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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; Chang, HC; Raghvendra, S; Xiao, A, Deterministic, near-linear-approximation algorithm for geometric bipartite matching, Proceedings of the Annual Acm Symposium on Theory of Computing (September, 2022), pp. 1052-1065, ISBN 9781450392648 [doi]  [abs]
  2. Hu, X; Liu, Y; Xiu, H; Agarwal, PK; Panigrahi, D; Roy, S; Yang, J, Selectivity Functions of Range Queries are Learnable, Proceedings of the Acm Sigmod International Conference on Management of Data (June, 2022), pp. 959-972, ISBN 9781450392495 [doi]  [abs]
  3. Hu, X; Sintos, S; Gao, J; Agarwal, PK; Yang, J, Computing Complex Temporal Join Queries Efficiently, Proceedings of the Acm Sigmod International Conference on Management of Data (June, 2022), pp. 2076-2090, ISBN 9781450392495 [doi]  [abs]
  4. Agarwal, PK; Aronov, B; Ezra, E; Katz, MJ; Sharir, M, Intersection Queries for Flat Semi-Algebraic Objects in Three Dimensions and Related Problems, Leibniz International Proceedings in Informatics, Lipics, vol. 224 (June, 2022), ISBN 9783959772273 [doi]  [abs]
  5. Agarwal, PK; Raghvendra, S; Shirzadian, P; Sowle, R, An Improved ϵ-Approximation Algorithm for Geometric Bipartite Matching, Leibniz International Proceedings in Informatics, Lipics, vol. 227 (June, 2022), ISBN 9783959772365 [doi]  [abs]
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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. Capriotti, M; Roy, T; Hugenberg, NR; Harrigan, H; Lee, H-C; Aquino, W; Guddati, M; Greenleaf, JF; Urban, MW, The influence of acoustic radiation force beam shape and location on wave spectral content for arterial dispersion ultrasound vibrometry., Physics in Medicine and Biology, vol. 67 no. 13 (June, 2022) [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, vol. 159 (January, 2022), pp. 247-254 [doi]  [abs]
  3. 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]
  4. Hugenberg, NR; Roy, T; Harrigan, H; Capriotti, M; Lee, H-K; Guddati, M; Greenleaf, JF; Urban, MW; Aquino, W, Toward improved accuracy in shear wave elastography of arteries through controlling the arterial response to ultrasound perturbation in-silico and in phantoms., Physics in Medicine and Biology, vol. 66 no. 23 (November, 2021) [doi]  [abs]
  5. 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]
  6. 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]

Arapura, Donu V.

  1. Arapura, D, HODGE CYCLES AND THE LERAY FILTRATION, Pacific Journal of Mathematics, vol. 319 no. 2 (January, 2022), pp. 233-258 [doi]  [abs]
  2. Arapura, D, Motivic sheaves revisited, Journal of Pure and Applied Algebra (January, 2022) [doi]  [abs]

Arcila-Maya, Niny J.

  1. Arcila-Maya, N; Bethea, C; Opie, M; Wickelgren, K; Zakharevich, I, Compactly supported A1-Euler characteristic and the Hochschild complex, Topology and Its Applications, vol. 316 (July, 2022) [doi]  [abs]
  2. Arcila-Maya, N, Decomposition of Topological Azumaya Algebras (June, 2021) [doi]

Autry, Eric A.

  1. 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 [doi]  [abs]

Bendich, Paul L

  1. Solomon, E; Wagner, A; Bendich, P, From Geometry to Topology: Inverse Theorems for Distributed Persistence, Leibniz International Proceedings in Informatics, Lipics, vol. 224 (June, 2022), ISBN 9783959772273 [doi]  [abs]
  2. Smith, A; Bendich, P; Harer, J, Persistent obstruction theory for a model category of measures with applications to data merging, Transactions of the American Mathematical Society, Series B, vol. 8 no. 1 (February, 2021), pp. 1-38, American Mathematical Society (AMS) [doi]  [abs]
  3. 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), pp. e2100293118 [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. Ciocanel, M-V, Applications of PDEs and Stochastic Modeling to Protein Transport in Cell Biology (July, 2022)
  2. Benson, J; Bessonov, M; Burke, K; Cassani, S; Ciocanel, M-V; Cooney, DB; Volkening, A, How do classroom-turnover times depend on lecture-hall size? (June, 2022)
  3. Ciocanel, M-V; Chandrasekaran, A; Mager, C; Ni, Q; Papoian, GA; Dawes, A, Simulated actin reorganization mediated by motor proteins., Plos Computational Biology, vol. 18 no. 4 (April, 2022), pp. e1010026 [doi]  [abs]
  4. 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]
  5. 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
  6. 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]
  7. 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]
  8. 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; Yakir, O; Zeitouni, O, Universality of Poisson Limits for Moduli of Roots of Kac Polynomials, International Mathematics Research Notices (March, 2022), Oxford University Press (OUP) [doi]  [abs]
  2. 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]
  3. Cook, NA; Nguyen, HH; Yakir, O; Zeitouni, O, Universality of Poisson limits for moduli of roots of Kac polynomials (May, 2021)  [abs]
  4. Cook, NA; Dembo, A; Pham, HT, Regularity method and large deviation principles for the Erdős--Rényi hypergraph (February, 2021)  [abs]
  5. 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]
  6. Cook, NA; Nguyen, HH, Universality of the minimum modulus for random trigonometric polynomials, Discrete Analysis, vol. 2021 (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. Daubechies, I; DeVore, R; Foucart, S; Hanin, B; Petrova, G, Nonlinear Approximation and (Deep) ReLU Networks, Constructive Approximation, vol. 55 no. 1 (February, 2022), pp. 127-172 [doi]  [abs]
  2. Pu, W; Huang, J-J; Sober, B; Daly, N; Higgitt, C; Daubechies, I; Dragotti, PL; Rodrigues, MRD, Mixed X-Ray Image Separation for Artworks With Concealed Designs., Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society, vol. 31 (January, 2022), pp. 4458-4473 [doi]  [abs]
  3. Daubechies, I; DeVore, R; Dym, N; Faigenbaum-Golovin, S; Kovalsky, SZ; Lin, KC; Park, J; Petrova, G; Sober, B, Neural Network Approximation of Refinable Functions, Ieee Transactions on Information Theory (January, 2022) [doi]  [abs]
  4. 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]
  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. 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
  7. 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]
  8. 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]
  9. Pu, W; Huang, J; Sober, B; Daly, N; Higgitt, C; Dragotti, PL; Daubechies, I; Rodrigues, MRD, A Learning Based Approach to Separate Mixed X-Ray Images Associated with Artwork with Concealed Designs, European Signal Processing Conference, vol. 2021-August (January, 2021), pp. 1491-1495, ISBN 9789082797060 [doi]  [abs]

Dolbow, John E.

