Recombination for Redistricting, Multiscale Modeling & Simulation, vol. 19 no. 4 (January, 2021), pp. 1885-1914, Society for Industrial & Applied Mathematics (SIAM) [doi]

Kim, Woojin

  1. Kim, W; Mémoli, F, Generalized persistence diagrams for persistence modules over posets, Journal of Applied and Computational Topology, vol. 5 no. 4 (December, 2021), pp. 533-581, Springer Science and Business Media LLC [doi]
  2. Dey, TK; Kim, W; Mémoli, F, Computing Generalized Rank Invariant for 2-Parameter Persistence Modules via Zigzag Persistence and its Applications (November, 2021)  [abs]
  3. Kim, W; Moore, S, The Generalized Persistence Diagram Encodes the Bigraded Betti Numbers (November, 2021)  [abs]
  4. 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]
  5. 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]

Kiselev, Alexander A.

  1. Kiselev, A; Luo, X, On nonexistence of splash singularities for the $α$-SQG patches (November, 2021)  [abs]
  2. 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]
  3. Gong, Y; He, S; Kiselev, A, Random search in fluid flow aided by chemotaxis (July, 2021)  [abs]
  4. Gong, Y; Kiselev, A, Chemotactic Reaction Enhancement in One Dimension (March, 2021)  [abs]
  5. Kiselev, A; Yao, Y, Small scale formations in the incompressible porous media equation (February, 2021)  [abs]
  6. 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]

Lee, Holden

  1. Helmuth, T; Lee, H; Perkins, W; Ravichandran, M; Wu, Q, Approximation algorithms for the random-field Ising model (August, 2021)  [abs]
  2. Lee, H; Pabbaraju, C; Sevekari, A; Risteski, A, Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows (July, 2021)  [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]

Li, Bowen

  1. Li, B; Zou, J, An adaptive edge element method and its convergence for an electromagnetic constrained optimal control problem, Esaim: Mathematical Modelling and Numerical Analysis, vol. 55 no. 5 (September, 2021), pp. 2013-2044, E D P SCIENCES [doi]  [abs]
  2. H. Ammari, B. Li, J. Zou, Mathematical analysis of electromagnetic scattering by dielectric nanopar-ticles with high refractive indices, Trans. Amer. Math. Soc. (2021) [arXiv:2003.10223]
  3. B. Li, J. Zou, On a general matrix valued unbalanced optimal transport and its fully discretization: dynamic formulation and convergence framework (2021) [arXiv:2011.05845]

Liu, Jian-Guo

  1. Liu, JG; Zhang, Z, EXISTENCE of GLOBAL WEAK SOLUTIONS of p-NAVIER-STOKES EQUATIONS, Discrete and Continuous Dynamical Systems Series B, vol. 27 no. 1 (January, 2022), pp. 469-486, American Institute of Mathematical Sciences (AIMS) [doi]  [abs]
  2. Gao, Y; Liu, JG, Surfactant-dependent contact line dynamics and droplet spreading on textured substrates: Derivations and computations, Physica D: Nonlinear Phenomena, vol. 428 (December, 2021) [doi]  [abs] [reputed journal]
  3. Gao, Y; Liu, JG; Liu, Z, Existence and rigidity of the vectorial peierls-nabarro model for dislocations in high dimensions, Nonlinearity, vol. 34 no. 11 (November, 2021), pp. 7778-7828 [doi]  [abs] [high impact paper]
  4. 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., Medical Physics, vol. 48 no. 7 (July, 2021), pp. 3767-3777 [doi]  [abs] [reputed journal]
  5. 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] [reputed journal]
  6. 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] [reputed journal]
  7. 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] [reputed journal]
  8. 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]
  9. 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] [reputed journal]
  10. 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] [reputed journal]

