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Mathematics Faculty: Publications since January 2023

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: Theory and Applications, vol. 117 (February, 2024) [doi]  [abs]
  2. Agarwal, PK; Har-Peled, S, Computing Instance-Optimal Kernels in Two Dimensions, Discrete and Computational Geometry (January, 2024) [doi]  [abs]
  3. Agarwal, PK; Geft, T; Halperin, D; Taylor, E, Multi-robot motion planning for unit discs with revolving areas, Computational Geometry: Theory and Applications, vol. 114 (October, 2023) [doi]  [abs]

Akin, Victoria S

  1. Akin, V; Viel, S, Equity in Grading Systems: Moving Away From “Fair” Towards Transparency and Inclusion in Coordinated Calculus Courses, in Justice through the lens of calculus: Framing new possibilities for diversity, equity, and inclusion., edited by Voigt, M; Hagaman, J; Gehrtz, J; Ratliff, B; Alexander, N; Levy, R, vol. 96 (September, 2023), MAA Press
  2. Akin, V; Bookman, J; Braley, E, Modeling Active Learning in Professional Development for Teaching, The journal of faculty development, vol. 37 no. 3 (September, 2023), pp. 28-39, Magna Publications

An, Jing

  1. An, J; Henderson, C; Ryzhik, L, Voting models and semilinear parabolic equations, Nonlinearity, vol. 36 no. 11 (November, 2023) [doi]  [abs]
  2. An, J; Henderson, C; Ryzhik, L, Quantitative Steepness, Semi-FKPP Reactions, and Pushmi-Pullyu Fronts, Archive for Rational Mechanics and Analysis, vol. 247 no. 5 (October, 2023) [doi]  [abs]
  3. Bréchet, P; Papagiannouli, K; An, J; Montúfar, G, Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 3106-3147  [abs]

Aquino, Wilkins

  1. Zhao, Y; Parlak, Z; Yu, W; French, D; Aquino, W; Zauscher, S, Microfluidic QCM enables ultrahigh Q-factor: a new paradigm for in-liquid gravimetric sensing., Microsystems & nanoengineering, vol. 10 no. 1 (August, 2024), pp. 116 [doi]  [abs]
  2. Aquino, W; Desmond, J; Eldred, M; Kurzawski, A; McCormick, C; Sanders, C; Smith, C; Walsh, T, Assessing decision boundaries under uncertainty, Structural and Multidisciplinary Optimization, vol. 67 no. 7 (July, 2024) [doi]  [abs]
  3. Aquino, W; Rouse, J; Bonnet, M, Active design of diffuse acoustic fields in enclosures., The Journal of the Acoustical Society of America, vol. 155 no. 2 (February, 2024), pp. 1297-1307 [doi]  [abs]
  4. Baek, Y; Aquino, W; Mukherjee, S, Generalized Bayes approach to inverse problems with model misspecification., Inverse problems, vol. 39 no. 10 (October, 2023), pp. 105011 [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]

Beale, J. Thomas

  1. Beale, JT; Tlupova, S, Extrapolated regularization of nearly singular integrals on surfaces, Advances in Computational Mathematics, vol. 50 no. 4 (August, 2024) [doi]  [abs]
  2. Beale, JT; Storm, M; Tlupova, S, The adjoint double layer potential on smooth surfaces in R3 and the Neumann problem, Advances in Computational Mathematics, vol. 50 no. 3 (June, 2024) [doi]  [abs]

Beekie, Rajendra W

  1. Beekie, R; Novack, M, Non-conservative Solutions of the Euler- α Equations, Journal of Mathematical Fluid Mechanics, vol. 25 no. 1 (February, 2023) [doi]  [abs]