  1. Hu, T; Dolbow, JE; Yosibash, Z, Towards validation of crack nucleation criteria from V-notches in quasi-brittle metallic alloys: Energetics or strength?, Computer Methods in Applied Mechanics and Engineering (January, 2022) [doi]  [abs]
  2. 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]
  3. 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]
  4. 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. Chakraborty, A; Ovaskainen, O; Dunson, DB, BAYESIAN SEMIPARAMETRIC LONG MEMORY MODELS FOR DISCRETIZED EVENT DATA, The Annals of Applied Statistics, vol. 16 no. 3 (September, 2022), pp. 1380-1399 [doi]  [abs]
  2. Guha, S; Jung, R; Dunson, D, Predicting phenotypes from brain connection structure, Journal of the Royal Statistical Society. Series C, Applied Statistics, vol. 71 no. 3 (June, 2022), pp. 639-668 [doi]  [abs]
  3. Aliverti, E; Dunson, DB, COMPOSITE MIXTURE OF LOG-LINEAR MODELS WITH APPLICATION TO PSYCHIATRIC STUDIES., The Annals of Applied Statistics, vol. 16 no. 2 (June, 2022), pp. 765-790 [doi]  [abs]
  4. Dey, P; Zhang, Z; Dunson, DB, Outlier Detection for Multi-Network Data., Bioinformatics (Oxford, England) (June, 2022), pp. btac431 [doi]  [abs]
  5. Dunson, DB; Wu, HT; Wu, N, Graph based Gaussian processes on restricted domains, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 84 no. 2 (April, 2022), pp. 414-439 [doi]  [abs]
  6. Lum, K; Dunson, DB; Johndrow, J, Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment, Journal of the Royal Statistical Society: Series a (Statistics in Society), vol. 185 no. 2 (April, 2022), pp. 588-614 [doi]  [abs]
  7. Van Den Boom, W; Reeves, G; Dunson, DB, Erratum: Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation (Biometrika (2021) 108 (269-282) DOI: 10.1093/biomet/asaa068), Biometrika, vol. 109 no. 1 (March, 2022), pp. 275 [doi]  [abs]
  8. Russo, M; Singer, BH; Dunson, DB, MULTIVARIATE MIXED MEMBERSHIP MODELING: INFERRING DOMAIN-SPECIFIC RISK PROFILES., The Annals of Applied Statistics, vol. 16 no. 1 (March, 2022), pp. 391-413 [doi]  [abs]
  9. Joubert, BR; Kioumourtzoglou, M-A; Chamberlain, T; Chen, HY; Gennings, C; Turyk, ME; Miranda, ML; Webster, TF; Ensor, KB; Dunson, DB; Coull, BA, Powering Research through Innovative Methods for Mixtures in Epidemiology (PRIME) Program: Novel and Expanded Statistical Methods., International Journal of Environmental Research and Public Health, vol. 19 no. 3 (January, 2022), pp. 1378 [doi]  [abs]
  10. Peruzzi, M; Dunson, DB, Spatial Multivariate Trees for Big Data Bayesian Regression., Journal of Machine Learning Research, vol. 23 (January, 2022), pp. 17  [abs]
  11. Zhang, R; Mak, S; Dunson, D, GAUSSIAN PROCESS SUBSPACE PREDICTION FOR MODEL REDUCTION, Siam Journal on Scientific Computing, vol. 44 no. 3 (January, 2022), pp. A1428-A1449 [doi]  [abs]
  12. Zito, A; Rigon, T; Ovaskainen, O; Dunson, DB, Bayesian Modeling of Sequential Discoveries, Journal of the American Statistical Association (January, 2022) [doi]  [abs]
  13. Badea, A; Li, D; Niculescu, AR; Anderson, RJ; Stout, JA; Williams, CL; Colton, CA; Maeda, N; Dunson, DB, Absolute Winding Number Differentiates Mouse Spatial Navigation Strategies With Genetic Risk for Alzheimer's Disease., Frontiers in Neuroscience, vol. 16 (2022), pp. 848654 [doi]  [abs]
  14. 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]
  15. 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]
  16. Roy, A; Lavine, I; Herring, AH; Dunson, DB, Perturbed factor analysis: Accounting for group differences in exposure profiles, The Annals of Applied Statistics, vol. 15 no. 3 (September, 2021), pp. 1386-1404 [doi]  [abs]
  17. 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]
  18. 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]
  19. 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]
  20. VAN DEN Boom, W; Reeves, G; Dunson, DB, Approximating posteriors with high-dimensional nuisance parameters via integrated rotated Gaussian approximation., Biometrika, vol. 108 no. 2 (June, 2021), pp. 269-282 [doi]  [abs]
  21. Paganin, S; Herring, AH; Olshan, AF; Dunson, DB; National Birth Defects Prevention Study,, Centered Partition Processes: Informative Priors for Clustering (with Discussion)., Bayesian Analysis, vol. 16 no. 1 (March, 2021), pp. 301-370 [doi]  [abs]
  22. 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]
  23. 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]
  24. 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]
  25. Roy, A; Borg, JS; Dunson, DB, Bayesian time-aligned factor analysis of paired multivariate time series., Journal of Machine Learning Research, vol. 22 (January, 2021), pp. 250  [abs]
  26. Papadogeorgou, G; Zhang, Z; Dunson, DB, Soft tensor regression, Journal of Machine Learning Research, vol. 22 (January, 2021), pp. 1-53  [abs]
  27. Duan, LL; Dunson, DB, Bayesian Distance Clustering., Journal of Machine Learning Research, vol. 22 (January, 2021), pp. 224  [abs]
  28. Zhu, Y; Li, C; Dunson, DB, Classification Trees for Imbalanced Data: Surface-to-Volume Regularization, Journal of the American Statistical Association (January, 2021) [doi]  [abs]

Durrett, Richard T.

  1. Durrett, R; Yao, D, Susceptible–infected epidemics on evolving graphs, Electronic Journal of Probability, vol. 27 (January, 2022), pp. 1-66 [doi]  [abs]
  2. 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]
  3. 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
  4. 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]
  5. 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]

Elgindi, Tarek M

  1. Drivas, TD; Elgindi, TM; Iyer, G; Jeong, IJ, Anomalous Dissipation in Passive Scalar Transport, Archive for Rational Mechanics and Analysis, vol. 243 no. 3 (March, 2022), pp. 1151-1180 [doi]  [abs]
  2. Constantin, P; Drivas, TD; Elgindi, TM, Inviscid Limit of Vorticity Distributions in the Yudovich Class, Communications on Pure and Applied Mathematics, vol. 75 no. 1 (January, 2022), pp. 60-82 [doi]  [abs]
  3. Elgindi, TM; Jeong, IJ, The incompressible Euler equations under octahedral symmetry: Singularity formation in a fundamental domain, Advances in Mathematics, vol. 393 (December, 2021) [doi]  [abs]
  4. Elgindi, TM, Finite-time singularity formation for $C^{1,\alpha}$ solutions to the incompressible Euler equations on $\mathbb{R}^3$, Annals of Mathematics, vol. 194 no. 3 (November, 2021), Annals of Mathematics [doi]
  5. Elgindi, T; Ibrahim, S; Shen, S, Finite-time singularity formation for an active scalar equation, Nonlinearity, vol. 34 no. 7 (July, 2021), pp. 5045-5069 [doi]  [abs]

Faigenbaum-Golovin, Shira

  1. Daubechies, I; DeVore, R; Dym, N; Faigenbaum-Golovin, S; Kovalsky, SZ; Lin, K-C; Park, J; Petrova, G; Sober, B, Neural Network Approximation of Refinable Functions, vol. abs/2107.13191 (July, 2021)  [abs]
  2. 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]

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. Frandsen, A; Ge, R, Optimization landscape of Tucker decomposition, Mathematical Programming, vol. 193 no. 2 (June, 2022), pp. 687-712 [doi]  [abs]
  2. Ge, R; Ma, T, On the optimization landscape of tensor decompositions, Mathematical Programming, vol. 193 no. 2 (June, 2022), pp. 713-759 [doi]  [abs]
  3. Azar, Y; Ganesh, A; Ge, R; Panigrahi, D, Online Service with Delay, Acm Transactions on Algorithms, vol. 17 no. 3 (August, 2021) [doi]  [abs]
  4. 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]
  5. Ge, R; Ren, Y; Wang, X; Zhou, M, Understanding Deflation Process in Over-parametrized Tensor Decomposition, Advances in Neural Information Processing Systems, vol. 2 (January, 2021), pp. 1299-1311, ISBN 9781713845393  [abs]
  6. Anand, K; Ge, R; Kumar, A; Panigrahi, D, A Regression Approach to Learning-Augmented Online Algorithms, Advances in Neural Information Processing Systems, vol. 36 (January, 2021), pp. 30504-30517, ISBN 9781713845393  [abs]

Goldberg, Amy

  1. Korunes, KL; Soares-Souza, GB; Bobrek, K; Tang, H; Araújo, II; Goldberg, A; Beleza, S, Sex-biased admixture and assortative mating shape genetic variation and influence demographic inference in admixed Cabo Verdeans., G3 (Bethesda, Md.) (July, 2022), pp. jkac183 [doi]  [abs]
  2. Gopalan, S; Smith, SP; Korunes, K; Hamid, I; Ramachandran, S; Goldberg, A, Human genetic admixture through the lens of population genomics., Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, vol. 377 no. 1852 (June, 2022), pp. 20200410 [doi]  [abs]
  3. Voinson, M; Nunn, CL; Goldberg, A, Primate malarias as a model for cross-species parasite transmission., Elife, vol. 11 (January, 2022), pp. e69628 [doi]  [abs]
  4. 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]
  5. 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]
  6. Voinson, M; Nunn, CL; Goldberg, A, Primate malarias as a model for cross-species parasite transmission (April, 2021) [doi]  [abs]
  7. Korunes, KL; Goldberg, A, Human genetic admixture., Plos Genetics, vol. 17 no. 3 (March, 2021), pp. e1009374 [doi]  [abs]
  8. 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), pp. e63177 [doi]  [abs]
  9. 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 (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. Gong, Y; He, S; Kiselev, A, Random Search in Fluid Flow Aided by Chemotaxis., Bulletin of Mathematical Biology, vol. 84 no. 7 (June, 2022), pp. 71 [doi]  [abs]
  2. He, S, Enhanced dissipation, hypoellipticity for passive scalar equations with fractional dissipation, Journal of Functional Analysis, vol. 282 no. 3 (February, 2022) [doi]  [abs]
  3. Chouliara, D; Gong, Y; He, S; Kiselev, A; Lim, J; Melikechi, O; Powers, K, Hitting time of Brownian motion subject to shear flow, Involve, a Journal of Mathematics, vol. 15 no. 1 (January, 2022), pp. 131-140 [doi]  [abs]
  4. 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]
  5. Gong, Y; He, S; Kiselev, A, Random search in fluid flow aided by chemotaxis (July, 2021)  [abs]
  6. 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]
  7. 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]
  8. 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]  [abs]

