Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Math HOME

.......................

Faculty

.......................

Webpage

Mathematics Faculty: Publications since January 2022

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

Agarwal, Pankaj K.

  1. Agarwal, PK; Katz, MJ; Sharir, M, On reverse shortest paths in geometric proximity graphs, Computational Geometry, vol. 117 (February, 2024) [doi]  [abs]
  2. Agarwal, PK; Geft, T; Halperin, D; Taylor, E, Multi-robot motion planning for unit discs with revolving areas, Computational Geometry, vol. 114 (October, 2023) [doi]  [abs]
  3. Agarwal, PK; Katz, MJ; Sharir, M, On Reverse Shortest Paths in Geometric Proximity Graphs, Leibniz International Proceedings in Informatics, Lipics, vol. 248 (December, 2022), ISBN 9783959772587 [doi]  [abs]
  4. Agarwal, PK; Geft, T; Halperin, D; Taylor, E, Multi-Robot Motion Planning for Unit Discs with Revolving Areas, Leibniz International Proceedings in Informatics, Lipics, vol. 248 (December, 2022), ISBN 9783959772587 [doi]  [abs]
  5. Agarwal, PK; Cohen, R; Halperin, D; Mulzer, W, Maintaining the Union of Unit Discs under Insertions with Near-Optimal Overhead, Acm Transactions on Algorithms, vol. 18 no. 3 (October, 2022) [doi]  [abs]
  6. 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]
  7. Agarwal, PK; Ko, SH; Munagala, K; Taylor, E, Locally Fair Partitioning, Proceedings of the 36th Aaai Conference on Artificial Intelligence, Aaai 2022, vol. 36 (June, 2022), pp. 4752-4759, ISBN 1577358767  [abs]
  8. 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]
  9. 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]
  10. 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]
  11. 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]

Aquino, Wilkins

  1. Chen, MJ; Sivakumar, K; Banyay, GA; Golchert, BM; Walsh, TF; Zavlanos, MM; Aquino, W, Bayesian Optimal Sensor Placement for Damage Detection in Frequency-Domain Dynamics, Journal of Engineering Mechanics, vol. 148 no. 12 (December, 2022) [doi]  [abs]
  2. Zou, Z; Kouri, DP; Aquino, W, A Locally Adapted Reduced-Basis Method for Solving Risk-Averse PDE-Constrained Optimization Problems, Siam/Asa Journal on Uncertainty Quantification, vol. 10 no. 4 (December, 2022), pp. 1629-1651 [doi]  [abs]
  3. Treweek, BC; Sanders, CM; Aquino, W; Walsh, TF, Optimization of Acoustic Channels to Minimize Scattered Pressure Fields, Journal of Theoretical and Computational Acoustics, vol. 30 no. 3 (September, 2022) [doi]  [abs]
  4. 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]
  5. 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]

Arapura, Donu V.

  1. Arapura, D; Matsuki, K; Patel, D; Włodarczyk, J, A Kawamata–Viehweg type formulation of the logarithmic Akizuki–Nakano vanishing theorem, Mathematische Zeitschrift, vol. 303 no. 4 (April, 2023) [doi]  [abs]
  2. Arapura, D, HODGE CYCLES AND THE LERAY FILTRATION, Pacific Journal of Mathematics, vol. 319 no. 2 (January, 2022), pp. 233-258 [doi]  [abs]
  3. 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]

Bendich, Paul L

  1. Koplik, G; Borggren, N; Voisin, S; Angeloro, G; Hineman, J; Johnson, T; Bendich, P, Topological Simplification of Signals for Inference and Approximate Reconstruction, Ieee Aerospace Conference Proceedings, vol. 2023-March (January, 2023), ISBN 9781665490320 [doi]  [abs]
  2. Voisin, S; Hineman, J; Polly, JB; Koplik, G; Ball, K; Bendich, P; D‘Addezio, J; Jacobs, GA; Özgökmen, T, Topological Feature Tracking for Submesoscale Eddies, Geophysical Research Letters, vol. 49 no. 20 (October, 2022) [doi]  [abs]
  3. 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]

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

Bezemek, Zachary W

  1. Bezemek, ZW; Spiliopoulos, K, Moderate deviations for fully coupled multiscale weakly interacting particle systems, Stochastics and Partial Differential Equations: Analysis and Computations (July, 2023), Springer Science and Business Media LLC [doi]
  2. Bezemek, ZW; Spiliopoulos, K, Rate of homogenization for fully-coupled McKean–Vlasov SDEs, Stochastics and Dynamics, vol. 23 no. 02 (March, 2023), World Scientific Pub Co Pte Ltd [doi]  [abs]
  3. Bezemek, ZW; Spiliopoulos, K, Large deviations for interacting multiscale particle systems, Stochastic Processes and Their Applications, vol. 155 (January, 2023), pp. 27-108, Elsevier BV [doi]
  4. Bezemek, Z; Heldman, M, Importance Sampling for the Empirical Measure of Weakly Interacting Diffusions (October, 2022)

Bray, Hubert

  1. Bray, H; Hirsch, S; Kazaras, D; Khuri, M; Zhang, Y, Spacetime Harmonic Functions and Applications to Mass, edited by Gromov, ML; Lawson, HB, Perspectives in Scalar Curvature (February, 2023), World Scientific  [abs]

Bryant, Robert   (search)

  1. Bryant, R, On the Convex Pfaff–Darboux Theorem of Ekeland and Nirenberg, Symmetry, Integrability and Geometry: Methods and Applications, vol. 19 (August, 2023), National Academy of Science of Ukraine
  2. Bryant, R; Cheeger, J; Griffiths, P; Blum, L; Burns, D; Connes, A; Donnelly, H; Ebin, D; Guillemin, V; Palais, R; Rossi, H; Simons, J; Singer, E; Singer, N; Stanton, N; Sternberg, S, Isadore M. Singer (1924–2021) In Memoriam Part 2: Personal Recollections, Notices of the American Mathematical Society, vol. 69 no. 10 (November, 2022), pp. 1-1, American Mathematical Society (AMS) [doi]
  3. Bryant, R; Bismut, J-M; Cheeger, J; Griffiths, P; Donaldson, S; Hitchin, N; Lawson, HB; Gromov, M; Marcus, A; Spielman, D; Srivastava, N; Witten, E, Isadore M. Singer (1924–2021) In Memoriam Part 1: Scientific Works, Notices of the American Mathematical Society, vol. 69 no. 09 (October, 2022), pp. 1-1, American Mathematical Society (AMS) [doi]
  4. Phong, DH; Siu, Y-T; Bryant, R; Chau, A; Falbel, E; Fefferman, C; Friedman, R; Morgan, J; Futaki, A; Griffiths, P; Kohn, JJ; Mok, N; Mori, S; Namba, M; Noguchi, J; Ohsawa, T; Sato, M; Yau, S-T, Masatake Kuranishi (1924–2021), Notices of the American Mathematical Society, vol. 69 no. 05 (May, 2022), pp. 1-1, American Mathematical Society (AMS) [doi]

Chariker, Christopher L.

  1. Chariker, L; Shapley, R; Hawken, M; Young, L-S, A Computational Model of Direction Selectivity in Macaque V1 Cortex Based on Dynamic Differences between On and Off Pathways., The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, vol. 42 no. 16 (April, 2022), pp. 3365-3380 [doi]  [abs]
  2. Chariker, L; Lebowitz, JL, Time evolution of a mean-field generalized contact process, Journal of Statistical Mechanics: Theory and Experiment, vol. 2022 no. 2 (February, 2022), pp. 023502-023502, IOP Publishing [doi]  [abs]

Cheng, Xiuyuan

  1. Cheng, X; Wu, N, Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation, Applied and Computational Harmonic Analysis, vol. 61 (November, 2022), pp. 132-190 [doi]  [abs]
  2. Tan, Y; Zhang, Y; Cheng, X; Zhou, X-H, Statistical inference using GLEaM model with spatial heterogeneity and correlation between regions., Scientific Reports, vol. 12 no. 1 (October, 2022), pp. 16630 [doi]  [abs]
  3. Cheng, X; Cloninger, A, Classification Logit Two-Sample Testing by Neural Networks for Differentiating Near Manifold Densities, Ieee Transactions on Information Theory, vol. 68 no. 10 (October, 2022), pp. 6631-6662 [doi]  [abs]
  4. Cheng, X; Wu, H-T, Convergence of graph Laplacian with kNN self-tuned kernels, Information and Inference, vol. 11 no. 3 (September, 2022), pp. 889-957, Oxford University Press (OUP) [doi]  [abs]
  5. 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]
  6. Zhu, S; Wang, H; Dong, Z; Cheng, X; Xie, Y, NEURAL SPECTRAL MARKED POINT PROCESSES, Iclr 2022 10th International Conference on Learning Representations (January, 2022)  [abs]