Bendich, Paul L

  1. Solomon, YE; Bendich, P, Convolutional persistence transforms, Journal of Applied and Computational Topology (January, 2024) [doi]  [abs]
  2. Catanzaro, MJ; Dharna, A; Hineman, J; Polly, JB; McGoff, K; Smith, AD; Bendich, P, Topological Decompositions Enhance Efficiency of Reinforcement Learning, IEEE Aerospace Conference Proceedings (January, 2024) [doi]  [abs]
  3. Jin, Y; McDaniel, R; Tatro, NJ; Catanzaro, MJ; Smith, AD; Bendich, P; Dwyer, MB; Fletcher, PT, Implications of data topology for deep generative models, Frontiers in Computer Science, vol. 6 (January, 2024) [doi]  [abs]
  4. 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]
  5. Solomon, E; Wagner, A; Bendich, P, FROM GEOMETRY TO TOPOLOGY: INVERSE THEOREMS FOR DISTRIBUTED PERSISTENCE, Journal of Computational Geometry, vol. 14 no. 2 Special Issue (January, 2023), pp. 172-196 [doi]  [abs]
  6. Smith, AD; Angeloro, G; Catanzaro, MJ; Patel, N; Bendich, P, Topological Parallax: A Geometric Specification for Deep Perception Models, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [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, vol. 12 no. 2 (June, 2024), pp. 1265-1373, Springer Science and Business Media LLC [doi]  [abs]
  2. Bezemek, Z; Heldman, M, Importance Sampling for the Empirical Measure of Weakly Interacting Diffusions, Applied Mathematics and Optimization, vol. 89 (November, 2023), Springer
  3. Bezemek, ZW; Spiliopoulos, K, Rate of homogenization for fully-coupled McKean-Vlasov SDEs, Stochastics and Dynamics, vol. 23 no. 2 (March, 2023), World Scientific Pub Co Pte Ltd [doi]  [abs]
  4. 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]  [abs]

Bonolis, Dante

  1. Bonolis, D; Browning, T; Huang, Z, Density of rational points on some quadric bundle threefolds., Mathematische annalen, vol. 390 no. 3 (January, 2024), pp. 4123-4207 [doi]  [abs]
  2. Dante, B; Tim, B, Uniform bounds for rational points on hyperelliptic fibrations, Annali della Scuola Normale Superiore di Pisa - Classe di Scienze, vol. 24 no. 1 (January, 2023), pp. 173-204, Scuola Normale Superiore - Edizioni della Normale [doi]  [abs]

Bookman, Jack

  1. Akin, V; Bookman, J; Braley, E, Modeling Active Learning in Professional Development for Teaching, The journal of faculty development, vol. 37 no. 3 (September, 2023), pp. 28-39, Magna Publications

Brailovskaya, Tatiana I

  1. Brailovskaya, T; van Handel, R, Universality and Sharp Matrix Concentration Inequalities, Geometric and Functional Analysis (January, 2024) [doi]  [abs]
  2. Brailovskaya, T; Rácz, MZ, Tree trace reconstruction using subtraces, Journal of Applied Probability, vol. 60 no. 2 (January, 2023), pp. 629-641 [doi]  [abs]

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. Buckmire, R; Beeton, B; Bryant, R; Gouvêa, FQ; Phillips, AV; Sullivan, D; Wolf, M, Michael Spivak: A Memorial, Notices of the American Mathematical Society, vol. 71 no. 6 (June, 2024), pp. 786-795 [doi]
  2. Bryant, R; Cheeger, J; Lima-Filho, P; Rosenberg, J; White, B, The mathematical work of H. Blaine Lawson, Jr., Pure and Applied Mathematics Quarterly, vol. 19 no. 6 (January, 2024), pp. 2627-2662, International Press
  3. Bryant, R, The generality of closed G_2 solitons, edited by Cheng, S-Y; Lima-Filho, P; Yau, SS-T; Yau, S-T, Pure and Applied Mathematics Quarterly, vol. 19 no. 6 (January, 2024), pp. 2827-2840, International Press  [abs]

Buck, Dorothy E.

  1. Tubiana, L; Alexander, GP; Barbensi, A; Buck, D; Cartwright, JHE; Chwastyk, M; Cieplak, M; Coluzza, I; Čopar, S; Craik, DJ; Di Stefano, M; Everaers, R; Faísca, PFN; Ferrari, F; Giacometti, A; Goundaroulis, D; Haglund, E; Hou, YM; Ilieva, N; Jackson, SE; Japaridze, A; Kaplan, N; Klotz, AR; Li, H; Likos, CN; Locatelli, E; López-León, T; Machon, T; Micheletti, C; Michieletto, D; Niemi, A; Niemyska, W; Niewieczerzal, S; Nitti, F; Orlandini, E; Pasquali, S; Perlinska, AP; Podgornik, R; Potestio, R; Pugno, NM; Ravnik, M; Ricca, R; Rohwer, CM; Rosa, A; Smrek, J; Souslov, A; Stasiak, A; Steer, D; Sułkowska, J; Sułkowski, P; Sumners, DWL; Svaneborg, C; Szymczak, P; Tarenzi, T; Travasso, R; Virnau, P; Vlassopoulos, D; Ziherl, P; Žumer, S, Topology in soft and biological matter, Physics Reports, vol. 1075 (July, 2024), pp. 1-137 [doi]  [abs]

Chariker, Christopher L.