Jones, Edna L

  1. Jones, E, Local densities of diagonal integral ternary quadratic forms at odd primes, International Journal of Number Theory, vol. 17 no. 3 (April, 2021), pp. 547-575 [doi]  [abs]

Kim, Woojin

  1. Dey, TK; Kim, W; Mémoli, F, Computing Generalized Rank Invariant for 2-Parameter Persistence Modules via Zigzag Persistence and Its Applications, Leibniz International Proceedings in Informatics, Lipics, vol. 224 (June, 2022), ISBN 9783959772273 [doi]  [abs]
  2. 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]  [abs]
  3. Dey, TK; Kim, W; Mémoli, F, Computing Generalized Rank Invariant for 2-Parameter Persistence Modules via Zigzag Persistence and its Applications (November, 2021)  [abs]
  4. Kim, W; Moore, S, Bigraded Betti numbers and Generalized Persistence Diagrams (November, 2021)  [abs]
  5. 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]
  6. 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; Tan, C, The Flow of Polynomial Roots Under Differentiation, Annals of Pde, vol. 8 no. 2 (December, 2022) [doi]  [abs]
  2. Kiselev, A; Luo, X, On nonexistence of splash singularities for the $α$-SQG patches (November, 2021)  [abs]
  3. 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]
  4. Gong, Y; He, S; Kiselev, A, Random search in fluid flow aided by chemotaxis (July, 2021)  [abs]
  5. Gong, Y; Kiselev, A, Chemotactic Reaction Enhancement in One Dimension (March, 2021)  [abs]
  6. Kiselev, A; Yao, Y, Small scale formations in the incompressible porous media equation (February, 2021)  [abs]
  7. 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. Gao, Y; Liu, JG, Revisit of Macroscopic Dynamics for Some Non-equilibrium Chemical Reactions from a Hamiltonian Viewpoint, Journal of Statistical Physics, vol. 189 no. 2 (November, 2022), Springer Science and Business Media LLC [doi]  [abs]
  2. Degond, P; Frouvelle, A; Liu, JG, FROM KINETIC TO FLUID MODELS OF LIQUID CRYSTALS BY THE MOMENT METHOD, Kinetic and Related Models, vol. 15 no. 3 (June, 2022), pp. 417-465, American Institute of Mathematical Sciences (AIMS) [doi]  [abs]
  3. Li, L; Liu, J-G; Liu, Z; Yang, Y; Zhou, Z, On Energy Stable Runge-Kutta Methods for the Water Wave Equation and Its Simplified Non-Local Hyperbolic Model, Communications in Computational Physics, vol. 32 no. 1 (June, 2022), pp. 222-258, Global Science Press [doi]
  4. Li, L; Liu, J-G; Tang, Y, Some Random Batch Particle Methods for the Poisson-Nernst-Planck and Poisson-Boltzmann Equations, Communications in Computational Physics, vol. 32 no. 1 (June, 2022), pp. 41-82, Global Science Press [doi]
  5. Liu, JG; Wang, Z; Zhang, Y; Zhou, Z, RIGOROUS JUSTIFICATION OF THE FOKKER-PLANCK EQUATIONS OF NEURAL NETWORKS BASED ON AN ITERATION PERSPECTIVE, Siam Journal on Mathematical Analysis, vol. 54 no. 1 (January, 2022), pp. 1270-1312, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  6. 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]
  7. Gao, Y; Liu, JG, PROJECTION METHOD FOR DROPLET DYNAMICS ON GROOVE-TEXTURED SURFACE WITH MERGING AND SPLITTING, Siam Journal on Scientific Computing, vol. 44 no. 2 (January, 2022), pp. B310-B338 [doi]  [abs]
  8. Dou, X; Liu, J-G; Zhou, Z, A tumor growth model with autophagy: The reaction-(cross-)diffusion system and its free boundary limit, Discrete and Continuous Dynamical Systems Series B (2022), American Institute of Mathematical Sciences (AIMS) [doi]  [abs]
  9. Gao, Y; Katsevich, AE; Liu, J-G; Lu, J; Marzuola, JL, Analysis of a fourth-order exponential PDE arising from a crystal surface jump process with Metropolis-type transition rates, Pure and Applied Analysis, vol. 3 no. 4 (December, 2021), pp. 595-612, Mathematical Sciences Publishers [doi]
  10. 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]
  11. Liu, J-G; Wang, Z; Xie, Y; Zhang, Y; Zhou, Z, Investigating the integrate and fire model as the limit of a random discharge model: a stochastic analysis perspective, Mathematical Neuroscience and Applications, vol. Volume 1 (November, 2021), Centre pour la Communication Scientifique Directe (CCSD) [doi]  [abs]
  12. 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]
  13. 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] [reputed journal]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. Liu, JG; Tang, M; Wang, L; Zhou, Z, Toward understanding the boundary propagation speeds in tumor growth models, Siam Journal on Applied Mathematics, vol. 81 no. 3 (January, 2021), pp. 1052-1076 [doi]  [abs]
  20. 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. Cai, Z; Lu, J; Yang, S, Fast algorithms of bath calculations in simulations of quantum system-bath dynamics, Computer Physics Communications, vol. 278 (September, 2022) [doi]  [abs]
  2. Bierman, J; Li, Y; Lu, J, Quantum Orbital Minimization Method for Excited States Calculation on a Quantum Computer., Journal of Chemical Theory and Computation, vol. 18 no. 8 (August, 2022), pp. 4674-4689 [doi]  [abs]
  3. Lu, J; Steinerberger, S, Neural collapse under cross-entropy loss, Applied and Computational Harmonic Analysis, vol. 59 (July, 2022), pp. 224-241 [doi]  [abs]
  4. Lu, J; Wang, L, Complexity of zigzag sampling algorithm for strongly log-concave distributions, Statistics and Computing, vol. 32 no. 3 (June, 2022) [doi]  [abs]
  5. Pescia, G; Han, J; Lovato, A; Lu, J; Carleo, G, Neural-network quantum states for periodic systems in continuous space, Physical Review Research, vol. 4 no. 2 (June, 2022) [doi]  [abs]
  6. Chen, K; Chen, S; Li, Q; Lu, J; Wright, S, Low-Rank Approximation for Multiscale PDEs, Notices of the American Mathematical Society, vol. 69 no. 6 (June, 2022), pp. 901-913 [doi]
  7. Lu, J; Wang, L, ON EXPLICIT L2-CONVERGENCE RATE ESTIMATE FOR PIECEWISE DETERMINISTIC MARKOV PROCESSES IN MCMC ALGORITHMS, The Annals of Applied Probability, vol. 32 no. 2 (April, 2022), pp. 1333-1361 [doi]  [abs]
  8. Lu, J; Stubbs, KD; Watson, AB, Existence and Computation of Generalized Wannier Functions for Non-Periodic Systems in Two Dimensions and Higher, Archive for Rational Mechanics and Analysis, vol. 243 no. 3 (March, 2022), pp. 1269-1323 [doi]  [abs]
  9. 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]
  10. Lu, J; Marzuola, JL; Watson, AB, DEFECT RESONANCES OF TRUNCATED CRYSTAL STRUCTURES, Siam Journal on Applied Mathematics, vol. 82 no. 1 (January, 2022), pp. 49-74, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  11. Lu, J; Zhang, Z; Zhou, Z, Bloch dynamics with second order Berry phase correction, Asymptotic Analysis, vol. 128 no. 1 (January, 2022), pp. 55-84 [doi]  [abs]
  12. Han, J; Li, Y; Lin, L; Lu, J; Zhang, J; Zhang, L, UNIVERSAL APPROXIMATION OF SYMMETRIC AND ANTI-SYMMETRIC FUNCTIONS, Communications in Mathematical Sciences, vol. 20 no. 5 (January, 2022), pp. 1397-1408 [doi]  [abs]
  13. 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]
  14. Chen, K; Chen, S; Li, Q; Lu, J; Wright, SJ, Low-rank approximation for multiscale PDEs (November, 2021)  [abs]
  15. Huang, H; Landsberg, JM; Lu, J, Geometry of backflow transformation ansatz for quantum many-body fermionic wavefunctions (November, 2021)  [abs]
  16. 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]
  17. Lu, J; Otto, F; Wang, L, Optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media (September, 2021)  [abs]
  18. 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]
  19. Barthel, T; Lu, J; Friesecke, G, On the closedness and geometry of tensor network state sets, Arxiv:2108.00031 (July, 2021)  [abs]
  20. Lu, J; Stubbs, KD, Algebraic localization of Wannier functions implies Chern triviality in non-periodic insulators (July, 2021)  [abs]
  21. Chen, Z; Lu, J; Lu, Y, On the Representation of Solutions to Elliptic PDEs in Barron Spaces (June, 2021)  [abs]
  22. 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]
  23. 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]
  24. Bal, G; Becker, S; Drouot, A; Kammerer, CF; Lu, J; Watson, A, Edge state dynamics along curved interfaces (June, 2021)  [abs]
  25. Lu, J; Lu, Y, A Priori Generalization Error Analysis of Two-Layer Neural Networks for Solving High Dimensional Schrödinger Eigenvalue Problems (May, 2021)  [abs]
  26. 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]
  27. 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]
  28. 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]
  29. Cao, Y; Lu, J, Structure-preserving numerical schemes for Lindblad equations (March, 2021)  [abs]
  30. 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]
  31. Lu, J; Stubbs, KD, Algebraic localization implies exponential localization in non-periodic insulators (January, 2021)  [abs]
  32. Lu, J; Lu, Y; Wang, M, A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations (January, 2021)  [abs]
  33. Chen, Z; Li, Y; Lu, J, On the global convergence of randomized coordinate gradient descent for non-convex optimization (January, 2021)  [abs]
  34. 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]
  35. 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]  [abs]
  36. 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]  [abs]
  37. Cao, Y; Lu, J; Wang, L, COMPLEXITY OF RANDOMIZED ALGORITHMS FOR UNDERDAMPED LANGEVIN DYNAMICS*, Communications in Mathematical Sciences, vol. 19 no. 7 (January, 2021), pp. 1827-1853, International Press of Boston [doi]  [abs]
  38. 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]
  39. Loring, TA; Lu, J; Watson, AB, Locality of the windowed local density of states (January, 2021)  [abs]
  40. 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]
  41. Chen, Z; Lu, J; Lu, Y, On the Representation of Solutions to Elliptic PDEs in Barron Spaces, Advances in Neural Information Processing Systems, vol. 8 (January, 2021), pp. 6454-6465, ISBN 9781713845393  [abs]
  42. 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 (2021), pp. e2107588118 [doi]  [abs]
  43. 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, Sharp nonuniqueness for the Navier–Stokes equations, Inventiones Mathematicae, vol. 229 no. 3 (September, 2022), pp. 987-1054 [doi]  [abs]
  2. Kiselev, A; Luo, X, On nonexistence of splash singularities for the $α$-SQG patches (November, 2021)  [abs]
  3. 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]
  4. 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. Mattingly, JC; Romito, M; Su, L, The Gaussian structure of the singular stochastic Burgers equation, Forum of Mathematics, Sigma, vol. 10 (2022), Cambridge University Press (CUP) [doi]  [abs]
  2. Earle, G; Mattingly, J, Convergence of Stratified MCMC Sampling of Non-Reversible Dynamics (November, 2021)  [abs]
  3. Herzog, DP; Mattingly, JC; Nguyen, HD, Gibbsian dynamics and the generalized Langevin equation (November, 2021)  [abs]
  4. 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]
  5. 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]  [abs]
  6. 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]
  7. 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]