Ciocanel, Maria-Veronica

  1. Ciocanel, M-V; Ding, L; Mastromatteo, L; Reichheld, S; Cabral, S; Mowry, K; Sandstede, B, Parameter identifiability in PDE models of fluorescence recovery after photobleaching (July, 2023)
  2. Ciocanel, M-V, Applications of PDEs and Stochastic Modeling to Protein Transport in Cell Biology, Notices of the American Mathematical Society (December, 2022), American Mathematical Society [doi]  [abs]
  3. Ciocanel, M-V; Nardini, J, Online and In-Person Interviewing for Tenure-Track Positions, Notices of the American Mathematical Society (August, 2022) [doi]  [abs]
  4. 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)
  5. 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]
  6. Dawson, M; Dudley, C; Omoma, S; Tung, H-R; Ciocanel, M-V, Characterizing emerging features in cell dynamics using topological data analysis methods, Mathematical Biosciences and Engineering, vol. 20 no. 2 (2022), pp. 3023-3046, American Institute of Mathematical Sciences (AIMS) [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, vol. 2023 no. 8 (April, 2023), pp. 6648-6690, Oxford University Press (OUP) [doi]  [abs]
  2. Cook, N; Hachem, W; Najim, J; Renfrew, D, Non-Hermitian Random Matrices with a Variance Profile (II): Properties and Examples, Journal of Theoretical Probability, vol. 35 no. 4 (December, 2022), pp. 2343-2382 [doi]  [abs]
  3. Cook, NA; Guionnet, A; Husson, J, Spectrum and pseudospectrum for quadratic polynomials in Ginibre matrices, Annales De L'Institut Henri Poincaré, Probabilités Et Statistiques, vol. 58 no. 4 (November, 2022), pp. 2284-2320 [doi]  [abs]

Dasgupta, Samit

  1. Dasgupta, S; Kakde, M, On the Brumer-Stark conjecture, Annals of Mathematics, vol. 197 no. 1 (January, 2023), pp. 289-388, Annals of Mathematics [doi]  [abs]

Daubechies, Ingrid

  1. Xu, J; Li, Y; Yang, H; Dunson, D; Daubechies, I, PiPs: A kernel-based optimization scheme for analyzing non-stationary 1D signals, Applied and Computational Harmonic Analysis, vol. 66 (September, 2023), pp. 1-17 [doi]  [abs]
  2. Balmaceda, JM; Clemens, CH; Daubechies, I; Pineda, AR; Rusu, G; Waldschmidt, M, Graduate Assistantships in Developing Countries (GRAID) Supporting Mathematics Graduate Students in the Countries that Need it Most, Notices of the American Mathematical Society, vol. 70 no. 8 (September, 2023), pp. 1281-1284 [doi]
  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, vol. 69 no. 1 (January, 2023), pp. 482-495 [doi]  [abs]
  4. Pu, W; Sober, B; Daly, N; Zhou, C; Sabetsarvestani, Z; Higgitt, C; Daubechies, I; Rodrigues, MRD, Image Separation With Side Information: A Connected Auto-Encoders Based Approach., Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society, vol. 32 (January, 2023), pp. 2931-2946 [doi]  [abs]
  5. Shan, S; Daubechies, I, Diffusion Maps: Using the Semigroup Property for Parameter Tuning, in Applied and Numerical Harmonic Analysis, vol. Part F6 (January, 2023), pp. 409-424 [doi]  [abs]
  6. 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]
  7. 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]

Deng, Haohua

  1. Deng, H, Extension of period maps by polyhedral fans, Advances in Mathematics, vol. 406 (September, 2022) [doi]  [abs]

Dolbow, John E.

  1. Liu, Y; Claus, S; Kerfriden, P; Chen, J; Zhong, P; Dolbow, JE, Model-based simulations of pulsed laser ablation using an embedded finite element method., International Journal of Heat and Mass Transfer, vol. 204 (May, 2023), pp. 123843 [doi]  [abs]
  2. Fei, F; Costa, A; Dolbow, JE; Settgast, RR; Cusini, M, Phase-Field Simulation of Near-Wellbore Nucleation and Propagation of Hydraulic Fractures in Enhanced Geothermal Systems (EGS), Society of Petroleum Engineers Spe Reservoir Simulation Conference, Rsc 2023 (January, 2023), ISBN 9781613998717 [doi]  [abs]
  3. 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, vol. 402 (December, 2022) [doi]  [abs]
  4. Costa, A; Cusini, M; Jin, T; Settgast, R; Dolbow, JE, A multi-resolution approach to hydraulic fracture simulation, International Journal of Fracture, vol. 237 no. 1-2 (September, 2022), pp. 165-188 [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

Dunlap, Alexander J

  1. Dunlap, A; Gu, Y; Komorowski, T, Fluctuation exponents of the KPZ equation on a large torus, Communications on Pure and Applied Mathematics, vol. 76 no. 11 (November, 2023), pp. 3104-3149, WILEY [doi]  [abs]
  2. Drivas, TD; Dunlap, A; Graham, C; La, J; Ryzhik, L, Invariant measures for stochastic conservation laws on the line, Nonlinearity, vol. 36 no. 9 (September, 2023), pp. 4553-4594 [doi]  [abs]
  3. Dunlap, A; Gu, Y; Li, L, Localization Length of the $$1+1$$ Continuum Directed Random Polymer, Annales Henri Poincaré, vol. 24 no. 7 (July, 2023), pp. 2537-2555, Springer Science and Business Media LLC [doi]
  4. Dunlap, A; Mourrat, J-C, Local Versions of Sum-of-Norms Clustering, Siam Journal on Mathematics of Data Science, vol. 4 no. 4 (December, 2022), pp. 1250-1271, Society for Industrial & Applied Mathematics (SIAM) [doi]
  5. Dunlap, A; Gu, Y, A forward-backward SDE from the 2D nonlinear stochastic heat equation, The Annals of Probability, vol. 50 no. 3 (May, 2022), Institute of Mathematical Statistics [doi]
  6. Dunlap, A; Gu, Y, A quenched local limit theorem for stochastic flows, Journal of Functional Analysis, vol. 282 no. 6 (March, 2022) [doi]  [abs]

Dunson, David B.   (search)

  1. Xu, J; Li, Y; Yang, H; Dunson, D; Daubechies, I, PiPs: A kernel-based optimization scheme for analyzing non-stationary 1D signals, Applied and Computational Harmonic Analysis, vol. 66 (September, 2023), pp. 1-17 [doi]  [abs]
  2. Rigon, T; Herring, AH; Dunson, DB, A generalized Bayes framework for probabilistic clustering, Biometrika, vol. 110 no. 3 (September, 2023), pp. 559-578 [doi]  [abs]
  3. Liu, R; Li, M; Dunson, DB, PPA: Principal parcellation analysis for brain connectomes and multiple traits., Neuroimage, vol. 276 (August, 2023), pp. 120214 [doi]  [abs]
  4. Buch, DA; Johndrow, JE; Dunson, DB, Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model., Biometrics (July, 2023) [doi]  [abs]
  5. Mahzarnia, A; Stout, JA; Anderson, RJ; Moon, HS; Yar Han, Z; Beck, K; Browndyke, JN; Dunson, DB; Johnson, KG; O'Brien, RJ; Badea, A, Identifying vulnerable brain networks associated with Alzheimer's disease risk., Cerebral Cortex, vol. 33 no. 9 (April, 2023), pp. 5307-5322 [doi]  [abs]
  6. Gu, Y; Dunson, DB, Bayesian Pyramids: identifiable multilayer discrete latent structure models for discrete data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 85 no. 2 (April, 2023), pp. 399-426 [doi]  [abs]
  7. Zito, A; Rigon, T; Dunson, DB, Inferring taxonomic placement from DNA barcoding aiding in discovery of new taxa, Methods in Ecology and Evolution, vol. 14 no. 2 (February, 2023), pp. 529-542 [doi]  [abs]
  8. Papadogeorgou, G; Bello, C; Ovaskainen, O; Dunson, DB, Covariate-Informed Latent Interaction Models: Addressing Geographic & Taxonomic Bias in Predicting Bird–Plant Interactions, Journal of the American Statistical Association (January, 2023) [doi]  [abs]
  9. Barrientos, AF; Sen, D; Page, GL; Dunson, DB, Bayesian Inferences on Uncertain Ranks and Orderings: Application to Ranking Players and Lineups, Bayesian Analysis, vol. 18 no. 3 (January, 2023), pp. 777-806 [doi]  [abs]
  10. Sachs, M; Sen, D; Lu, J; Dunson, D, Posterior Computation with the Gibbs Zig-Zag Sampler, Bayesian Analysis, vol. 18 no. 3 (January, 2023), pp. 909-927 [doi]  [abs]
  11. Young, AL; van den Boom, W; Schroeder, RA; Krishnamoorthy, V; Raghunathan, K; Wu, H-T; Dunson, DB, Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data., Plos One, vol. 18 no. 4 (2023), pp. e0284904 [doi]  [abs]
  12. Legramanti, S; Rigon, T; Durante, D; Dunson, DB, EXTENDED STOCHASTIC BLOCK MODELS WITH APPLICATION TO CRIMINAL NETWORKS., The Annals of Applied Statistics, vol. 16 no. 4 (December, 2022), pp. 2369-2395 [doi]  [abs]
  13. 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]
  14. Melikechi, O; Young, AL; Tang, T; Bowman, T; Dunson, D; Johndrow, J, Limits of epidemic prediction using SIR models., Journal of Mathematical Biology, vol. 85 no. 4 (September, 2022), pp. 36 [doi]  [abs]
  15. Schiavon, L; Canale, A; Dunson, DB, Generalized infinite factorization models., Biometrika, vol. 109 no. 3 (September, 2022), pp. 817-835 [doi]  [abs]
  16. Dey, P; Zhang, Z; Dunson, DB, Outlier detection for multi-network data., Bioinformatics (Oxford, England), vol. 38 no. 16 (August, 2022), pp. 4011-4018 [doi]  [abs]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. Peruzzi, M; Dunson, DB, Spatial Multivariate Trees for Big Data Bayesian Regression., Journal of Machine Learning Research, vol. 23 (January, 2022), pp. 17  [abs]
  25. 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]
  26. Zito, A; Rigon, T; Ovaskainen, O; Dunson, DB, Bayesian Modeling of Sequential Discoveries, Journal of the American Statistical Association (January, 2022) [doi]  [abs]
  27. 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]
  28. Badea, A; Li, D; Niculescu, AR; Anderson, RJ; Stout, JA; Williams, CL; Colton, CA; Maeda, N; Dunson, DB, Corrigendum: Absolute winding number differentiates mouse spatial navigation strategies with genetic risk for Alzheimer's disease., Frontiers in Neuroscience, vol. 16 (2022), pp. 1070425 [doi]  [abs]

Durrett, Richard T.