  1. L. Chariker, A. De Masi, J. Lebowitz, E. Presutti, Scaling limit of a generalized contact process, J. Stat. Phys, vol. 190 no. 3 (March, 2023), ISSN 1572-9613 [scaling-limit-of-a-generalized-contact-process], [doi]  [abs]

Cheng, Xiuyuan

  1. Repasky, M; Cheng, X; Xie, Y, Neural Stein Critics with Staged L2-Regularization, IEEE Transactions on Information Theory, vol. 69 no. 11 (November, 2023), pp. 7246-7275 [doi]  [abs]
  2. Landa, B; Cheng, X, Robust Inference of Manifold Density and Geometry by Doubly Stochastic Scaling, SIAM Journal on Mathematics of Data Science, vol. 5 no. 3 (September, 2023), pp. 589-614, Society for Industrial & Applied Mathematics (SIAM) [doi]
  3. Lee, J; Xie, Y; Cheng, X, Training Neural Networks for Sequential Change-Point Detection, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2023-June (January, 2023), ISBN 9781728163277 [doi]  [abs]

Ciocanel, Maria-Veronica

  1. Nelson, AC; Rolls, MM; Ciocanel, M-V; McKinley, SA, Minimal Mechanisms of Microtubule Length Regulation in Living Cells., Bulletin of mathematical biology, vol. 86 no. 5 (April, 2024), pp. 58 [doi]  [abs]
  2. 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., Bulletin of mathematical biology, vol. 86 no. 4 (March, 2024), pp. 36 [doi]  [abs]
  3. Topaz, CM; Ning, S; Ciocanel, MV; Bushway, S, Federal criminal sentencing: race-based disparate impact and differential treatment in judicial districts, Humanities and Social Sciences Communications, vol. 10 no. 1 (December, 2023) [doi]  [abs]
  4. Nelson, AC; Rolls, MM; Ciocanel, M-V; McKinley, SA, Minimal Mechanisms of Microtubule Length Regulation in Living Cells (October, 2023)
  5. Ciocanel, M-V; Goldrosen, N; Topaz, C, Quantifying Federal Sentence Disparities with Inferred Sentencing Records, SIAM News Blogs (September, 2023)
  6. 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)
  7. 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?, Mathematical biosciences and engineering : MBE, vol. 20 no. 5 (March, 2023), pp. 9179-9207 [doi]  [abs]
  8. Smith, CM; Goldrosen, N; Ciocanel, M-V; Santorella, R; Topaz, CM; Sen, S, Racial Disparities in Criminal Sentencing Vary Considerably across Federal Judges, Journal of Institutional and Theoretical Economics, vol. 179 (January, 2023), Mohr Siebeck [doi]
  9. 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 : MBE, vol. 20 no. 2 (January, 2023), pp. 3023-3046, American Institute of Mathematical Sciences (AIMS) [doi]  [abs]

Cook, Nicholas A   (search)

  1. Cook, NA; Dembo, A, TYPICAL STRUCTURE OF SPARSE EXPONENTIAL RANDOM GRAPH MODELS, Annals of Applied Probability, vol. 34 no. 3 (June, 2024), pp. 2885-2939 [doi]  [abs]
  2. Cook, NA; Dembo, A; Pham, HT, REGULARITY METHOD AND LARGE DEVIATION PRINCIPLES FOR THE ERDŐS–RÉNYI HYPERGRAPH, Duke Mathematical Journal, vol. 173 no. 5 (April, 2024), pp. 873-946 [doi]  [abs]
  3. 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]

Dasgupta, Samit

  1. Dasgupta, S; Kakde, M, BRUMER–STARK UNITS AND EXPLICIT CLASS FIELD THEORY, Duke Mathematical Journal, vol. 173 no. 8 (January, 2024), pp. 1477-1555 [doi]  [abs]
  2. 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. Duprez, F; Crombin, M; Daubechies, I; Devries, N; Durant, V; El Khalil, M; Audag, N, [Update on manual bronchial clearance techniques (adults and adolescents)]., Revue des maladies respiratoires, vol. 41 no. 1 (January, 2024), pp. 43-50 [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. 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]
  4. 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]
  5. 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]
  6. 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]