McPhail-Snyder, Calvin

  1. Kai-Chieh, C; McPhail-Snyder, C; Morrison, S; Snyder, N, Kashaev-Reshetikhin Invariants of Links (August, 2021)

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. Björk, JR; Dasari, MR; Roche, K; Grieneisen, L; Gould, TJ; Grenier, J-C; Yotova, V; Gottel, N; Jansen, D; Gesquiere, LR; Gordon, JB; Learn, NH; Wango, TL; Mututua, RS; Kinyua Warutere, J; Siodi, L; Mukherjee, S; Barreiro, LB; Alberts, SC; Gilbert, JA; Tung, J; Blekhman, R; Archie, EA, Synchrony and idiosyncrasy in the gut microbiome of wild baboons., Nature Ecology and Evolution, vol. 6 no. 7 (July, 2022), pp. 955-964 [doi]  [abs]
  2. Tang, WS; da Silva, GM; Kirveslahti, H; Skeens, E; Feng, B; Sudijono, T; Yang, KK; Mukherjee, S; Rubenstein, B; Crawford, L, A topological data analytic approach for discovering biophysical signatures in protein dynamics., Plos Computational Biology, vol. 18 no. 5 (May, 2022), pp. e1010045 [doi]  [abs]
  3. Lahkar, R; Mukherjee, S; Roy, S, Generalized perturbed best response dynamics with a continuum of strategies, Journal of Economic Theory, vol. 200 (March, 2022) [doi]  [abs]
  4. McGoff, K; Mukherjee, S; Nobel, AB, GIBBS POSTERIOR CONVERGENCE AND THE THERMODYNAMIC FORMALISM, The Annals of Applied Probability, vol. 32 no. 1 (February, 2022), pp. 461-496 [doi]  [abs]
  5. Mukherjee, S, A Grover Search-Based Algorithm for the List Coloring Problem, Ieee Transactions on Quantum Engineering, vol. 3 (January, 2022) [doi]  [abs]
  6. He, S; Mukherjee, S, Exploration of stochastic dynamics and complexity in an epidemic system, The European Physical Journal Special Topics (January, 2022) [doi]  [abs]
  7. Mukherjee, S; Pramanik, A, Mild and Expeditious Synthesis of Sulfenyl Enaminones of l -α-Amino Esters and Aryl/Alkyl Amines through NCS-Mediated Sulfenylation, Acs Omega, vol. 6 no. 49 (December, 2021), pp. 33805-33821 [doi]  [abs]
  8. Mukherjee, S; Fataf, NAA; Rahim, MFA; Natiq, H, Characterizing noise-induced chaos and multifractality of a finance system, The European Physical Journal Special Topics, vol. 230 no. 21-22 (December, 2021), pp. 3873-3879 [doi]  [abs]
  9. Mukherjee, S; Hua, BS; Umetani, N; Meister, D, Neural Sequence Transformation, Computer Graphics Forum, vol. 40 no. 7 (October, 2021), pp. 131-140 [doi]  [abs]
  10. He, S; Natiq, H; Mukherjee, S, Multistability and chaos in a noise-induced blood flow, The European Physical Journal Special Topics, vol. 230 no. 5 (July, 2021), pp. 1525-1533 [doi]  [abs]
  11. Yan, B; Mukherjee, S; Saha, A, Exploring noise-induced chaos and complexity in a red blood cell system, The European Physical Journal Special Topics, vol. 230 no. 5 (July, 2021), pp. 1517-1523 [doi]  [abs]
  12. Mahato, CK; Mukherjee, S; Kundu, M; Vallapure, VP; Pramanik, A, Asymmetric 1,4-Michael Addition in Aqueous Medium Using Hydrophobic Chiral Organocatalysts, The Journal of Organic Chemistry, vol. 86 no. 7 (April, 2021), pp. 5213-5226 [doi]  [abs]
  13. Palit, SK; Mukherjee, S, A study on dynamics and multiscale complexity of a neuro system, Chaos, Solitons & Fractals, vol. 145 (April, 2021) [doi]  [abs]
  14. Mishra, R; Behera, BK; Mukherjee, S; Petru, M; Muller, M, Axial and radial compression behavior of composite rocket launcher developed by robotized filament winding: Simulation and experimental validation, Polymers, vol. 13 no. 4 (February, 2021), pp. 1-18 [doi]  [abs]
  15. Pradhan, B; Mukherjee, S; Saha, A; Natiq, H; Banerjee, S, Multistability and chaotic scenario in a quantum pair-ion plasma, Zeitschrift Für Naturforschung A, vol. 76 no. 2 (February, 2021), pp. 109-119 [doi]  [abs]
  16. Mukherjee, S, Comments on “A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach”, Journal of Cleaner Production, vol. 280 (January, 2021) [doi]  [abs]
  17. Spell, CS; Bezrukova, K; Mukherjee, S; Baveja, A, EVERY LITTLE BIT HELPS: DOES DIVERSITY IN POLICE DEPARTMENTS AND COMMUNITIES AFFECT ARREST RATES?, 81st Annual Meeting of the Academy of Management 2021: Bringing the Manager Back in Management, Aom 2021 (January, 2021) [doi]  [abs]

Nelson, Anna C

  1. Fogelson, AL; Nelson, AC; Zapata-Allegro, C; Keener, JP, DEVELOPMENT OF FIBRIN BRANCH STRUCTURE BEFORE AND AFTER GELATION., Siam Journal on Applied Mathematics, vol. 82 no. 1 (January, 2022), pp. 267-293, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  2. 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]  [abs]

Ng, Lenhard L.