  1. Tung, H-R; Durrett, R, Competitive exclusion in a model with seasonality: Three species cannot coexist in an ecosystem with two seasons., Theoretical Population Biology, vol. 148 (December, 2022), pp. 40-45 [doi]  [abs]
  2. Boyle, L; Hletko, S; Huang, J; Lee, J; Pallod, G; Tung, H-R; Durrett, R, Selective sweeps in SARS-CoV-2 variant competition., Proceedings of the National Academy of Sciences of the United States of America, vol. 119 no. 47 (November, 2022), pp. e2213879119 [doi]  [abs]
  3. Durrett, R; Yao, D, Susceptible–infected epidemics on evolving graphs, Electronic Journal of Probability, vol. 27 (January, 2022), pp. 1-66 [doi]  [abs]

Elgindi, Tarek M

  1. Crippa, G; Elgindi, T; Iyer, G; Mazzucato, AL, Growth of Sobolev norms and loss of regularity in transport equations., Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, vol. 380 no. 2225 (June, 2022), pp. 20210024 [doi]  [abs]
  2. 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]
  3. 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]
  4. Drivas, TD; Elgindi, TM; La, J, Propagation of singularities by Osgood vector fields and for 2D inviscid incompressible fluids, Mathematische Annalen (January, 2022) [doi]  [abs]

Faigenbaum-Golovin, Shira

  1. Faigenbaum-Golovin, S; Levin, D, Manifold reconstruction and denoising from scattered data in high dimension, Journal of Computational and Applied Mathematics, vol. 421 (March, 2023), pp. 114818-114818, Elsevier BV [doi]
  2. Faigenbaum-Golovin, S; Levin, D, Approximation of functions on manifolds in high dimension from noisy scattered data, Jaen Journal on Approximation, vol. 13 (December, 2022), pp. 43-73, Universidad de Jaen Campus Las Lagunillas
  3. Faigenbaum-Golovin, S; Shimshi, O, Image quality assessment: Learning to rank image distortion level, Electronic Imaging, vol. 34 no. 9 (January, 2022), Society for Imaging Science & Technology [doi]
  4. Faigenbaum-Golovin, S; Shaus, A; Sober, B, Computational Handwriting Analysis of Ancient Hebrew Inscriptions – A Survey, Ieee Bits the Information Theory Magazine (2022), pp. 1-15, Institute of Electrical and Electronics Engineers (IEEE) [doi]

Fang, Di

  1. Huang, H-Y; Tong, Y; Fang, D; Su, Y, Learning Many-Body Hamiltonians with Heisenberg-Limited Scaling., Physical Review Letters, vol. 130 no. 20 (May, 2023), pp. 200403 [doi]  [abs]
  2. Fang, D; Vilanova, AT, Observable Error Bounds of the Time-Splitting Scheme for Quantum-Classical Molecular Dynamics, Siam Journal on Numerical Analysis, vol. 61 no. 1 (February, 2023), pp. 26-44, Society for Industrial & Applied Mathematics (SIAM) [doi]
  3. Fang, D; Lin, L; Tong, Y, Time-marching based quantum solvers for time-dependent linear differential equations, Quantum, vol. 7 (January, 2023) [doi]  [abs]
  4. An, D; Fang, D; Lin, L, Parallel transport dynamics for mixed quantum states with applications to time-dependent density functional theory, Journal of Computational Physics, vol. 451 (February, 2022) [doi]  [abs]
  5. An, D; Fang, D; Lin, L, Time-dependent Hamiltonian Simulation of Highly Oscillatory Dynamics and Superconvergence for Schrödinger Equation, Quantum, vol. 6 (January, 2022) [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. Anand, K; Ge, R; Kumar, A; Panigrahi, D, Online Algorithms with Multiple Predictions, Proceedings of Machine Learning Research, vol. 162 (January, 2022), pp. 582-598  [abs]
  4. Frandsen, A; Ge, R; Lee, H, Extracting Latent State Representations with Linear Dynamics from Rich Observations, Proceedings of Machine Learning Research, vol. 162 (January, 2022), pp. 6705-6725  [abs]
  5. Cheng, Y; Diakonikolas, I; Ge, R; Gupta, S; Kane, DM; Soltanolkotabi, M, Outlier-Robust Sparse Estimation via Non-Convex Optimization, Advances in Neural Information Processing Systems, vol. 35 (January, 2022), ISBN 9781713871088  [abs]
  6. Chidambaram, M; Wang, X; Hu, Y; Wu, C; Ge, R, TOWARDS UNDERSTANDING THE DATA DEPENDENCY OF MIXUP-STYLE TRAINING, Iclr 2022 10th International Conference on Learning Representations (January, 2022)  [abs]

Goldberg, Amy

  1. Hamid, I; Korunes, KL; Schrider, DR; Goldberg, A, Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes., edited by Rogers, R, Molecular Biology and Evolution, vol. 40 no. 4 (April, 2023), pp. msad074, Oxford University Press (OUP) [doi]  [abs]
  2. 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.), vol. 12 no. 10 (September, 2022), pp. jkac183 [doi]  [abs]
  3. 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]
  4. 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]

Harer, John

  1. Motta, FC; McGoff, K; Moseley, RC; Cho, C-Y; Kelliher, CM; Smith, LM; Ortiz, MS; Leman, AR; Campione, SA; Devos, N; Chaorattanakawee, S; Uthaimongkol, N; Kuntawunginn, W; Thongpiam, C; Thamnurak, C; Arsanok, M; Wojnarski, M; Vanchayangkul, P; Boonyalai, N; Smith, PL; Spring, MD; Jongsakul, K; Chuang, I; Harer, J; Haase, SB, The parasite intraerythrocytic cycle and human circadian cycle are coupled during malaria infection., Proceedings of the National Academy of Sciences of the United States of America, vol. 120 no. 24 (June, 2023), pp. e2216522120 [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]

Herschlag, Gregory J.

  1. Zhao, Z; Hettle, C; Gupta, S; Mattingly, JC; Randall, D; Herschlag, GJ, Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan, Acm International Conference Proceeding Series (October, 2022), ISBN 9781450394772 [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]

Kiselev, Alexander A.

  1. Kiselev, A; Yao, Y, Small Scale Formations in the Incompressible Porous Media Equation, Archive for Rational Mechanics and Analysis, vol. 247 no. 1 (February, 2023) [doi]  [abs]
  2. Kiselev, A; Tan, C, The Flow of Polynomial Roots Under Differentiation, Annals of Pde, vol. 8 no. 2 (December, 2022) [doi]  [abs]
  3. 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]
  4. 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]
  5. Kiselev, A; Tan, C, GLOBAL REGULARITY FOR A NONLOCAL PDE DESCRIBING EVOLUTION OF POLYNOMIAL ROOTS UNDER DIFFERENTIATION, Siam Journal on Mathematical Analysis, vol. 54 no. 3 (January, 2022), pp. 3161-3191 [doi]  [abs]

Levine, Adam S.

  1. Hom, J; Levine, AS; Lidman, T, KNOT CONCORDANCE IN HOMOLOGY COBORDISMS, Duke Mathematical Journal, vol. 171 no. 15 (October, 2022), pp. 3089-3131 [doi]  [abs]
  2. Gujral, OS; Levine, AS, Khovanov homology and cobordisms between split links, Journal of Topology, vol. 15 no. 3 (September, 2022), pp. 973-1016 [doi]  [abs]

Li, Bowen

  1. H. Ammari, B. Li, H. Li, J. Zou, Fano resonances in all-dielectric electromagnetic metasurfaces (November, 2022) [arXiv:2211.03224]
  2. Ammari, H; Li, B; Zou, J, Mathematical analysis of electromagnetic scattering by dielectric nanoparticles with high refractive indices, Transactions of the American Mathematical Society, vol. 376 no. 1 (October, 2022), pp. 39-90, American Mathematical Society [html], [doi]
  3. B. Li, J. Lu, Interpolation between modified logarithmic Sobolev and Poincare inequalities for quantum Markovian dynamics (July, 2022) [arXiv:2207.06422]
  4. B. Li, J. Lu, Z. Yu, Vector-wise Joint Diagonalization of Almost Commuting Matrices (May, 2022) [arXiv:2205.15519]