Lu, Jianfeng

  1. Lu, J; Murphey, C; Steinerberger, S, Fast Localization of Eigenfunctions via Smoothed Potentials, Journal of Scientific Computing, vol. 90 no. 1 (January, 2022) [doi]  [abs]
  2. Lu, J; Otto, F, Optimal Artificial Boundary Condition for Random Elliptic Media, Foundations of Computational Mathematics, vol. 21 no. 6 (December, 2021), pp. 1643-1702 [doi]  [abs]
  3. Chen, K; Chen, S; Li, Q; Lu, J; Wright, SJ, Low-rank approximation for multiscale PDEs (November, 2021)  [abs]
  4. Huang, H; Landsberg, JM; Lu, J, Geometry of backflow transformation ansatz for quantum many-body fermionic wavefunctions (November, 2021)  [abs]
  5. 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]
  6. Lu, J; Otto, F; Wang, L, Optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media (September, 2021)  [abs]
  7. 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]
  8. 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]
  9. Barthel, T; Lu, J; Friesecke, G, On the closedness and geometry of tensor network state sets, Arxiv:2108.00031 (July, 2021)  [abs]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. Cao, Y; Lu, J, Structure-preserving numerical schemes for Lindblad equations (March, 2021)  [abs]
  16. 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]
  17. Lu, J; Stubbs, KD, Algebraic localization implies exponential localization in non-periodic insulators (January, 2021)  [abs]
  18. Lu, J; Lu, Y; Wang, M, A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations (January, 2021)  [abs]
  19. Chen, Z; Li, Y; Lu, J, On the global convergence of randomized coordinate gradient descent for non-convex optimization (January, 2021)  [abs]
  20. 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 (January, 2021), pp. 1549-1577, International Press of Boston [doi]  [abs]
  21. Zhou, M; Han, J; Lu, J, Actor-Critic Method for High Dimensional Static Hamilton--Jacobi--Bellman Partial Differential Equations based on Neural Networks, Siam Journal on Scientific Computing, vol. 43 no. 6 (January, 2021), pp. A4043-A4066, Society for Industrial & Applied Mathematics (SIAM) [doi]
  22. Lu, J; Shen, Z; Yang, H; Zhang, S, Deep Network Approximation for Smooth Functions, Siam Journal on Mathematical Analysis, vol. 53 no. 5 (January, 2021), pp. 5465-5506, Society for Industrial & Applied Mathematics (SIAM) [doi]
  23. 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]
  24. Loring, TA; Lu, J; Watson, AB, Locality of the windowed local density of states (January, 2021)  [abs]
  25. 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]
  26. 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]
  27. 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)

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, Anomalous dissipation, anomalous work, and energy balance for the navier-stokes equations, Siam Journal on Mathematical Analysis, vol. 53 no. 4 (January, 2021), pp. 3856-3887 [doi]  [abs]

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. Herzog, DP; Mattingly, JC; Nguyen, HD, Gibbsian dynamics and the generalized Langevin equation (November, 2021)  [abs]
  2. Mattingly, JC; Romito, M; Su, L, The Gaussian Structure of the Singular Stochastic Burgers Equation (April, 2021)  [abs]
  3. 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 (January, 2021), pp. 1549-1577, International Press of Boston [doi]  [abs]
  4. Autry, EA; Carter, D; Herschlag, GJ; Hunter, Z; Mattingly, JC, Metropolized Multiscale Forest Recombination for Redistricting, Multiscale Modeling & Simulation, vol. 19 no. 4 (January, 2021), pp. 1885-1914, Society for Industrial & Applied Mathematics (SIAM) [doi]
  5. Bakhtin, Y; Hurth, T; Lawley, SD; Mattingly, JC, Singularities of invariant densities for random switching between two linear ODEs in 2D, Siam Journal on Applied Dynamical Systems, vol. 20 no. 4 (January, 2021), pp. 1917-1958 [doi]  [abs]
  6. 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]

Miller, Ezra

  1. Miller, E, Stratifications of real vector spaces from constructible sheaves with conical microsupport, Journal of Applied and Computational Topology (2021), SPRINGER

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. Thomas, BS; You, K; Lin, L; Lim, LH; Mukherjee, S, Learning Subspaces of Different Dimensions, Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America (January, 2021) [doi]  [abs]
  8. Li, W; Hannig, J; Mukherjee, S, Subspace clustering through sub-clusters, Journal of Machine Learning Research, vol. 22 (January, 2021)  [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, vol. 20 (December, 2021), pp. 100350-100350, Elsevier BV [doi]

Ng, Lenhard L.