  1. Casals, R; Ng, L, Braid Loops with infinite monodromy on the Legendrian contact DGA (January, 2021)  [abs]

Nolen, James H.

  1. Tough, O; Nolen, J, The Fleming-Viot Process with McKean-Vlasov Dynamics, Electronic Journal of Probability, vol. 27 (August, 2022), pp. 1-72, Institute of Mathematical Statistics [doi]  [abs]
  2. Berestycki, J; Brunet, E; Nolen, J; Penington, S, Brownian bees in the infinite swarm limit, The Annals of Probability (2022), Institute of Mathematical Statistics
  3. Berestycki, J; Brunet, É; Nolen, J; Penington, S, A free boundary problem arising from branching Brownian motion with selection, Transactions of the American Mathematical Society, vol. 374 no. 09 (May, 2021), pp. 6269-6329, American Mathematical Society (AMS) [doi]

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. Coskun, MC; Pfister, HD, An Information-Theoretic Perspective on Successive Cancellation List Decoding and Polar Code Design, Ieee Transactions on Information Theory, vol. 68 no. 9 (September, 2022), pp. 5779-5791 [doi]  [abs]
  2. Brandsen, S; Lian, M; Stubbs, KD; Rengaswamy, N; Pfister, HD, Adaptive procedures for discriminating between arbitrary tensor-product quantum states, Physical Review A, vol. 106 no. 1 (July, 2022) [doi]  [abs]
  3. Tal, I; Pfister, HD; Fazeli, A; Vardy, A, Polar Codes for the Deletion Channel: Weak and Strong Polarization, Ieee Transactions on Information Theory, vol. 68 no. 4 (April, 2022), pp. 2239-2265 [doi]  [abs]
  4. Brandsen, S; Stubbs, KD; Pfister, HD, Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing, Quantum, vol. 6 (January, 2022) [doi]  [abs]
  5. Coskun, MC; Liva, G; Amat, AGI; Lentmaier, M; Pfister, HD, Successive Cancellation Decoding of Single Parity-Check Product Codes: Analysis and Improved Decoding, Ieee Transactions on Information Theory (2022), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  6. 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]
  7. Reeves, G; Pfister, HD, Reed-Muller Codes Achieve Capacity on BMS Channels, edited by Wichs, D; Mansour, Y (October, 2021), pp. 658-669, ACM, ISBN 978-1-4503-4132-5 [doi]  [abs]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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. Bonolis, D; Pierce, LB, Application of a polynomial sieve: beyond separation of variables (September, 2022)
  2. Pierce, LB, Counting problems: class groups, primes, and number fields (June, 2022)
  3. Pierce, LB, On Superorthogonality, The Journal of Geometric Analysis, vol. 31 no. 7 (July, 2021), pp. 7096-7183 [doi]  [abs]
  4. 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]
  5. 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]
  6. Pierce, L; Bucur, A; Cojocaru, A; Lalin, M, Geometric generalizations of the square sieve, with an application to cyclic covers (2021)
  7. Pierce, L, Counting problems: class groups, primes, and number fields, ICM 2022 Proceedings (accepted, in press) (2021)
  8. Pierce, L; Gressman, P; Guo, S; Roos, J; Yung, P-L, On the strict majorant property in arbitrary dimensions (2021)
  9. 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]
  10. 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. Puleri, DF; Randles, A, The role of adhesive receptor patterns on cell transport in complex microvessels., Biomechanics and Modeling in Mechanobiology, vol. 21 no. 4 (August, 2022), pp. 1079-1098 [doi]  [abs]
  2. 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]
  3. Chidyagwai, SG; Vardhan, M; Kaplan, M; Chamberlain, R; Barker, P; Randles, A, Characterization of hemodynamics in anomalous aortic origin of coronary arteries using patient-specific modeling., J Biomech, vol. 132 (February, 2022), pp. 110919 [doi]  [abs]
  4. Feiger, B; Lorenzana-Saldivar, E; Cooke, C; Horstmeyer, R; Bishawi, M; Doberne, J; Hughes, GC; Ranney, D; Voigt, S; Randles, A, Evaluation of U-Net Based Architectures for Automatic Aortic Dissection Segmentation, Acm Transactions on Computing for Healthcare, vol. 3 no. 1 (January, 2022) [doi]  [abs]
  5. Bishawi, M; Kaplan, M; Chidyagwai, S; Cappiello, J; Cherry, A; MacLeod, D; Gall, K; Evans, N; Kim, M; Shaha, R; Whittle, J; Hollidge, M; Truskey, G; Randles, A, Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13352 LNCS (January, 2022), pp. 137-149, ISBN 9783031087561 [doi]  [abs]
  6. Tanade, C; Putney, S; Randles, A, Developing a Scalable Cellular Automaton Model of 3D Tumor Growth, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13350 LNCS (January, 2022), pp. 3-16, ISBN 9783031087509 [doi]  [abs]
  7. Roychowdhury, S; Draeger, EW; Randles, A, Establishing Metrics to Quantify Underlying Structure in Vascular Red Blood Cell Distributions, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13350 LNCS (January, 2022), pp. 89-102, ISBN 9783031087509 [doi]  [abs]
  8. 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]
  9. Liu, X; Vardhan, M; Wen, Q; Das, A; Randles, A; Chi, EC, An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions., Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference, vol. 2021 (November, 2021), pp. 4432-4435 [doi]  [abs]
  10. Tanade, C; Feiger, B; Vardhan, M; Chen, SJ; Leopold, JA; Randles, A, Global Sensitivity Analysis For Clinically Validated 1D Models of Fractional Flow Reserve., Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual International Conference, vol. 2021 (November, 2021), pp. 4395-4398 [doi]  [abs]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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. Holmes, J; Lau, T; Saylor, R; Fernández-Novel, N; Hersey, M; Keen, D; Hampel, L; Horschitz, S; Ladewig, J; Parke, B; Reed, MC; Nijhout, HF; Best, J; Koch, P; Hashemi, P, Voltammetric Approach for Characterizing the Biophysical and Chemical Functionality of Human Induced Pluripotent Stem Cell-Derived Serotonin Neurons., Analytical Chemistry, vol. 94 no. 25 (June, 2022), pp. 8847-8856 [doi]  [abs]
  2. Lawley, SD; Nijhout, HF; Reed, MC, Spiracular fluttering decouples oxygen uptake and water loss: a stochastic PDE model of respiratory water loss in insects., Journal of Mathematical Biology, vol. 84 no. 6 (April, 2022), pp. 40 [doi]  [abs]
  3. 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]
  4. 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]
  5. Kim, R; Reed, M, A mathematical model of circadian rhythms and dopamine, Theoretical Biology & Medical Modelling (January, 2021), BioMed Central

Regan, Margaret H.

  1. Bernal, EA; Hauenstein, JD; Mehta, D; Regan, MH; Tang, T, Machine learning the real discriminant locus, Journal of Symbolic Computation, vol. 115 (March, 2023), pp. 409-426 [doi]  [abs]

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, vol. 382 no. 3-4 (April, 2022), pp. 1517-1590 [doi]  [abs]
  2. Green, ML; Griffiths, P; Robles, C, Completions of period mappings: progress report (June, 2021)  [abs]
  3. Green, M; Griffiths, P; Robles, C, Natural line bundles on completions of period mappings (February, 2021)  [abs]

Rossman, Benjamin

  1. Cavalar, BP; Kumar, M; Rossman, B, Monotone Circuit Lower Bounds from Robust Sunflowers, Algorithmica (January, 2022) [doi]  [abs]
  2. Rossman, B, Shrinkage of decision lists and DNF formulas, Leibniz International Proceedings in Informatics, Lipics, vol. 185 (February, 2021), ISBN 9783959771771 [doi]  [abs]
  3. Kawarabayashi, KI; Rossman, B, A polynomial excluded-minor approximation of treedepth, Journal of the European Mathematical Society, vol. 24 no. 4 (January, 2021), pp. 1449-1470 [doi]  [abs]

Rudin, Cynthia D.