Deng, Haohua

  1. Castor, B; Deng, H; Kerr, M; Pearlstein, G, REMARKS ON EIGENSPECTRA OF ISOLATED SINGULARITI, Pacific Journal of Mathematics, vol. 327 no. 1 (January, 2024), pp. 29-54 [doi]  [abs]
  2. Deng, H; Robles, C, Completion of two-parameter period maps by nilpotent orbits (December, 2023)  [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]

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, Jointly stationary solutions of periodic Burgers flow, Journal of Functional Analysis, vol. 287 no. 12 (December, 2024), pp. 110656-110656, Elsevier BV [doi]  [abs]
  2. Dunlap, A; Mourrat, J-C, Sum-of-norms clustering does not separate nearby balls, Journal of Machine Learning Research, vol. 25 no. 143 (April, 2024), pp. 1-40, Microtome Publishing  [abs]
  3. 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]
  4. 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]
  5. 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]  [abs]

Dunson, David B.   (search)

  1. Winter, S; Mahzarnia, A; Anderson, RJ; Han, ZY; Tremblay, J; Stout, JA; Moon, HS; Marcellino, D; Dunson, DB; Badea, A, Brain network fingerprints of Alzheimer's disease risk factors in mouse models with humanized APOE alleles., Magnetic resonance imaging, vol. 114 (December, 2024), pp. 110251 [doi]  [abs]
  2. Huang, J; Morsomme, R; Dunson, D; Xu, J, Detecting changes in the transmission rate of a stochastic epidemic model., Statistics in medicine, vol. 43 no. 10 (May, 2024), pp. 1867-1882 [doi]  [abs]
  3. Melikechi, O; Dunson, DB, Ellipsoid fitting with the Cayley transform., IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol. 72 (January, 2024), pp. 70-83 [doi]  [abs]
  4. Datta, J; Banerjee, S; Dunson, DB, Nonparametric Bayes multiresolution testing for high-dimensional rare events, Journal of Nonparametric Statistics (January, 2024) [doi]  [abs]
  5. Winter, S; Campbell, T; Lin, L; Srivastava, S; Dunson, DB, Emerging Directions in Bayesian Computation, Statistical Science, vol. 39 no. 1 (January, 2024), pp. 62-89 [doi]  [abs]
  6. Zhai, Q; Ye, Z; Li, C; Revie, M; Dunson, DB, Modeling Recurrent Failures on Large Directed Networks, Journal of the American Statistical Association (January, 2024) [doi]  [abs]
  7. Jin, B; Herring, AH; Dunson, D, SPATIAL PREDICTIONS ON PHYSICALLY CONSTRAINED DOMAINS: APPLICATIONS TO ARCTIC SEA SALINITY DATA, Annals of Applied Statistics, vol. 18 no. 2 (January, 2024), pp. 1596-1617 [doi]  [abs]
  8. Papamarkou, T; Skoularidou, M; Palla, K; Aitchison, L; Arbel, J; Dunson, D; Filippone, M; Fortuin, V; Hennig, P; Hernández-Lobato, JM; Hubin, A; Immer, A; Karaletsos, T; Khan, ME; Kristiadi, A; Li, Y; Mandt, S; Nemeth, C; Osborne, MA; Rudner, TGJ; Rügamer, D; Teh, YW; Welling, M; Wilson, AG; Zhang, R, Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI, Proceedings of Machine Learning Research, vol. 235 (January, 2024), pp. 39556-39586  [abs]
  9. Tang, T; Mak, S; Dunson, D, Hierarchical Shrinkage Gaussian Processes: Applications to Computer Code Emulation and Dynamical System Recovery, SIAM-ASA Journal on Uncertainty Quantification, vol. 12 no. 4 (January, 2024), pp. 1085-1112 [doi]  [abs]
  10. 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, vol. 79 no. 4 (December, 2023), pp. 2987-2997 [doi]  [abs]
  11. 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]
  12. 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]
  13. 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]
  14. Talbot, A; Dunson, D; Dzirasa, K; Carlson, D, Estimating a brain network predictive of stress and genotype with supervised autoencoders., J R Stat Soc Ser C Appl Stat, vol. 72 no. 4 (August, 2023), pp. 912-936 [doi]  [abs]
  15. Jin, B; Dunson, DB; Rager, JE; Reif, DM; Engel, SM; Herring, AH, Bayesian matrix completion for hypothesis testing., Journal of the Royal Statistical Society. Series C, Applied statistics, vol. 72 no. 2 (May, 2023), pp. 254-270 [doi]  [abs]
  16. 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., Cereb Cortex, vol. 33 no. 9 (April, 2023), pp. 5307-5322 [doi]  [abs]
  17. 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]
  18. 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]
  19. Zhu, Y; Li, C; Dunson, DB, Classification Trees for Imbalanced Data: Surface-to-Volume Regularization, Journal of the American Statistical Association, vol. 118 no. 543 (January, 2023), pp. 1707-1717 [doi]  [abs]
  20. Zito, A; Rigon, T; Ovaskainen, O; Dunson, DB, Bayesian Modeling of Sequential Discoveries., Journal of the American Statistical Association, vol. 118 no. 544 (January, 2023), pp. 2521-2532 [doi]  [abs]
  21. 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, vol. 118 no. 544 (January, 2023), pp. 2250-2261 [doi]  [abs]
  22. 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]
  23. 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]
  24. Li, D; Nguyen, P; Zhang, Z; Dunson, D, Tree representations of brain structural connectivity via persistent homology., Frontiers in neuroscience, vol. 17 (January, 2023), pp. 1200373 [doi]  [abs]
  25. Chakraborty, A; Ou, R; Dunson, DB, Bayesian Inference on High-Dimensional Multivariate Binary Responses, Journal of the American Statistical Association (January, 2023) [doi]  [abs]
  26. 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]