  1. Casals, R; Ng, L, Braid Loops with infinite monodromy on the Legendrian contact DGA (January, 2021)  [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. Rengaswamy, N; Seshadreesan, KP; Guha, S; Pfister, H, A Belief Propagation-based Quantum Joint-Detection Receiver for Superadditive Optical Communications, 2021 Conference on Lasers and Electro Optics, Cleo 2021 Proceedings (May, 2021), ISBN 9781943580910  [abs]
  7. 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]
  8. Rengaswamy, N; Seshadreesan, KP; Guha, S; Pfister, H, A belief propagation-based quantum joint-detection receiver for superadditive optical communications, Optics Infobase Conference Papers (January, 2021), ISBN 9781557528209  [abs]
  9. 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]
  10. Tal, I; Pfister, HD; Fazeli, A; Vardy, A, Polar Codes for the Deletion Channel: Weak and Strong Polarization, Ieee Transactions on Information Theory (January, 2021) [doi]  [abs]
  11. 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]

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, L; Bucur, A; Cojocaru, A; Lalin, M, Geometric generalizations of the square sieve, with an application to cyclic covers (2021)
  5. Pierce, L, Counting problems: class groups, primes, and number fields, ICM 2022 Proceedings (accepted, in press) (2021)
  6. Pierce, L; Gressman, P; Guo, S; Roos, J; Yung, P-L, On the strict majorant property in arbitrary dimensions (2021)
  7. 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]
  8. An, C; Chu, R; Pierce, LB, Counterexamples for high-degree generalizations of the Schrödinger maximal operator (2021)  [abs]

Plesser, M. Ronen

  1. Marco Bertolini, Ilarion V. Melnikov, M. Ronen Plesser, Fixed points of (0,2) Landau-Ginzburg renormalization group flows and the chiral algebra (June, 2021) [2106.00105]  [abs]

Porter, Curtis W.

  1. Porter, C; Zelenko, I, Absolute parallelism for 2-nondegenerate CR structures via bigraded Tanaka prolongation, Journal Fur Die Reine Und Angewandte Mathematik, vol. 2021 no. 777 (August, 2021), pp. 195-250 [doi]  [abs]
  2. Porter, C, 3-folds CR-embedded in 5-dimensional real hyperquadrics, Journal of Geometry and Physics, vol. 163 (May, 2021) [doi]  [abs]
  3. Porter, C, Unit Tangent Bundles, CR Leaf Spaces, and Hypercomplex Structures (February, 2021)  [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. Bardhan, J; Leung, MA; Martin, E; Randles, A, DOE Computational Science Graduate Fellowship Research Showcase, Computing in Science & Engineering, vol. 23 no. 6 (January, 2021), pp. 5-8 [doi]

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

Robles, Colleen M

  1. Green, ML; Griffiths, P; Robles, C, Completions of period mappings: progress report (June, 2021)  [abs]
  2. Green, M; Griffiths, P; Robles, C, Natural line bundles on completions of period mappings (February, 2021)  [abs]
  3. 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]

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]

Rudin, Cynthia D.

  1. 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, vol. 152 (January, 2022) [doi]  [abs]
  2. Guo, Z; Ding, C; Hu, X; Rudin, C, A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables., Physiological Measurement, vol. 42 no. 12 (December, 2021) [doi]  [abs]
  3. Barnett, AJ; Schwartz, FR; Tao, C; Chen, C; Ren, Y; Lo, JY; Rudin, C, A case-based interpretable deep learning model for classification of mass lesions in digital mammography, Nature Machine Intelligence, vol. 3 no. 12 (December, 2021), pp. 1061-1070 [doi]  [abs]
  4. Coker, B; Rudin, C; King, G, A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results, Management Science, vol. 67 no. 10 (October, 2021), pp. 6174-6197 [doi]  [abs]
  5. 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]
  6. Wang, J; Zhang, X; Zhou, Y; Suh, C; Rudin, C, There once was a really bad poet, it was automated but you didn’t know it, Transactions of the Association for Computational Linguistics, vol. 9 (July, 2021), pp. 605-620 [doi]  [abs]
  7. Wang, Y; Huang, H; Rudin, C; Shaposhnik, Y, Understanding how dimension reduction tools work: An empirical approach to deciphering T-SNE, UMAP, TriMap, and PaCMAP for data visualization, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  8. Traca, S; Rudin, C; Yan, W, Regulating greed over time in multi-armed bandits, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  9. 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]
  10. Gupta, NR; Orlandi, V; Chang, C-R; Wang, T; Morucci, M; Dey, P; Howell, TJ; Sun, X; Ghosal, A; Roy, S; Rudin, C; Volfovsky, A, dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference., Corr, vol. abs/2101.01867 (2021)
  11. 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., J. Mach. Learn. Res., vol. 22 (2021), pp. 31:1-31:1  [abs]

Ryser, Marc D.