  1. Afnan, M; Afnan, MAM; Liu, Y; Savulescu, J; Mishra, A; Conitzer, V; Rudin, C, Data solidarity for machine learning for embryo selection: a call for the creation of an open access repository of embryo data., Reproductive Biomedicine Online, vol. 45 no. 1 (July, 2022), pp. 10-13 [doi]  [abs]
  2. Huang, H; Wang, Y; Rudin, C; Browne, EP, Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization., Communications Biology, vol. 5 no. 1 (July, 2022), pp. 719 [doi]  [abs]
  3. Semenova, L; Rudin, C; Parr, R, On the Existence of Simpler Machine Learning Models, Acm International Conference Proceeding Series (June, 2022), pp. 1827-1858, ISBN 9781450393522 [doi]  [abs]
  4. Wang, T; Rudin, C, Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects, Informs Journal on Computing, vol. 34 no. 3 (May, 2022), pp. 1626-1643 [doi]  [abs]
  5. 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]
  6. Rudin, C; Chen, C; Chen, Z; Huang, H; Semenova, L; Zhong, C, Interpretable machine learning: Fundamental principles and 10 grand challenges, Statistics Surveys, vol. 16 (January, 2022), pp. 1-85 [doi]  [abs]
  7. Wang, C; Han, B; Patel, B; Rudin, C, In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction, Journal of Quantitative Criminology (January, 2022) [doi]  [abs]
  8. Li, C; Rudin, C; McCormick, TH, Rethinking Nonlinear Instrumental Variable Models through Prediction Validity, Journal of Machine Learning Research, vol. 23 (January, 2022)  [abs]
  9. Barnett, AJ; Sharma, V; Gajjar, N; Fang, J; Schwartz, FR; Chen, C; Lo, JY; Rudin, C, Interpretable Deep Learning Models for Better Clinician-AI Communication in Clinical Mammography, Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 12035 (January, 2022), ISBN 9781510649453 [doi]  [abs]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. Barnett, AJ; Schwartz, FR; Tao, C; Chen, C; Ren, Y; Lo, JY; Rudin, C, IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography (March, 2021)  [abs]
  16. 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, vol. abs/2101.01867 (January, 2021)  [abs]
  17. 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]
  18. Traca, S; Rudin, C; Yan, W, Regulating greed over time in multi-armed bandits, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  19. 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]
  20. 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. Fridman, I; Chan, L; Thomas, J; Fish, LJ; Falkovic, M; Brioux, J; Hunter, N; Ryser, DH; Hwang, ES; Pollak, KI; Weinfurt, KP; Ryser, MD, A web-based personalized decision support tool for patients diagnosed with ductal carcinoma in situ: development, content evaluation, and usability testing., Breast Cancer Res Treat, vol. 192 no. 3 (April, 2022), pp. 517-527 [doi]  [abs]
  2. Ryser, MD; Lange, J; Inoue, LYT; O'Meara, ES; Gard, C; Miglioretti, DL; Bulliard, J-L; Brouwer, AF; Hwang, ES; Etzioni, RB, Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort., Ann Intern Med, vol. 175 no. 4 (April, 2022), pp. 471-478 [doi]  [abs]
  3. Schapiro, D; Yapp, C; Sokolov, A; Reynolds, SM; Chen, Y-A; Sudar, D; Xie, Y; Muhlich, J; Arias-Camison, R; Arena, S; Taylor, AJ; Nikolov, M; Tyler, M; Lin, J-R; Burlingame, EA; Human Tumor Atlas Network, ; Chang, YH; Farhi, SL; Thorsson, V; Venkatamohan, N; Drewes, JL; Pe'er, D; Gutman, DA; Herrmann, MD; Gehlenborg, N; Bankhead, P; Roland, JT; Herndon, JM; Snyder, MP; Angelo, M; Nolan, G; Swedlow, JR; Schultz, N; Merrick, DT; Mazzili, SA; Cerami, E; Rodig, SJ; Santagata, S; Sorger, PK, MITI minimum information guidelines for highly multiplexed tissue images., Nat Methods, vol. 19 no. 3 (March, 2022), pp. 262-267 [doi]
  4. Schmitz, RSJM; van den Belt-Dusebout, SW; Cresta, C; Liu, Y-H; Schaapveld, M; Clements, K; Timbres, J; Byng, DT; Ryser, MD; Ren, Y; Lynch, T; Hyslop, T; Menegaz, B; Collyar, D; Hwang, S; Thompson, A; Sawyer, E; Wesseling, J; Lips, EH; Schmidt, MK, Abstract P1-22-02: Subsequent risk of ipsilateral breast events in a multinational DCIS cohort of 48.619 patients: A meta-analysis within the PRECISION consortium, Cancer Research, vol. 82 no. 4_Supplement (February, 2022), American Association for Cancer Research (AACR) [doi]  [abs]
  5. Grimm, LJ; Rahbar, H; Abdelmalak, M; Hall, AH; Ryser, MD, Ductal Carcinoma in Situ: State-of-the-Art Review., Radiology, vol. 302 no. 2 (February, 2022), pp. 246-255 [doi]  [abs]
  6. 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]
  7. 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]
  8. Byng, D; Retel, VP; van Harten, W; Rushing, CN; Thomas, SM; Lynch, T; McCarthy, A; Francescatti, AB; Frank, ES; Partridge, AH; Thompson, AM; Grimm, L; Hyslop, T; Hwang, E-SS; Ryser, MD, Disparities in surveillance imaging after breast conserving surgery for primary DCIS., Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, vol. 39 no. 15_suppl (May, 2021), pp. 6516-6516, American Society of Clinical Oncology (ASCO) [doi]  [abs]
  9. 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 (2021), pp. E284-E286
  10. Ryser, MD; Rushing, CN; Thomas, SM; Lynch, T; McCarthy, A; Mohammed, ZA; Francescatti, AB; Frank, ES; Partridge, AH; Thompson, AM; Hyslop, T; Hwang, ES, Ipsilateral invasive cancer risk after diagnosis with ductal carcinoma in situ in patients with and without index surgery: The effects of endocrine therapy and radiation treatment, Cancer Research, vol. 81 no. 4 (2021)

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. Krishnappa Babu, PR; Di Martino, JM; Chang, Z; Perochon, S; Aiello, R; Carpenter, KLH; Compton, S; Davis, N; Franz, L; Espinosa, S; Flowers, J; Dawson, G; Sapiro, G, Complexity analysis of head movements in autistic toddlers., The Journal of Child Psychology and Psychiatry and Allied Disciplines (August, 2022) [doi]  [abs]
  2. 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, vol. 26 no. 6 (August, 2022), pp. 1451-1459 [doi]  [abs]
  3. Papadaki, A; Martinez, N; Bertran, M; Sapiro, G; Rodrigues, M, Minimax Demographic Group Fairness in Federated Learning, Acm International Conference Proceeding Series (June, 2022), pp. 142-159, ISBN 9781450393522 [doi]  [abs]
  4. Chaudhary, UN; Kelly, CN; Wesorick, BR; Reese, CM; Gall, K; Adams, SB; Sapiro, G; Di Martino, JM, Computational and image processing methods for analysis and automation of anatomical alignment and joint spacing in reconstructive surgery., Int J Comput Assist Radiol Surg, vol. 17 no. 3 (March, 2022), pp. 541-551 [doi]  [abs]
  5. 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., Int J Eat Disord, vol. 55 no. 1 (January, 2022), pp. 108-119 [doi]  [abs]
  6. Azami, H; Chang, Z; Arnold, SE; Sapiro, G; Gupta, AS, Detection of Oculomotor Dysmetria From Mobile Phone Video of the Horizontal Saccades Task Using Signal Processing and Machine Learning Approaches, Ieee Access, vol. 10 (January, 2022), pp. 34022-34031 [doi]  [abs]
  7. Zhu, W; Qiu, Q; Calderbank, R; Sapiro, G; Cheng, X, Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters, Journal of Machine Learning Research, vol. 23 (January, 2022)  [abs]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. Wang, Z; Ding, S; Li, Y; Fenn, J; Roychowdhury, S; Wallin, A; Martin, L; Ryvola, S; Sapiro, G; Qiu, Q, Cirrus: A Long-range Bi-pattern LiDAR Dataset, Proceedings Ieee International Conference on Robotics and Automation, vol. 2021-May (January, 2021), pp. 5744-5750, ISBN 9781728190778 [doi]  [abs]

Smith, David A.