Durrett, Richard T.

  1. Huang, X; Durrett, R, A stochastic spatial model for the sterile insect control strategy, Stochastic Processes and their Applications, vol. 157 (March, 2023), pp. 249-278 [doi]  [abs]
  2. Huang, X; Durrett, R, Corrigendum to: The contact process on periodic trees (Electronic Communications in Probability), Electronic Communications in Probability, vol. 28 (January, 2023) [doi]  [abs]

Elgindi, Tarek M

  1. Drivas, TD; Elgindi, TM; Jeong, IJ, Twisting in Hamiltonian flows and perfect fluids, Inventiones Mathematicae, vol. 238 no. 1 (October, 2024), pp. 331-370 [doi]  [abs]
  2. Elgindi, TM, Remark on the stability of energy maximizers for the 2D Euler equation on $ \mathbb{T}^2 $, Communications on Pure and Applied Analysis, vol. 23 no. 10 (2024), pp. 1562-1568, American Institute of Mathematical Sciences (AIMS) [doi]
  3. Drivas, TD; Elgindi, TM; La, J, Propagation of singularities by Osgood vector fields and for 2D inviscid incompressible fluids, Mathematische Annalen, vol. 387 no. 3-4 (December, 2023), pp. 1691-1718 [doi]  [abs]
  4. Drivas, TD; Elgindi, TM, Singularity formation in the incompressible Euler equation in finite and infinite time, EMS Surveys in Mathematical Sciences, vol. 10 no. 1 (November, 2023), pp. 1-100, European Mathematical Society - EMS - Publishing House GmbH [doi]  [abs]
  5. Elgindi, TM; Jeong, IJ, On Singular Vortex Patches, I: Well-Posedness Issues, Memoirs of the American Mathematical Society, vol. 283 no. 1400 (March, 2023), pp. 1-102 [doi]  [abs]
  6. Coti Zelati, M; Elgindi, TM; Widmayer, K, Stationary Structures Near the Kolmogorov and Poiseuille Flows in the 2d Euler Equations, Archive for Rational Mechanics and Analysis, vol. 247 no. 1 (February, 2023) [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]

Fang, Di

  1. An, D; Fang, D; Jordan, S; Liu, J-P; Low, GH; Wang, J, Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation, in arXiv 2205.01141, Commun. Math. Phys., vol. 404 (October, 2023), pp. 963-1020 [doi]
  2. 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]
  3. 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]
  4. Fang, D; Lin, L; Tong, Y, Time-marching based quantum solvers for time-dependent linear differential equations, Quantum, vol. 7 (January, 2023) [doi]  [abs]

Fintzen, Jessica

  1. Fintzen, J, Supercuspidal representations in non-defining characteristics, Journal of Algebra, vol. 656 (October, 2024), pp. 196-205 [doi]  [abs]
  2. Fintzen, J; Kaletha, T; Spice, L, A TWISTED YU CONSTRUCTION, HARISH-CHANDRA CHARACTERS, AND ENDOSCOPY, Duke Mathematical Journal, vol. 172 no. 12 (January, 2023), pp. 2241-2301 [doi]  [abs]