Liu, Jian-Guo

  1. Wang, Y; Li, X; Konanur, M; Konkel, B; Seyferth, E; Brajer, N; Liu, J-G; Bashir, MR; Lafata, KJ, Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality., Med Phys, vol. 50 no. 6 (June, 2023), pp. 3526-3537 [doi]  [abs] [reputed journal]
  2. Dou, X; Liu, JG; 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, vol. 28 no. 3 (March, 2023), pp. 1964-1992, American Institute of Mathematical Sciences (AIMS) [doi]  [abs]
  3. Gao, Y; Li, T; Li, X; Liu, JG, TRANSITION PATH THEORY FOR LANGEVIN DYNAMICS ON MANIFOLDS: OPTIMAL CONTROL AND DATA-DRIVEN SOLVER, Multiscale Modeling & Simulation, vol. 21 no. 1 (March, 2023), pp. 1-33, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  4. Qi, D; Liu, J-G, A random batch method for efficient ensemble forecasts of multiscale turbulent systems., Chaos (Woodbury, N.Y.), vol. 33 no. 2 (February, 2023), pp. 023113 [doi]  [abs]
  5. Gao, Y; Liu, JG; Wu, N, Data-driven efficient solvers for Langevin dynamics on manifold in high dimensions, Applied and Computational Harmonic Analysis, vol. 62 (January, 2023), pp. 261-309 [doi]  [abs]
  6. Liu, JG; Tang, Y; Zhao, Y, ON THE EQUILIBRIUM OF THE POISSON-NERNST-PLANCK-BIKERMANN MODEL EQUIPPING WITH THE STERIC AND CORRELATION EFFECTS, Communications in Mathematical Sciences, vol. 21 no. 2 (January, 2023), pp. 485-515 [doi]  [abs]
  7. Gao, Y; Liu, JG, Random Walk Approximation for Irreversible Drift-Diffusion Process on Manifold: Ergodicity, Unconditional Stability and Convergence, Communications in Computational Physics, vol. 34 no. 1 (January, 2023), pp. 132-172 [doi]  [abs]
  8. 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] [high impact paper]
  9. Craig, K; Liu, JG; Lu, J; Marzuola, JL; Wang, L, A proximal-gradient algorithm for crystal surface evolution, Numerische Mathematik, vol. 152 no. 3 (November, 2022), pp. 631-662 [doi]  [abs] [reputed journal]
  10. Li, L; Liu, JG; Tang, Y, Some Random Batch Particle Methods for the Poisson-Nernst-Planck and Poisson-Boltzmann Equations, Communications in Computational Physics, vol. 32 no. 1 (July, 2022), pp. 41-82, Global Science Press [doi]  [abs] [reputed journal]
  11. 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] [reputed journal]
  12. 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] [high impact paper]
  13. 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] [reputed journal]
  14. 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] [reputed journal]
  15. Li, L; Liu, JG; 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 (January, 2022), pp. 222-258, Global Science Press [doi]  [abs] [reputed journal]

Lu, Jianfeng

  1. Cao, Y; Lu, J; Wang, L, On Explicit L2 -Convergence Rate Estimate for Underdamped Langevin Dynamics, Archive for Rational Mechanics and Analysis, vol. 247 no. 5 (October, 2023) [doi]  [abs]
  2. Wang, M; Lu, J, Neural Network-Based Variational Methods for Solving Quadratic Porous Medium Equations in High Dimensions, Communications in Mathematics and Statistics, vol. 11 no. 1 (March, 2023), pp. 21-57 [doi]  [abs]
  3. Bierman, J; Li, Y; Lu, J, Improving the Accuracy of Variational Quantum Eigensolvers with Fewer Qubits Using Orbital Optimization., Journal of Chemical Theory and Computation, vol. 19 no. 3 (February, 2023), pp. 790-798 [doi]  [abs]
  4. Cai, Z; Lu, J; Yang, S, NUMERICAL ANALYSIS FOR INCHWORM MONTE CARLO METHOD: SIGN PROBLEM AND ERROR GROWTH, Mathematics of Computation, vol. 92 no. 341 (January, 2023), pp. 1141-1209, American Mathematical Society (AMS) [doi]  [abs]
  5. Chen, Z; Lu, J; Lu, Y; Zhou, S, A REGULARITY THEORY FOR STATIC SCHRÖDINGER EQUATIONS ON R d IN SPECTRAL BARRON SPACES, Siam Journal on Mathematical Analysis, vol. 55 no. 1 (January, 2023), pp. 557-570 [doi]  [abs]
  6. Chen, Z; Lu, J; Qian, H; Wang, X; Yin, W, HeteRSGD: Tackling Heterogeneous Sampling Costs via Optimal Reweighted Stochastic Gradient Descent, Proceedings of Machine Learning Research, vol. 206 (January, 2023), pp. 10732-10781  [abs]
  7. Lee, H; Lu, J; Tan, Y, Convergence of score-based generative modeling for general data distributions, Proceedings of Machine Learning Research, vol. 201 (January, 2023), pp. 946-985  [abs]
  8. Chen, Z; Li, Y; Lu, J, ON THE GLOBAL CONVERGENCE OF RANDOMIZED COORDINATE GRADIENT DESCENT FOR NONCONVEX OPTIMIZATION*, Siam Journal on Optimization, vol. 33 no. 2 (January, 2023), pp. 713-738 [doi]  [abs]
  9. Sachs, M; Sen, D; Lu, J; Dunson, D, Posterior Computation with the Gibbs Zig-Zag Sampler, Bayesian Analysis, vol. 18 no. 3 (January, 2023), pp. 909-927 [doi]  [abs]
  10. Huang, H; Landsberg, JM; Lu, J, GEOMETRY OF BACKFLOW TRANSFORMATION ANSATZE FOR QUANTUM MANY-BODY FERMIONIC WAVEFUNCTIONS, Communications in Mathematical Sciences, vol. 21 no. 5 (January, 2023), pp. 1447-1453 [doi]  [abs]
  11. Holst, M; Hu, H; Lu, J; Marzuola, JL; Song, D; Weare, J, Symmetry Breaking and the Generation of Spin Ordered Magnetic States in Density Functional Theory Due to Dirac Exchange for a Hydrogen Molecule, Journal of Nonlinear Science, vol. 32 no. 6 (December, 2022) [doi]  [abs]
  12. Craig, K; Liu, JG; Lu, J; Marzuola, JL; Wang, L, A proximal-gradient algorithm for crystal surface evolution, Numerische Mathematik, vol. 152 no. 3 (November, 2022), pp. 631-662 [doi]  [abs]
  13. 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]
  14. Barthel, T; Lu, J; Friesecke, G, On the closedness and geometry of tensor network state sets, Letters in Mathematical Physics, vol. 112 no. 4 (August, 2022) [doi]  [abs]
  15. 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]
  16. Lu, J; Steinerberger, S, Neural collapse under cross-entropy loss, Applied and Computational Harmonic Analysis, vol. 59 (July, 2022), pp. 224-241 [doi]  [abs]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. Chen, S; Li, Q; Lu, J; Wright, SJ, MANIFOLD LEARNING AND NONLINEAR HOMOGENIZATION, Multiscale Modeling & Simulation, vol. 20 no. 3 (January, 2022), pp. 1093-1126 [doi]  [abs]
  27. Lu, Y; Chen, H; Lu, J; Ying, L; Blanchet, J, MACHINE LEARNING FOR ELLIPTIC PDES: FAST RATE GENERALIZATION BOUND, NEURAL SCALING LAW AND MINIMAX OPTIMALITY, Iclr 2022 10th International Conference on Learning Representations (January, 2022)  [abs]
  28. Lee, H; Lu, J; Tan, Y, Convergence for score-based generative modeling with polynomial complexity, Advances in Neural Information Processing Systems, vol. 35 (January, 2022), ISBN 9781713871088  [abs]

Luo, Xiaoyutao

  1. Kiselev, A; Luo, X, Illposedness of C2 Vortex Patches, Archive for Rational Mechanics and Analysis, vol. 247 no. 3 (June, 2023), Springer Science and Business Media LLC [doi]  [abs]
  2. Kiselev, A; Luo, X, On Nonexistence of Splash Singularities for the α -SQG Patches, Journal of Nonlinear Science, vol. 33 no. 2 (April, 2023), Springer Science and Business Media LLC [doi]  [abs]
  3. Cheskidov, A; Luo, X, Sharp nonuniqueness for the Navier–Stokes equations, Inventiones Mathematicae, vol. 229 no. 3 (September, 2022), pp. 987-1054 [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. Autry, E; Carter, D; Herschlag, GJ; Hunter, Z; Mattingly, JC, METROPOLIZED FOREST RECOMBINATION FOR MONTE CARLO SAMPLING OF GRAPH PARTITIONS, Siam Journal on Applied Mathematics, vol. 83 no. 4 (August, 2023), pp. 1366-1391, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  2. Herzog, DP; Mattingly, JC; Nguyen, HD, Gibbsian dynamics and the generalized Langevin equation, Electronic Journal of Probability, vol. 28 (January, 2023) [doi]  [abs]
  3. Zhao, Z; Hettle, C; Gupta, S; Mattingly, JC; Randall, D; Herschlag, GJ, Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan, Acm International Conference Proceeding Series (October, 2022), ISBN 9781450394772 [doi]  [abs]
  4. Mattingly, JC; Romito, M; Su, L, The Gaussian structure of the singular stochastic Burgers equation, Forum of Mathematics, Sigma, vol. 10 (September, 2022), Cambridge University Press (CUP) [doi]  [abs]

McPhail-Snyder, Calvin

  1. McPhail-Snyder, C, Hyperbolic structures on link complements, octahedral decompositions, and quantum SL₂ (March, 2022) [doi]