  1. Smith, DA, THE JEWISHNESS OF LUKE-ACTS: LOCATING LUKAN CHRISTIANITY AMIDST THE PARTING OF THE WAYS, The Journal of Theological Studies, vol. 72 no. 2 (October, 2021), pp. 738-768 [doi]  [abs]
  2. 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. Venkatasubramanian, S; Wongkamthong, C; Soltani, M; Kang, B; Gogineni, S; Pezeshki, A; Rangaswamy, M; Tarokh, V, Toward Data-Driven STAP Radar, 2022 Ieee Radar Conference (Radarconf22) (March, 2022), IEEE [doi]
  2. Hasan, A; Pereira, JM; Farsiu, S; Tarokh, V, Identifying Latent Stochastic Differential Equations, Ieee Transactions on Signal Processing, vol. 70 (January, 2022), pp. 89-104 [doi]  [abs]
  3. Huo, Q; Shi, Y; Liu, C; Tarokh, V; Ferrari, S, Online Action Change Detection for Automatic Vision-based Ground Control of Aircraft, Aiaa Science and Technology Forum and Exposition, Aiaa Scitech Forum 2022 (January, 2022), ISBN 9781624106316 [doi]  [abs]
  4. Momenifar, M; Diao, E; Tarokh, V; Bragg, AD, Dimension reduced turbulent flow data from deep vector quantisers, Journal of Turbulence, vol. 23 no. 4-5 (January, 2022), pp. 232-264 [doi]  [abs]
  5. Le, CP; Soltani, M; Dong, J; Tarokh, V, Fisher Task Distance and its Application in Neural Architecture Search, Ieee Access, vol. 10 (January, 2022), pp. 47235-47249 [doi]  [abs]
  6. Momenifar, M; Diao, E; Tarokh, V; Bragg, AD, A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow, Data Compression Conference Proceedings, vol. 2022-March (January, 2022), pp. 312-321, ISBN 9781665478939 [doi]  [abs]
  7. Soltani, M; Wu, S; Li, Y; Ding, J; Tarokh, V, On The Energy Statistics of Feature Maps in Pruning of Neural Networks with Skip-Connections, Data Compression Conference Proceedings, vol. 2022-March (January, 2022), pp. 482, ISBN 9781665478939 [doi]  [abs]
  8. Dong, J; Wu, S; Soltani, M; Tarokh, V, Multi-Agent Adversarial Attacks for Multi-Channel Communications, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, Aamas, vol. 3 (January, 2022), pp. 1580-1582, ISBN 9781713854333  [abs]
  9. Wu, S; Diao, E; Elkhalil, K; Ding, J; Tarokh, V, Score-Based Hypothesis Testing for Unnormalized Models, Ieee Access, vol. 10 (January, 2022), pp. 71936-71950 [doi]  [abs]
  10. Soloveychik, I; Tarokh, V, Large deviations of convex polyominoes*, Electronic Journal of Probability, vol. 27 (January, 2022) [doi]  [abs]
  11. Kojima, S; Feng, Y; Maruta, K; Ootsu, K; Yokota, T; Ahn, CJ; Tarokh, V, Towards Deep Learning-Guided Multiuser SNR and Doppler Shift Detection for Next-Generation Wireless Systems, Ieee Vehicular Technology Conference, vol. 2022-June (January, 2022), ISBN 9781665482431 [doi]  [abs]
  12. Xu, X; Hasan, A; Elkhalil, K; Ding, J; Tarokh, V, Characteristic Neural Ordinary Differential Equations (November, 2021)  [abs]
  13. 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]
  14. Diao, E; Tarokh, V; Ding, J, Privacy-Preserving Multi-Target Multi-Domain Recommender Systems with Assisted AutoEncoders (October, 2021)  [abs]
  15. Le, CP; Dong, J; Soltani, M; Tarokh, V, Task Affinity with Maximum Bipartite Matching in Few-Shot Learning (October, 2021)  [abs]
  16. 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]
  17. Cannella, C; Tarokh, V, Semi-Empirical Objective Functions for MCMC Proposal Optimization, vol. abs/2106.02104 (June, 2021)  [abs]
  18. Diao, E; Ding, J; Tarokh, V, Gradient Assisted Learning, vol. abs/2106.01425 (June, 2021)  [abs]
  19. Diao, E; Ding, J; Tarokh, V, SemiFL: Communication Efficient Semi-Supervised Federated Learning with Unlabeled Clients, vol. abs/2106.01432 (June, 2021)  [abs]
  20. Yanchenko, AK; Soltani, M; Ravier, RJ; Mukherjee, S; Tarokh, V, A Methodology for Exploring Deep Convolutional Features in Relation to Hand-Crafted Features with an Application to Music Audio Modeling, vol. abs/2106.00110 (May, 2021)  [abs]
  21. 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]
  22. 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]
  23. Le, CP; Soltani, M; Dong, J; Tarokh, V, Fisher Task Distance and Its Application in Neural Architecture Search, vol. abs/2103.12827 (March, 2021)  [abs]
  24. 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, vol. abs/2103.02260 (March, 2021)  [abs]
  25. 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]
  26. Momenifar, M; Diao, E; Tarokh, V; Bragg, AD, Dimension Reduced Turbulent Flow Data From Deep Vector Quantizers, Journal of Turbulence 2022 (March, 2021)  [abs]
  27. Le, CP; Soltani, M; Ravier, R; Tarokh, V, Improved Automated Machine Learning from Transfer Learning, vol. abs/2103.00241 (February, 2021)  [abs]
  28. Ng, Y; Hasan, A; Elkhalil, K; Tarokh, V, Generative Archimedean Copulas, vol. abs/2102.11351 (February, 2021), pp. 643-653  [abs]
  29. Hasan, A; Elkhalil, K; Ng, Y; Pereira, JM; Farsiu, S; Blanchet, JH; Tarokh, V, Modeling Extremes with d-max-decreasing Neural Networks, vol. abs/2102.09042 (February, 2021)  [abs]
  30. Chan, CH; Tarokh, V; Xiong, M, Convergence Rate of Empirical Spectral Distribution of Random Matrices from Linear Codes, Ieee Transactions on Information Theory, vol. 67 no. 2 (February, 2021), pp. 1080-1087 [doi]  [abs]
  31. Zhou, J; Ding, J; Tan, KM; Tarokh, V, Model linkage selection for cooperative learning, Journal of Machine Learning Research, vol. 22 (January, 2021)  [abs]
  32. 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]
  33. 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]
  34. 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]
  35. Kojima, S; Feng, Y; Maruta, K; Ootsu, K; Yokota, T; Ahn, CJ; Tarokh, V, Investigation of Input Signal Representation to CNN for Improving SNR Classification Accuracy, Ieee Vehicular Technology Conference, vol. 2021-September (January, 2021), ISBN 9781665413688 [doi]  [abs]
  36. Ng, Y; Hasan, A; Elkhalil, K; Tarokh, V, Generative Archimedean Copulas, 37th Conference on Uncertainty in Artificial Intelligence, Uai 2021 (January, 2021), pp. 643-653  [abs]
  37. 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
  38. Elkhalil, K; Hasan, A; Ding, J; Farsiu, S; Tarokh, V, Fisher Auto-Encoders, edited by Banerjee, A; Fukumizu, K, 24th International Conference on Artificial Intelligence and Statistics (Aistats), vol. 130 (2021), pp. 352-360, PMLR
  39. Ding, J; Diao, E; Zhou, J; Tarokh, V, On Statistical Efficiency in Learning., Ieee Trans. Inf. Theory, vol. 67 (2021), pp. 2488-2506 [doi]
  40. Cannella, C; Soltani, M; Tarokh, V, Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows (2021), OpenReview.net  [abs]
  41. Elkhalil, K; Hasan, A; Ding, J; Farsiu, S; Tarokh, V, Fisher Auto-Encoders., edited by Banerjee, A; Fukumizu, K, Aistats, vol. 130 (2021), pp. 352-360, PMLR

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. Spiridonov, D; Vasilyeva, M; Wang, M; Chung, ET, Mixed Generalized Multiscale Finite Element Method for flow problem in thin domains, Journal of Computational and Applied Mathematics, vol. 416 (December, 2022) [doi]  [abs]
  2. Dahmen, W; Wang, M; Wang, Z, Nonlinear Reduced DNN Models for State Estimation (October, 2021)  [abs]

Wickelgren, Kirsten G.