  1. 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, vol. 125 no. 10 (November, 2021), pp. 1443-1449 [doi]  [abs]
  2. 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 (September, 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

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. Major, S; Isaev, D; Grapel, J; Calnan, T; Tenenbaum, E; Carpenter, K; Franz, L; Howard, J; Vermeer, S; Sapiro, G; Murias, M; Dawson, G, Shorter average look durations to dynamic social stimuli are associated with higher levels of autism symptoms in young autistic children., Autism (December, 2021), pp. 13623613211056427 [doi]  [abs]
  2. Kim, YK; Di Martino, JM; Nicholas, J; Rivera-Cancel, A; Wildes, JE; Marcus, MD; Sapiro, G; Zucker, N, Parent strategies for expanding food variety: Reflections of 19,239 adults with symptoms of Avoidant/Restrictive Food Intake Disorder., The International Journal of Eating Disorders (November, 2021) [doi]  [abs]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]

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]

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. Wagner, A; Solomon, E; Bendich, P, Improving Metric Dimensionality Reduction with Distributed Topology (June, 2021)  [abs]
  3. Solomon, E; Wagner, A; Bendich, P, From Geometry to Topology: Inverse Theorems for Distributed Persistence (January, 2021)  [abs]
  4. 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

Stern, Mark A.

  1. Cherkis, SA; Larrain-Hubach, A; Stern, M, Instantons on multi-Taub-NUT Spaces I: Asymptotic Form and Index Theorem, Journal of Differential Geometry, vol. 119 no. 1 (December, 2021), pp. 1-72, International Press  [abs]
  2. Cherkis, S; Larraín-Hubach, A; Stern, M, Instantons on multi-Taub-NUT Spaces II: Bow Construction (March, 2021)  [abs]

Tarokh, Vahid

  1. Xu, X; Hasan, A; Elkhalil, K; Ding, J; Tarokh, V, Characteristic Neural Ordinary Differential Equations (November, 2021)  [abs]
  2. Dong, J; Ren, S; Deng, Y; Khatib, O; Malof, J; Soltani, M; Padilla, W; Tarokh, V, Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions (November, 2021)  [abs]
  3. Diao, E; Tarokh, V; Ding, J, Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders (October, 2021)  [abs]
  4. Le, CP; Dong, J; Soltani, M; Tarokh, V, Task Affinity with Maximum Bipartite Matching in Few-Shot Learning (October, 2021)  [abs]
  5. 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]
  6. 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]
  7. 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]
  8. 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]
  9. Momenifar, M; Diao, E; Tarokh, V; Bragg, AD, Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers (March, 2021)  [abs]
  10. Zhou, J; Ding, J; Tan, KM; Tarokh, V, Model linkage selection for cooperative learning, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  11. Hasan, A; Pereira, JM; Farsiu, S; Tarokh, V, Identifying Latent Stochastic Differential Equations, Ieee Transactions on Signal Processing, vol. abs/2007.06075 (January, 2021) [doi]  [abs]
  12. 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]
  13. 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]
  14. 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]
  15. Le, CP; Soltani, M; Ravier, RJ; Tarokh, V, Task-Aware Neural Architecture Search., Icassp (2021), pp. 4090-4094, IEEE, ISBN 978-1-7281-7606-2
  16. 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)
  17. Le, CP; Soltani, M; Ravier, RJ; Tarokh, V, Neural Architecture Search From Task Similarity Measure., Corr, vol. abs/2103.00241 (2021)
  18. 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)
  19. 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)
  20. Ding, J; Diao, E; Zhou, J; Tarokh, V, On Statistical Efficiency in Learning., Ieee Trans. Inf. Theory, vol. 67 (2021), pp. 2488-2506 [doi]
  21. Ng, Y; Hasan, A; Elkhalil, K; Tarokh, V, Generative Archimedean Copulas., Corr, vol. abs/2102.11351 (2021)
  22. Cannella, C; Tarokh, V, Semi-Empirical Objective Functions for MCMC Proposal Optimization., Corr, vol. abs/2106.02104 (2021)
  23. Diao, E; Ding, J; Tarokh, V, Gradient Assisted Learning., Corr, vol. abs/2106.01425 (2021)
  24. Yanchenko, AK; Soltani, M; Ravier, RJ; Mukherjee, S; Tarokh, V, Towards Explainable Convolutional Features for Music Audio Modeling., Corr, vol. abs/2106.00110 (2021)
  25. Cannella, C; Soltani, M; Tarokh, V, Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows., Iclr (2021), OpenReview.net