Mukherjee, Sayan

  1. Mubayi, D; Mukherjee, S, Triangles in graphs without bipartite suspensions, Discrete Mathematics, vol. 346 no. 6 (June, 2023) [doi]  [abs]
  2. Shi, A; Berchuck, SI; Jammal, AA; Singh, G; Hunt, S; Roche, K; Mukherjee, S; Medeiros, FA, Identifying Risk Factors for Blindness From Glaucoma at First Presentation to a Tertiary Clinic., American Journal of Ophthalmology, vol. 250 (June, 2023), pp. 130-137 [doi]  [abs]
  3. Liu, X; Mukherjee, S, Stability theorems for some Kruskal–Katona type results, European Journal of Combinatorics, vol. 110 (May, 2023) [doi]  [abs]
  4. Lahkar, R; Mukherjee, S; Roy, S, The logit dynamic in supermodular games with a continuum of strategies: A deterministic approximation approach, Games and Economic Behavior, vol. 139 (May, 2023), pp. 133-160 [doi]  [abs]
  5. Fraiman, N; Mukherjee, S; Thoppe, G, The Shadow Knows: Empirical Distributions of Minimum Spanning Acycles and Persistence Diagrams of Random Complexes, Discrete Analysis, vol. 2023 (January, 2023) [doi]  [abs]
  6. Caprio, M; Mukherjee, S, Ergodic theorems for dynamic imprecise probability kinematics, International Journal of Approximate Reasoning, vol. 152 (January, 2023), pp. 325-343 [doi]  [abs]
  7. Kirveslahti, H; Mukherjee, S, Representing fields without correspondences: the lifted Euler characteristic transform, Journal of Applied and Computational Topology (January, 2023) [doi]  [abs]
  8. He, S; Mukherjee, S, Exploration of stochastic dynamics and complexity in an epidemic system, The European Physical Journal Special Topics, vol. 231 no. 18-20 (December, 2022), pp. 3281-3287 [doi]  [abs]
  9. Curry, J; Mukherjee, S; Turner, K, HOW MANY DIRECTIONS DETERMINE A SHAPE AND OTHER SUFFICIENCY RESULTS FOR TWO TOPOLOGICAL TRANSFORMS, Transactions of the American Mathematical Society, Series B, vol. 9 no. 32 (October, 2022), pp. 1006-1043 [doi]  [abs]
  10. 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]
  11. 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]
  12. 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]
  13. Caprio, M; Aveni, A; Mukherjee, S, Concerning three classes of non-Diophantine arithmetics, Involve, a Journal of Mathematics, vol. 15 no. 5 (March, 2022), pp. 763-774 [doi]  [abs]
  14. 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]
  15. Mukherjee, S, A Grover Search-Based Algorithm for the List Coloring Problem, Ieee Transactions on Quantum Engineering, vol. 3 (January, 2022) [doi]  [abs]
  16. Vejdemo-Johansson, M; Mukherjee, S, MULTIPLE HYPOTHESIS TESTING WITH PERSISTENT HOMOLOGY, Foundations of Data Science, vol. 4 no. 4 (January, 2022), pp. 667-705 [doi]  [abs]

Nelson, Anna C

  1. Nelson, AC; Fogelson, AL, Towards understanding the effect of fibrinogen interactions on fibrin gel structure., Physical Review. E, vol. 107 no. 2-1 (February, 2023), pp. 024413, American Physical Society (APS) [doi]  [abs]
  2. 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]

Ng, Lenhard L.

  1. Casals, R; Ng, L, Braid loops with infinite monodromy on the Legendrian contact DGA, Journal of Topology, vol. 15 no. 4 (December, 2022), pp. 1927-2016, WILEY [doi]  [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, vol. 50 no. 6 (2022), pp. 2133-2177, Institute of Mathematical Statistics [doi]

Pfister, Henry

  1. Coskun, MC; Liva, G; Graell I Amat, A; Lentmaier, M; Pfister, HD, Successive Cancellation Decoding of Single Parity-Check Product Codes: Analysis and Improved Decoding, Ieee Transactions on Information Theory, vol. 69 no. 2 (February, 2023), pp. 823-841, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  2. 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]
  3. 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]
  4. 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]
  5. Brandsen, S; Stubbs, KD; Pfister, HD, Reinforcement Learning with Neural Networks for Quantum Multiple Hypothesis Testing, Quantum, vol. 6 (January, 2022) [doi]  [abs]
  6. Brandsen, S; Mandal, A; Pfister, HD, Belief Propagation with Quantum Messages for Symmetric Classical-Quantum Channels, 2022 Ieee Information Theory Workshop, Itw 2022 (January, 2022), pp. 494-499, ISBN 9781665483414 [doi]  [abs]

Pierce, Lillian B.

  1. An, C; Chu, R; Pierce, LB, Counterexamples for High-Degree Generalizations of the Schrödinger Maximal Operator, International Mathematics Research Notices, vol. 2023 no. 10 (May, 2023), pp. 8371-8418, Oxford University Press (OUP) [doi]  [abs]
  2. Browning, T; Pierce, LB; Schindler, D, Generalised quadratic forms over totally real number fields (December, 2022)
  3. Gressman, PT; Pierce, LB; Roos, J; Yung, P-L, A new type of superorthogonality (December, 2022)
  4. Bonolis, D; Pierce, LB, Application of a polynomial sieve: beyond separation of variables (September, 2022)  [abs]
  5. Bucur, A; Cojocaru, AC; Lalín, MN; Pierce, LB, Geometric generalizations of the square sieve, with an application to cyclic covers, Mathematika (2022), WILEY  [abs]
  6. Gressman, PT; Guo, S; Pierce, LB; Roos, J; Yung, P-L, On the strict majorant property in arbitrary dimensions, The Quarterly Journal of Mathematics (2022), Oxford University Press  [abs]
  7. Pierce, L; Anderson, T; Maldague, D, On polynomial Carleson operators along quadratic hypersurfaces (2022)

Plesser, M. Ronen

  1. Bertolini, M; Melnikov, IV; Plesser, MR, Fixed points of (0,2) Landau-Ginzburg renormalization group flows and the chiral algebra, Journal of High Energy Physics, vol. 2022 no. 9 (September, 2022), ISSN 1029-8479 [doi]  [abs]

Randles, Amanda

  1. Feiger, B; Jensen, CW; Bryner, BS; Segars, WP; Randles, A, Modeling the effect of patient size on cerebral perfusion during veno-arterial extracorporeal membrane oxygenation., Perfusion (July, 2023), pp. 2676591231187962 [doi]  [abs]
  2. Roychowdhury, S; Draeger, EW; Randles, A, Establishing metrics to quantify spatial similarity in spherical and red blood cell distributions, Journal of Computational Science, vol. 71 (July, 2023) [doi]  [abs]
  3. Tanade, C; Putney, S; Randles, A, Establishing massively parallel models to examine the influence of cell heterogeneity on tumor growth, Journal of Computational Science, vol. 71 (July, 2023) [doi]  [abs]
  4. Shi, H; Vardhan, M; Randles, A, The Role of Immersion for Improving Extended Reality Analysis of Personalized Flow Simulations., Cardiovascular Engineering and Technology, vol. 14 no. 2 (April, 2023), pp. 194-203 [doi]  [abs]
  5. Pepona, M; Gounley, J; Randles, A, Effect of constitutive law on the erythrocyte membrane response to large strains, Computers & Mathematics With Applications, vol. 132 (February, 2023), pp. 145-160 [doi]  [abs]
  6. Ladd, W; Jensen, C; Vardhan, M; Ames, J; Hammond, JR; Draeger, EW; Randles, A, Optimizing Cloud Computing Resource Usage for Hemodynamic Simulation, Proceedings 2023 Ieee International Parallel and Distributed Processing Symposium, Ipdps 2023 (January, 2023), pp. 568-578, ISBN 9798350337662 [doi]  [abs]
  7. Puleri, DF; Martin, AX; Randles, A, Distributed Acceleration of Adhesive Dynamics Simulations, Acm International Conference Proceeding Series (September, 2022), pp. 37-45, ISBN 9781450397995 [doi]  [abs]
  8. 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]
  9. 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]
  10. 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]
  11. 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]
  12. Tanade, C; Chen, SJ; Leopold, JA; Randles, A, Analysis identifying minimal governing parameters for clinically accurate in silico fractional flow reserve., Frontiers in Medical Technology, vol. 4 (January, 2022), pp. 1034801 [doi]  [abs]
  13. 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]
  14. 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]
  15. 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]
  16. Puleri, DF; Roychowdhury, S; Balogh, P; Gounley, J; Draeger, EW; Ames, J; Adebiyi, A; Chidyagwai, S; Hernandez, B; Lee, S; Moore, SV; Vetter, JS; Randles, A, High Performance Adaptive Physics Refinement to Enable Large-Scale Tracking of Cancer Cell Trajectory, Proceedings Ieee International Conference on Cluster Computing, Iccc, vol. 2022-September (January, 2022), pp. 230-242, ISBN 9781665498562 [doi]  [abs]
  17. Vardhan, M; Shi, H; Urick, D; Patel, M; Leopold, JA; Randles, A, The role of extended reality for planning coronary artery bypass graft surgery, Proceedings 2022 Ieee Visualization Conference Short Papers, Vis 2022 (January, 2022), pp. 115-119, ISBN 9781665488129 [doi]  [abs]

Reed, Michael C.

  1. Kim, R; Nijhout, HF; Reed, MC, Mathematical insights into the role of dopamine signaling in circadian entrainment., Mathematical Biosciences, vol. 356 (February, 2023), pp. 108956 [doi]  [abs]
  2. Berger, SN; Baumberger, B; Samaranayake, S; Hersey, M; Mena, S; Bain, I; Duncan, W; Reed, MC; Nijhout, HF; Best, J; Hashemi, P, An In Vivo Definition of Brain Histamine Dynamics Reveals Critical Neuromodulatory Roles for This Elusive Messenger., International Journal of Molecular Sciences, vol. 23 no. 23 (November, 2022), pp. 14862 [doi]  [abs]
  3. 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]
  4. 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]

Regan, Margaret H.