  1. Arcila-Maya, N; Bethea, C; Opie, M; Wickelgren, K; Zakharevich, I, Compactly supported A1-Euler characteristic and the Hochschild complex, Topology and Its Applications, vol. 316 (July, 2022) [doi]  [abs]
  2. Kuhn, N; Mallory, D; Thatte, V; Wickelgren, K, An explicit self-duality (November, 2021)  [abs]
  3. 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]
  4. 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]
  5. 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]
  6. 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), pp. eabc7885 [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]

Wong, Biji

  1. Petkova, I; Wong, B, Twisted Mazur Pattern Satellite Knots & Bordered Floer Theory, The Michigan Mathematical Journal, vol. -1 no. -1 (January, 2021), Michigan Mathematical Journal [doi]

Wu, Hau-Tieng

  1. Chen, Z; Wu, HT, Disentangling modes with crossover instantaneous frequencies by synchrosqueezed chirplet transforms, from theory to application, Applied and Computational Harmonic Analysis, vol. 62 (January, 2023), pp. 84-122 [doi]  [abs]
  2. Steinerberger, S; Wu, HT, Eigenvector Phase Retrieval: Recovering eigenvectors from the absolute value of their entries, Linear Algebra and Its Applications, vol. 652 (November, 2022), pp. 239-252 [doi]  [abs]
  3. 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, vol. 174 (October, 2022), pp. 107384-107384, Elsevier BV [doi]  [abs]
  4. Alian, A; Shelley, K; Wu, H-T, Amplitude and phase measurements from harmonic analysis may lead to new physiologic insights: lower body negative pressure photoplethysmographic waveforms as an example., Journal of Clinical Monitoring and Computing (July, 2022) [doi]  [abs]
  5. Zimmermann, P; Antonelli, MC; Sharma, R; Müller, A; Zelgert, C; Fabre, B; Wenzel, N; Wu, H-T; Frasch, MG; Lobmaier, SM, Prenatal stress perturbs fetal iron homeostasis in a sex specific manner., Scientific Reports, vol. 12 no. 1 (June, 2022), pp. 9341 [doi]  [abs]
  6. Wu, HT; Wu, N, Strong uniform consistency with rates for kernel density estimators with general kernels on manifolds, Information and Inference, vol. 11 no. 2 (June, 2022), pp. 781-799 [doi]  [abs]
  7. Dunson, DB; Wu, HT; Wu, N, Graph based Gaussian processes on restricted domains, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 84 no. 2 (April, 2022), pp. 414-439 [doi]  [abs]
  8. 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]
  9. Liu, GR; Sheu, YC; Wu, HT, Asymptotic Analysis of higher-order scattering transform of Gaussian processes, Electronic Journal of Probability, vol. 27 (January, 2022) [doi]  [abs]
  10. Shen, C; Lin, YT; Wu, HT, Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing, Journal of Machine Learning Research, vol. 23 (January, 2022)  [abs]
  11. Chen, YC; Wu, HT; Tu, PH; Yeh, CH; Liu, TC; Yeap, MC; Chao, YP; Chen, PL; Lu, CS; Chen, CC, Theta Oscillations at Subthalamic Region Predicts Hypomania State After Deep Brain Stimulation in Parkinson's Disease, Frontiers in Human Neuroscience, vol. 15 (December, 2021) [doi]  [abs]
  12. Hamilton, W; Marzuola, JL; Wu, H-T, On the behavior of 1-Laplacian ratio cuts on nearly rectangular domains, Information and Inference, vol. 10 no. 4 (December, 2021), pp. 1563-1610, Oxford University Press (OUP) [doi]  [abs]
  13. Chen, Z; Wu, H-T, Disentangling modes with crossover instantaneous frequencies by synchrosqueezed chirplet transforms, from theory to application (December, 2021)  [abs]
  14. Steinerberger, S; Wu, H-T, Fundamental component enhancement via adaptive nonlinear activation functions (December, 2021)  [abs]
  15. Ding, X; Wu, H-T, How do kernel-based sensor fusion algorithms behave under high dimensional noise? (November, 2021)  [abs]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. Chiu, N-T; Huwiler, S; Ferster, ML; Karlen, W; Wu, H-T; Lustenberger, C, Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG (February, 2021) [doi]  [abs]
  27. 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]
  28. 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]
  29. Chung, Y-M; Hu, C-S; Lo, Y-L; Wu, H-T, A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification., Frontiers in Physiology, vol. 12 (January, 2021), pp. 637684 [doi]  [abs]
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. Meynard, A; Seneviratna, G; Doyle, E; Becker, J; Wu, HT; Borg, JS, Predicting Trust Using Automated Assessment of Multivariate Interactional Synchrony, Proceedings 2021 16th Ieee International Conference on Automatic Face and Gesture Recognition, Fg 2021 (January, 2021), ISBN 9781665431767 [doi]  [abs]

Wu, Nan

  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]

Xie, Jichun

  1. DiMarco, AV; Qin, X; McKinney, BJ; Garcia, NMG; Van Alsten, SC; Mendes, EA; Force, J; Hanks, BA; Troester, MA; Owzar, K; Xie, J; Alvarez, JV, APOBEC Mutagenesis Inhibits Breast Cancer Growth through Induction of T cell-Mediated Antitumor Immune Responses., Cancer Immunol Res, vol. 10 no. 1 (January, 2022), pp. 70-86 [doi]  [abs]
  2. Siamakpour-Reihani, S; Cao, F; Lyu, J; Ren, Y; Nixon, AB; Xie, J; Bush, AT; Starr, MD; Bain, JR; Muehlbauer, MJ; Ilkayeva, O; Byers Kraus, V; Huebner, JL; Chao, NJ; Sung, AD, Evaluating immune response and metabolic related biomarkers pre-allogenic hematopoietic stem cell transplant in acute myeloid leukemia., Plos One, vol. 17 no. 6 (2022), pp. e0268963 [doi]  [abs]
  3. Li, X; Sung, A; Xie, J, Distance Assisted Recursive Testing (March, 2021)  [abs]
  4. 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 (2021)

Zhang, Ruda

  1. Zhang, R; Ghanem, R, Drivers Learn City-Scale Intra-Daily Dynamic Equilibrium, Ieee Transactions on Intelligent Transportation Systems (January, 2022), pp. 1-10, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  2. Zhang, R; Mak, S; Dunson, D, Gaussian Process Subspace Regression for Model Reduction (July, 2021)  [abs]
  3. Zhang, R; Ghanem, R, Normal-Bundle Bootstrap, Siam Journal on Mathematics of Data Science, vol. 3 no. 2 (January, 2021), pp. 573-592, Society for Industrial & Applied Mathematics (SIAM) [doi]

Zhao, Hongkai

  1. Zhao, H; Zhong, Y, Quantitative PAT with simplified P N approximation, Inverse Problems, vol. 37 no. 5 (May, 2021) [doi]  [abs]
  2. Xiang, R; Lai, R; Zhao, H, A Dual Iterative Refinement Method for Non-rigid Shape Matching, Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition (January, 2021), pp. 15925-15934, IEEE, ISBN 9781665445092 [doi]  [abs]
  3. Zhong, Y; Zhao, H; Ren, K, Separability of the kernel function in an integral formulation for anisotropic radiative transfer equation, Siam Journal on Mathematical Analysis, vol. 53 no. 5 (2021), pp. 5613-5613, Society for Industrial and Applied Mathematics
  4. Zhao, H; Li, J, Scalable Incremental Nonconvex Optimization Approach for Phase Retrieval, Journal of Scientific Computing (2021), Springer (part of Springer Nature)
  5. Zhao, H; Bryson, J; Vershynin, R, Marchenko-Pastur law with relaxed independence conditions, Random Matrices: Theory and Applications (2021), World Scientific Publishing [doi]  [abs]

Zhong, Yimin

  1. Stefanov, P; Zhong, Y, INVERSE BOUNDARY PROBLEM FOR THE TWO PHOTON ABSORPTION TRANSPORT EQUATION, Siam Journal on Mathematical Analysis, vol. 54 no. 3 (January, 2022), pp. 2753-2767 [doi]  [abs]
  2. Zhao, H; Zhong, Y, Quantitative PAT with simplified P N approximation, Inverse Problems, vol. 37 no. 5 (May, 2021) [doi]  [abs]
  3. 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]
  4. Zhong, Y; Zhao, H; Ren, K, Separability of the kernel function in an integral formulation for anisotropic radiative transfer equation, Siam Journal on Mathematical Analysis, vol. 53 no. 5 (2021), pp. 5613-5613, Society for Industrial and Applied Mathematics

 

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