Venakides, Stephanos

  1. Komineas, S; Melcher, C; Venakides, S, Chiral skyrmions of large radius, Physica D: Nonlinear Phenomena, vol. 418 (April, 2021), Elsevier [doi]  [abs]

Wagner, Alexander Y

  1. Wagner, A; Solomon, E; Bendich, P, Improving Metric Dimensionality Reduction with Distributed Topology (June, 2021)  [abs]
  2. 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]
  3. Solomon, E; Wagner, A; Bendich, P, From Geometry to Topology: Inverse Theorems for Distributed Persistence (January, 2021)  [abs]
  4. 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-+

Wang, Min

  1. Dahmen, W; Wang, M; Wang, Z, Nonlinear Reduced DNN Models for State Estimation (October, 2021)  [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. 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]
  3. 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]
  4. 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]

Witelski, Thomas P.   (search)

  1. Kim, R; Witelski, T, Uncovering the dynamics of a circadian-dopamine model influenced by the light-dark cycle, Mathematical Biosciences, vol. 344 (December, 2021), Elsevier [doi]
  2. 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]
  3. 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]

Wu, Hau-Tieng

  1. Chiu, NT; Huwiler, S; Ferster, ML; Karlen, W; Wu, HT; Lustenberger, C, Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG, Biomedical Signal Processing and Control, vol. 72 (February, 2022) [doi]  [abs]
  2. Chen, Z; Wu, H-T, Disentangling modes with crossover instantaneous frequencies by synchrosqueezed chirplet transforms, from theory to application (December, 2021)  [abs]
  3. Steinerberger, S; Wu, H-T, Fundamental component enhancement via adaptive nonlinear activation functions (December, 2021)  [abs]
  4. Ding, X; Wu, H-T, How do kernel-based sensor fusion algorithms behave under high dimensional noise? (November, 2021)  [abs]
  5. Chen, HY; Malik, J; Wu, HT; Wang, CL, Is the median hourly ambulatory heart rate range helpful in stratifying mortality risk among newly diagnosed atrial fibrillation patients?, Journal of Personalized Medicine, vol. 11 no. 11 (November, 2021) [doi]  [abs]
  6. 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]
  7. 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]
  8. 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, vol. 19 no. 5 (September, 2021) [doi]  [abs]
  9. 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]
  10. 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]
  11. Steinerberger, S; Wu, HT, On Zeroes of Random Polynomials and an Application to Unwinding, International Mathematics Research Notices, vol. 2021 no. 13 (July, 2021), pp. 10100-10117, Oxford University Press (OUP) [doi]  [abs]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. Huang, WK; Chung, YM; Wang, YB; Mandel, JE; Wu, HT, Airflow recovery from thoracic and abdominal movements using synchrosqueezing transform and locally stationary Gaussian process regression, Computational Statistics & Data Analysis (January, 2021), pp. 107384-107384, Elsevier BV [doi]  [abs]
  19. 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]
  20. 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]
  21. 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]
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Wu, Nan

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Xie, Jichun

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  2. 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)

Zhang, Ruda

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Zhao, Hongkai

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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. Li, W; Schotland, JC; Yang, Y; Zhong, Y, An Acousto-electric Inverse Source Problem, Siam Journal on Imaging Sciences, vol. 14 no. 4 (January, 2021), pp. 1601-1616, Society for Industrial & Applied Mathematics (SIAM) [doi]
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