  1. Rotzoll, M; Regan, MH; Husty, ML; Hayes, MJD, Kinematic geometry of spatial RSSR mechanisms, Mechanism and Machine Theory, vol. 185 (July, 2023) [doi]  [abs]
  2. 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]

Rossman, Benjamin

  1. Kush, D; Rossman, B, TREE-DEPTH AND THE FORMULA COMPLEXITY OF SUBGRAPH ISOMORPHISM, Siam Journal on Computing, vol. 52 no. 1 (January, 2023), pp. 273-325 [doi]  [abs]
  2. He, W; Rossman, B, Symmetric Formulas for Products of Permutations, Leibniz International Proceedings in Informatics, Lipics, vol. 251 (January, 2023), ISBN 9783959772631 [doi]  [abs]
  3. Cavalar, BP; Kumar, M; Rossman, B, Monotone Circuit Lower Bounds from Robust Sunflowers., Algorithmica, vol. 84 no. 12 (January, 2022), pp. 3655-3685 [doi]  [abs]

Rudin, Cynthia D.

  1. Hahn, S; Zhu, R; Mak, S; Rudin, C; Jiang, Y, An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation, Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining (August, 2023), pp. 4089-4099, ISBN 9798400701030 [doi]  [abs]
  2. Parikh, H; Hoffman, K; Sun, H; Zafar, SF; Ge, W; Jing, J; Liu, L; Sun, J; Struck, A; Volfovsky, A; Rudin, C; Westover, MB, Effects of epileptiform activity on discharge outcome in critically ill patients in the USA: a retrospective cross-sectional study., The Lancet Digital Health, vol. 5 no. 8 (August, 2023), pp. e495-e502 [doi]  [abs]
  3. Peloquin, J; Kirillova, A; Rudin, C; Brinson, LC; Gall, K, Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning, Materials & Design, vol. 232 (August, 2023) [doi]  [abs]
  4. Peloquin, J; Kirillova, A; Mathey, E; Rudin, C; Brinson, LC; Gall, K, Tensile performance data of 3D printed photopolymer gyroid lattices., Data in Brief, vol. 49 (August, 2023), pp. 109396 [doi]  [abs]
  5. Zhang, R; Xin, R; Seltzer, M; Rudin, C, Optimal Sparse Regression Trees, Proceedings of the 37th Aaai Conference on Artificial Intelligence, Aaai 2023, vol. 37 (June, 2023), pp. 11270-11279, ISBN 9781577358800  [abs]
  6. 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, vol. 39 no. 2 (June, 2023), pp. 519-581 [doi]  [abs]
  7. Ou, YJ; Barnett, AJ; Mitra, A; Schwartz, FR; Chen, C; Grimm, L; Lo, JY; Rudin, C, A user interface to communicate interpretable AI decisions to radiologists, Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 12467 (January, 2023), ISBN 9781510660397 [doi]  [abs]
  8. Lanners, Q; Parikh, H; Volfovsky, A; Rudin, C; Page, D, Variable Importance Matching for Causal Inference, Proceedings of Machine Learning Research, vol. 216 (January, 2023), pp. 1174-1184  [abs]
  9. Garrett, BL; Rudin, C, The Right to a Glass Box: Rethinking the Use of Artificial Intelligence in Criminal Justice, Cornell Law Review (2023)
  10. Rudin, C, Why black box machine learning should be avoided for high-stakes decisions, in brief, Nature Reviews Methods Primers, vol. 2 no. 1 (December, 2022) [doi]
  11. Chen, Z; Ogren, A; Daraio, C; Brinson, LC; Rudin, C, How to see hidden patterns in metamaterials with interpretable machine learning, Extreme Mechanics Letters, vol. 57 (November, 2022) [doi]  [abs]
  12. Behrouz, A; Lécuyer, M; Rudin, C; Seltzer, M, Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design., Ceur Workshop Proceedings, vol. 3318 (October, 2022), pp. 26  [abs]
  13. Parikh, H; Rudin, C; Volfovsky, A, MALTS: Matching After Learning to Stretch, Journal of Machine Learning Research, vol. 23 (August, 2022)  [abs]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. Liu, J; Zhong, C; Seltzer, M; Rudin, C, Fast Sparse Classification for Generalized Linear and Additive Models., Proceedings of Machine Learning Research, vol. 151 (March, 2022), pp. 9304-9333  [abs]
  19. 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]
  20. 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]
  21. Li, C; Rudin, C; McCormick, TH, Rethinking Nonlinear Instrumental Variable Models through Prediction Validity, Journal of Machine Learning Research, vol. 23 (January, 2022)  [abs]
  22. McTavish, H; Zhong, C; Achermann, R; Karimalis, I; Chen, J; Rudin, C; Seltzer, M, Fast Sparse Decision Tree Optimization via Reference Ensembles., Proceedings of the ... Aaai Conference on Artificial Intelligence. Aaai Conference on Artificial Intelligence, vol. 36 no. 9 (January, 2022), pp. 9604-9613 [doi]  [abs]
  23. 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]
  24. Wang, ZJ; Zhong, C; Xin, R; Takagi, T; Chen, Z; Chau, DH; Rudin, C; Seltzer, M, TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization, Proceedings 2022 Ieee Visualization Conference Short Papers, Vis 2022 (January, 2022), pp. 60-64, ISBN 9781665488129 [doi]  [abs]
  25. Liu, J; Zhong, C; Li, B; Seltzer, M; Rudin, C, FasterRisk: Fast and Accurate Interpretable Risk Scores, Advances in Neural Information Processing Systems, vol. 35 (January, 2022), ISBN 9781713871088  [abs]
  26. Lobo, E; Singh, H; Petrik, M; Rudin, C; Lakkaraju, H, Data Poisoning Attacks on Off-Policy Policy Evaluation Methods, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, Uai 2022 (January, 2022), pp. 1264-1274, ISBN 9781713863298  [abs]
  27. Xin, R; Zhong, C; Chen, Z; Takagi, T; Seltzer, M; Rudin, C, Exploring the Whole Rashomon Set of Sparse Decision Trees, Advances in Neural Information Processing Systems, vol. 35 (January, 2022), ISBN 9781713871088  [abs]

Ryser, Marc D.

  1. Lange, J; Zhao, Y; Gogebakan, KC; Olivas-Martinez, A; Ryser, MD; Gard, CC; Etzioni, R, Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure., Stat Methods Med Res, vol. 32 no. 6 (June, 2023), pp. 1053-1063 [doi]  [abs]
  2. Byng, D; Thomas, SM; Rushing, CN; Lynch, T; McCarthy, A; Francescatti, AB; Frank, ES; Partridge, AH; Thompson, AM; Retèl, VP; van Harten, WH; Grimm, LJ; Hyslop, T; Hwang, ES; Ryser, MD, Surveillance Imaging after Primary Diagnosis of Ductal Carcinoma in Situ., Radiology, vol. 307 no. 1 (April, 2023), pp. e221210 [doi]  [abs]
  3. Etzioni, R; Gulati, R; Owens, L; Lange, J; Ryser, MD, Opportunity for interception as a driver of benefit in cancer early detection: implications for multi-cancer early detection testing., Cancer Prevention Research, vol. 16 no. 1 (2023), pp. 6-6
  4. 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]
  5. 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]
  6. 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]  [abs]
  7. 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]
  8. 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]
  9. Schmitz, RS; van den Belt-Dusebout, AW; Clements, K; Ren, Y; Cresta, C; Timbres, J; Liu, Y-H; Byng, D; Lynch, T; Menegaz, B; Collyar, D; Hyslop, T; Schaapveld, M; Sawyer, E; Hwang, SE; Thompson, A; Ryser, MD; Wesseling, J; Lips, EH; Schmidt, MK, Subsequent invasive breast cancer risk after DCIS treatment in multinational PRECISION consortium cohorts comprising 48,576 patients, Cancer Research, vol. 82 no. 12 (2022)

Sapiro, Guillermo

  1. Chen, J; Engelhard, M; Henao, R; Berchuck, S; Eichner, B; Perrin, EM; Sapiro, G; Dawson, G, Enhancing early autism prediction based on electronic records using clinical narratives., J Biomed Inform, vol. 144 (August, 2023), pp. 104390 [doi]  [abs]
  2. Coffman, M; Di Martino, JM; Aiello, R; Carpenter, KLH; Chang, Z; Compton, S; Eichner, B; Espinosa, S; Flowers, J; Franz, L; Perochon, S; Krishnappa Babu, PR; Sapiro, G; Dawson, G, Relationship between quantitative digital behavioral features and clinical profiles in young autistic children., Autism Res, vol. 16 no. 7 (July, 2023), pp. 1360-1374 [doi]  [abs]
  3. Krishnappa Babu, PR; Aikat, V; Di Martino, JM; Chang, Z; Perochon, S; Espinosa, S; Aiello, R; L H Carpenter, K; Compton, S; Davis, N; Eichner, B; Flowers, J; Franz, L; Dawson, G; Sapiro, G, Blink rate and facial orientation reveal distinctive patterns of attentional engagement in autistic toddlers: a digital phenotyping approach., Scientific Reports, vol. 13 no. 1 (May, 2023), pp. 7158 [doi]  [abs]
  4. Isaev, DY; Sabatos-DeVito, M; Di Martino, JM; Carpenter, K; Aiello, R; Compton, S; Davis, N; Franz, L; Sullivan, C; Dawson, G; Sapiro, G, Computer Vision Analysis of Caregiver-Child Interactions in Children with Neurodevelopmental Disorders: A Preliminary Report., J Autism Dev Disord (April, 2023) [doi]  [abs]
  5. Isaev, DY; Vlasova, RM; Di Martino, JM; Stephen, CD; Schmahmann, JD; Sapiro, G; Gupta, AS, Uncertainty of Vowel Predictions as a Digital Biomarker for Ataxic Dysarthria., Cerebellum (London, England) (April, 2023) [doi]  [abs]
  6. Perochon, S; Matias Di Martino, J; Carpenter, KLH; Compton, S; Davis, N; Espinosa, S; Franz, L; Rieder, AD; Sullivan, C; Sapiro, G; Dawson, G, A tablet-based game for the assessment of visual motor skills in autistic children., Npj Digital Medicine, vol. 6 no. 1 (February, 2023), pp. 17 [doi]  [abs]
  7. Engelhard, MM; Henao, R; Berchuck, SI; Chen, J; Eichner, B; Herkert, D; Kollins, SH; Olson, A; Perrin, EM; Rogers, U; Sullivan, C; Zhu, Y; Sapiro, G; Dawson, G, Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year., Jama Network Open, vol. 6 no. 2 (February, 2023), pp. e2254303 [doi]  [abs]
  8. 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, vol. 64 no. 1 (January, 2023), pp. 156-166 [doi]  [abs]
  9. Solomon, O; Patriat, R; Braun, H; Palnitkar, TE; Moeller, S; Auerbach, EJ; Ugurbil, K; Sapiro, G; Harel, N, Motion robust magnetic resonance imaging via efficient Fourier aggregation., Medical Image Analysis, vol. 83 (January, 2023), pp. 102638 [doi]  [abs]
  10. Babu, PRK; Di Martino, JM; Chang, Z; Perochon, S; Carpenter, KLH; Compton, S; Espinosa, S; Dawson, G; Sapiro, G, Exploring Complexity of Facial Dynamics in Autism Spectrum Disorder., Ieee Transactions on Affective Computing, vol. 14 no. 2 (2023), pp. 919-930 [doi]  [abs]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. Simhal, AK; Carpenter, KLH; Kurtzberg, J; Song, A; Tannenbaum, A; Zhang, L; Sapiro, G; Dawson, G, Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion., Frontiers in Psychiatry, vol. 13 (2022), pp. 1026279 [doi]  [abs]

Schoen, Chadmark L.

  1. Beauville, A; Schoen, C, A Non-Hyperelliptic Curve with Torsion Ceresa Cycle Modulo Algebraic Equivalence, International Mathematics Research Notices, vol. 2023 no. 5 (March, 2023), pp. 3671-3675, Oxford University Press (OUP) [doi]  [abs]

Schott, Sarah

  1. Schott, S; Slate Young, E; Bookman, J; Hash, P, An Examination of Factors that Support Sustainable Cultural and Curricular Change in STEM Teaching and Learning, Journal of Mathematics and Science: Collaborative Explorations, vol. 18 (2022) [doi]  [abs]

Stern, Mark A.

  1. Di Cerbo, LF; Stern, M, Price inequalities and Betti number growth on manifolds without conjugate points, Communications in Analysis and Geometry, vol. 30 no. 2 (January, 2022), pp. 297-334, International Press [doi]  [abs]

Tarokh, Vahid

  1. Soloveychik, I; Tarokh, V, Region selection in Markov random fields: Gaussian case, Journal of Multivariate Analysis, vol. 196 (July, 2023) [doi]  [abs]
  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. Venkatasubramanian, S; Wongkamthong, C; Soltani, M; Kang, B; Gogineni, S; Pezeshki, A; Rangaswamy, M; Tarokh, V, Toward Data-Driven STAP Radar, Proceedings of the Ieee Radar Conference (January, 2022), IEEE [doi]  [abs]
  11. Soloveychik, I; Tarokh, V, Large deviations of convex polyominoes*, Electronic Journal of Probability, vol. 27 (January, 2022) [doi]  [abs]
  12. 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]
  13. Cannella, C; Tarokh, V, Semi-Empirical Objective Functions for MCMC Proposal Optimization, Proceedings International Conference on Pattern Recognition, vol. 2022-August (January, 2022), pp. 4758-4764, ISBN 9781665490627 [doi]  [abs]
  14. Hasan, A; Elkhalil, K; Ng, Y; Pereira, JM; Farsiu, S; Blanchet, J; Tarokh, V, Modeling Extremes with d-max-decreasing Neural Networks, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, Uai 2022 (January, 2022), pp. 759-768, ISBN 9781713863298  [abs]
  15. Le, CP; Dong, J; Soltani, M; Tarokh, V, TASK AFFINITY WITH MAXIMUM BIPARTITE MATCHING IN FEW-SHOT LEARNING, Iclr 2022 10th International Conference on Learning Representations (January, 2022)  [abs]
  16. Le, Q; Diao, E; Wang, X; Anwar, A; Tarokh, V; Ding, J, Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders, Conference Record Asilomar Conference on Signals, Systems and Computers, vol. 2022-October (January, 2022), pp. 1157-1163, ISBN 9781665459068 [doi]  [abs]
  17. Dong, J; Ren, S; Deng, Y; Khatib, O; Malof, J; Soltani, M; Padilla, W; Tarokh, V, BLASCHKE PRODUCT NEURAL NETWORK (BPNN): A PHYSICS-INFUSED NEURAL NETWORK FOR PHASE RETRIEVAL OF MEROMORPHIC FUNCTIONS, Iclr 2022 10th International Conference on Learning Representations (January, 2022)  [abs]

Viel, Shira

  1. Tackett, M; Viel, S; Manturuk, K, A validation of the short-form classroom community scale for undergraduate mathematics and statistics students, Journal of University Teaching and Learning Practice, vol. 20 no. 1 (January, 2023), pp. 1-17 [doi]  [abs]
  2. Spencer, D; Fenn, M; Willis, C; Shen, Y; Viel, S, Utilizing a Blended + Flipped Learning Approach in a Calculus for Life and Management Sciences Classroom, Primus (November, 2022), Taylor and Francis [doi]

Wagner, Alexander Y

  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]

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]

Wei, Fan

  1. Przybyło, J; Wei, F, On the Asymptotic Confirmation of the Faudree–Lehel Conjecture for General Graphs, Combinatorica, vol. 43 no. 4 (August, 2023), pp. 791-826 [doi]  [abs]
  2. Liu, CH; Wei, F, Phase transition of degeneracy in minor-closed families, Advances in Applied Mathematics, vol. 146 (May, 2023) [doi]  [abs]
  3. Alon, N; Wei, F, Irregular subgraphs, Combinatorics, Probability and Computing, vol. 32 no. 2 (March, 2023), pp. 269-283, Cambridge University Press (CUP) [doi]  [abs]
  4. Conlon, D; Fox, J; Sudakov, B; Wei, F, Threshold Ramsey multiplicity for paths and even cycles, European Journal of Combinatorics, vol. 107 (January, 2023) [doi]  [abs]
  5. Král’, D; Noel, JA; Norin, S; Volec, J; Wei, F, Non-Bipartite K-Common Graphs, Combinatorica, vol. 42 no. 1 (February, 2022), pp. 87-114 [doi]  [abs]
  6. Conlon, D; Fox, J; Sudakov, B; Wei, F, THRESHOLD RAMSEY MULTIPLICITY FOR ODD CYCLES, Revista De La Union Matematica Argentina, vol. 64 no. 1 (January, 2022), pp. 49-68 [doi]  [abs]

Wickelgren, Kirsten G.

  1. Bachmann, T; Wickelgren, K, On quadratically enriched excess and residual intersections, Journal Fur Die Reine Und Angewandte Mathematik, vol. 2023 no. 802 (September, 2023), pp. 77-123 [doi]  [abs]
  2. 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, vol. 22 no. 2 (March, 2023), pp. 681-746 [doi]  [abs]
  3. Kuhn, N; Mallory, D; Thatte, V; Wickelgren, K, An explicit self-duality, in Stacks Project Expository Collection (SPEC), edited by Belmans, P; Ho, W; de Jong, AJ, vol. 480 (October, 2022), Cambridge University Press, ISBN 1009054856  [abs]
  4. 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]

Witelski, Thomas P.   (search)

  1. Chapman, SJ; Dallaston, MC; Kalliadasis, S; Trinh, PH; Witelski, TP, The role of exponential asymptotics and complex singularities in self-similarity, transitions, and branch merging of nonlinear dynamics, Physica D: Nonlinear Phenomena, vol. 453 (November, 2023) [doi]  [abs]
  2. Bowen, M; King, JR; Witelski, TP, CAUCHY-DIRICHLET PROBLEMS FOR THE POROUS MEDIUM EQUATION, Discrete and Continuous Dynamical Systems Series A, vol. 43 no. 3-4 (March, 2023), pp. 1143-1174, American Institute of Mathematical Sciences (AIMS) [doi]  [abs]
  3. Sanaei, P; Breward, C; Ellis, M; Han, S; Holzer, B; Ji, H; El Kahza, H; Smith, SL; Parsa, S; Reynolds, H; Troy, J; Witelski, T; Zhang, N; Zyskin, M, Evaporation and deposition in porous media (April, 2022) [doi]  [abs]
  4. Kim, R; Witelski, TP, Uncovering the dynamics of a circadian-dopamine model influenced by the light-dark cycle., Mathematical Biosciences, vol. 344 (February, 2022), pp. 108764, Elsevier [doi]  [abs]

Wong, Biji

  1. Petkova, I; Wong, B, Twisted Mazur Pattern Satellite Knots & Bordered Floer Theory, The Michigan Mathematical Journal, vol. 73 no. 2 (May, 2023), pp. 255-304, Michigan Mathematical Journal [doi]  [abs]

Wu, Hau-Tieng

  1. Wang, S-C; Ting, C-K; Chen, C-Y; Liu, C; Lin, N-C; Loong, C-C; Wu, H-T; Lin, Y-T, Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients' acuity and predicting short term outcomes., Journal of Clinical Monitoring and Computing (July, 2023) [doi]  [abs]
  2. Eid, A-M; Elgamal, M; Gonzalez-Fiol, A; Shelley, KH; Wu, H-T; Alian, AA, Using the ear photoplethysmographic waveform as an early indicator of central hypovolemia in healthy volunteers utilizing LBNP induced hypovolemia model., Physiological Measurement, vol. 44 no. 5 (July, 2023) [doi]  [abs]
  3. Wu, H-T; Harezlak, J, Application of de-shape synchrosqueezing to estimate gait cadence from a single-sensor accelerometer placed in different body locations., Physiological Measurement, vol. 44 no. 5 (May, 2023) [doi]  [abs]
  4. Liu, GR; Sheu, YC; Wu, HT, CENTRAL AND NONCENTRAL LIMIT THEOREMS ARISING FROM THE SCATTERING TRANSFORM AND ITS NEURAL ACTIVATION GENERALIZATION, Siam Journal on Mathematical Analysis, vol. 55 no. 2 (April, 2023), pp. 1170-1213 [doi]  [abs]
  5. Wang, YG; Womersley, RS; Wu, HT; Yu, WH, Numerical computation of triangular complex spherical designs with small mesh ratio, Journal of Computational and Applied Mathematics, vol. 421 (March, 2023) [doi]  [abs]
  6. Ding, X; Wu, HT, Impact of Signal-to-Noise Ratio and Bandwidth on Graph Laplacian Spectrum From High-Dimensional Noisy Point Cloud, Ieee Transactions on Information Theory, vol. 69 no. 3 (March, 2023), pp. 1899-1931, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  7. Steinerberger, S; Wu, HT, Fundamental component enhancement via adaptive nonlinear activation functions, Applied and Computational Harmonic Analysis, vol. 63 (March, 2023), pp. 135-143 [doi]  [abs]
  8. 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, vol. 37 no. 1 (February, 2023), pp. 127-137 [doi]  [abs]
  9. 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]
  10. Colominas, MA; Wu, HT, An Iterative Warping and Clustering Algorithm to Estimate Multiple Wave-Shape Functions From a Nonstationary Oscillatory Signal, Ieee Transactions on Signal Processing, vol. 71 (January, 2023), pp. 701-712, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  11. Young, AL; van den Boom, W; Schroeder, RA; Krishnamoorthy, V; Raghunathan, K; Wu, H-T; Dunson, DB, Mutual information: Measuring nonlinear dependence in longitudinal epidemiological data., Plos One, vol. 18 no. 4 (2023), pp. e0284904 [doi]  [abs]
  12. Shen, C; Wu, HT, Scalability and robustness of spectral embedding: landmark diffusion is all you need, Information and Inference, vol. 11 no. 4 (December, 2022), pp. 1527-1595 [doi]  [abs]
  13. Gavish, M; Su, PC; Talmon, R; Wu, HT, Optimal recovery of precision matrix for Mahalanobis distance from high-dimensional noisy observations in manifold learning, Information and Inference, vol. 11 no. 4 (December, 2022), pp. 1173-1202 [doi]  [abs]
  14. 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]
  15. 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]
  16. Sourisseau, M; Wu, HT; Zhou, Z, ASYMPTOTIC ANALYSIS OF SYNCHROSQUEEZING TRANSFORM—TOWARD STATISTICAL INFERENCE WITH NONLINEAR-TYPE TIME-FREQUENCY ANALYSIS, The Annals of Statistics, vol. 50 no. 5 (October, 2022), pp. 2694-2712 [doi]  [abs]
  17. Chen, PL; Chen, YC; Tu, PH; Liu, TC; Chen, MC; Wu, HT; Yeap, MC; Yeh, CH; Lu, CS; Chen, CC, Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson's disease, Frontiers in Human Neuroscience, vol. 16 (September, 2022) [doi]  [abs]
  18. Alian, A; Lo, YL; Shelley, K; Wu, HT, RECONSIDER PHASE RECONSTRUCTION IN SIGNALS WITH DYNAMIC PERIODICITY FROM THE MODERN SIGNAL PROCESSING PERSPECTIVE, Foundations of Data Science, vol. 4 no. 3 (September, 2022), pp. 355-393 [doi]  [abs]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. Baldazzi, G; Pani, D; Wu, HT, Extraction Algorithm for Morphologically Preserved Non-Invasive Multi-Channel Fetal ECG, Computing in Cardiology, vol. 2022-September (January, 2022), ISBN 9798350300970 [doi]  [abs]
  26. Chew, J; Steach, H; Viswanath, S; Wu, HT; Hirn, M; Needell, D; Vesely, MD; Krishnaswamy, S; Perlmutter, M, THE MANIFOLD SCATTERING TRANSFORM FOR HIGH-DIMENSIONAL POINT CLOUD DATA, Proceedings of Machine Learning Research, vol. 196 (January, 2022), pp. 67-78  [abs]

Xie, Jichun

  1. Gao, Q; Ji, Z; Wang, L; Owzar, K; Li, Q-J; Chan, C; Xie, J, SifiNet: A robust and accurate method to identify feature gene sets and annotate cells., Biorxiv (August, 2023) [doi]  [abs]
  2. Poe, JC; Fang, J; Zhang, D; Lee, MR; DiCioccio, RA; Su, H; Qin, X; Zhang, JY; Visentin, J; Bracken, SJ; Ho, VT; Wang, KS; Rose, JJ; Pavletic, SZ; Hakim, FT; Jia, W; Suthers, AN; Curry-Chisolm, IM; Horwitz, ME; Rizzieri, DA; McManigle, WC; Chao, NJ; Cardones, AR; Xie, J; Owzar, K; Sarantopoulos, S, Single-cell landscape analysis unravels molecular programming of the human B cell compartment in chronic GVHD., Jci Insight, vol. 8 no. 11 (June, 2023), pp. e169732 [doi]  [abs]
  3. Moris, D; Barfield, R; Chan, C; Chasse, S; Stempora, L; Xie, J; Plichta, JK; Thacker, J; Harpole, DH; Purves, T; Lagoo-Deenadayalan, S; Hwang, ES; Kirk, AD, Immune Phenotype and Postoperative Complications following Elective Surgery., Ann Surg (April, 2023) [doi]  [abs]
  4. Li, X; Sung, A; Xie, J, DART: Distance Assisted Recursive Testing, Journal of Machine Learning Research, vol. 24 no. 169 (April, 2023)  [abs]
  5. Pura, JA; Li, X; Chan, C; Xie, J, TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS, The Annals of Applied Statistics, vol. 17 no. 1 (March, 2023), pp. 621-640 [doi]  [abs]
  6. Murdoch, DM; Barfield, R; Chan, C; Towe, SL; Bell, RP; Volkheimer, A; Choe, J; Hall, SA; Berger, M; Xie, J; Meade, CS, Neuroimaging and immunological features of neurocognitive function related to substance use in people with HIV., J Neurovirol, vol. 29 no. 1 (February, 2023), pp. 78-93 [doi]  [abs]
  7. Hou, L; Ji, Z; Wang, J; Xie, J, Editorial: Statistical and computational methods for single-cell sequencing analysis., Frontiers in Genetics, vol. 14 (2023), pp. 1235174 [doi]
  8. Fang, J; Chan, C; Owzar, K; Wang, L; Qin, D; Li, Q-J; Xie, J, Clustering Deviation Index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering., Genome Biology, vol. 23 no. 1 (December, 2022), pp. 269 [doi]  [abs]
  9. SenNet Consortium,, NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health., Nature Aging, vol. 2 no. 12 (December, 2022), pp. 1090-1100 [doi]  [abs]
  10. 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]
  11. 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]

Yu, Jiajia

  1. Huang, H; Yu, J; Chen, J; Lai, R, Bridging mean-field games and normalizing flows with trajectory regularization, Journal of Computational Physics, vol. 487 (August, 2023), pp. 112155-112155, Elsevier BV [doi]
  2. Yu, J; Lai, R; Li, W; Osher, S, A fast proximal gradient method and convergence analysis for dynamic mean field planning, Mathematics of Computation (July, 2023), American Mathematical Society (AMS) [doi]  [abs]
  3. Yu, J; Lai, R; Li, W; Osher, S, Computational mean-field games on manifolds, Journal of Computational Physics, vol. 484 (July, 2023), pp. 112070-112070, Elsevier BV [doi]

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]

Zhang, Shijun

  1. Shen, Z; Yang, H; Zhang, S, Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons, Journal of Machine Learning Research, vol. 23 (September, 2022)  [abs]
  2. Shen, Z; Yang, H; Zhang, S, Optimal approximation rate of ReLU networks in terms of width and depth, Journal De Mathématiques Pures Et Appliquées, vol. 157 (January, 2022), pp. 101-135 [doi]  [abs]
  3. Shen, Z; Yang, H; Zhang, S, Deep Network Approximation in Terms of Intrinsic Parameters, Proceedings of Machine Learning Research, vol. 162 (January, 2022), pp. 19909-19934  [abs]
  4. Shen, Z; Yang, H; Zhang, S, Neural Network Architecture Beyond Width and Depth, Advances in Neural Information Processing Systems, vol. 35 (January, 2022), ISBN 9781713871088  [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]

 

dept@math.duke.edu
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320
Mathematics Home | Arts & Sciences | Duke University