Ge, Rong

  1. Chidambaram, M; Ge, R, ON THE LIMITATIONS OF TEMPERATURE SCALING FOR DISTRIBUTIONS WITH OVERLAPS, 12th International Conference on Learning Representations, ICLR 2024 (January, 2024)  [abs]
  2. Chidambaram, M; Wu, C; Cheng, Y; Ge, R, Hiding Data Helps: On the Benefits of Masking for Sparse Coding, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 5600-5615  [abs]
  3. Zhou, M; Ge, R, Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 42543-42573  [abs]
  4. Chidambaram, M; Wang, X; Wu, C; Ge, R, Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 5563-5599  [abs]
  5. Zhao, H; Panigrahi, A; Ge, R; Arora, S, Do Transformers Parse while Predicting the Masked Word?, EMNLP 2023 - 2023 Conference on Empirical Methods in Natural Language Processing, Proceedings (January, 2023), pp. 16513-16542, ISBN 9798891760608  [abs]
  6. Damian, A; Nichani, E; Ge, R; Lee, JD, Smoothing the Landscape Boosts the Signal for SGD Optimal Sample Complexity for Learning Single Index Models, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [abs]
  7. Wu, C; Li, LE; Ermon, S; Haffner, P; Ge, R; Zhang, Z, The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models, Proceedings of Machine Learning Research, vol. 239 (January, 2023), pp. 118-126  [abs]
  8. Zhu, X; Wang, Z; Wang, X; Zhou, M; Ge, R, UNDERSTANDING EDGE-OF-STABILITY TRAINING DYNAMICS WITH A MINIMALIST EXAMPLE, 11th International Conference on Learning Representations, ICLR 2023 (January, 2023)  [abs]
  9. Wang, X; Wang, AN; Zhou, M; Ge, R, PLATEAU IN MONOTONIC LINEAR INTERPOLATION - A "BIASED" VIEW OF LOSS LANDSCAPE FOR DEEP NETWORKS, 11th International Conference on Learning Representations, ICLR 2023 (January, 2023)  [abs]
  10. Luo, Z; Wu, S; Weng, C; Zhou, M; Ge, R, UNDERSTANDING THE ROBUSTNESS OF SELF-SUPERVISED LEARNING THROUGH TOPIC MODELING, 11th International Conference on Learning Representations, ICLR 2023 (January, 2023)  [abs]
  11. Ren, Y; Zhou, M; Ge, R, DEPTH SEPARATION WITH MULTILAYER MEAN-FIELD NETWORKS, 11th International Conference on Learning Representations, ICLR 2023 (January, 2023)  [abs]
  12. Li, S; Diakonikolas, I; Ge, R; Cheng, Y; Diakonikolas, J; Wright, S, Robust Second-Order Nonconvex Optimization and Its Application to Low Rank Matrix Sensing, Advances in Neural Information Processing Systems, vol. 36 (January, 2023), pp. 54386-54398  [abs]

Getz, Jayce R.

  1. Getz, J; Hsu, C-H; Leslie, S, Harmonic analysis on certain spherical varieties, Journal of the European Mathematical Society (October, 2023), EMS Press [doi]

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]

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]

Herschlag, Gregory J.

  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 [doi]  [abs]

Hughes, James M.

  1. Hughes, J, Weave-realizability for D–type, Algebraic & Geometric Topology, vol. 23 no. 6 (September, 2023), pp. 2735-2776, Mathematical Sciences Publishers [doi]
  2. Hughes, J, Lagrangian fillings in $A$-type and their Kálmán loop orbits, Revista Matemática Iberoamericana, vol. 39 no. 5 (July, 2023), pp. 1681-1723, European Mathematical Society - EMS - Publishing House GmbH [doi]  [abs]

Jo, Min Jun

  1. Jo, MJ; Kim, J, Quantitative asymptotic stability of the quasi-linearly stratified densities in the IPM equation with the sharp decay rates, Journal of Functional Analysis, vol. 286 no. 11 (June, 2024) [doi]  [abs]
  2. Jo, MJ; Kim, J; Lee, J, NONCONVERGENCE OF THE ROTATING STRATIFIED FLOWS TOWARD THE QUASI-GEOSTROPHIC DYNAMICS, SIAM Journal on Mathematical Analysis, vol. 56 no. 3 (January, 2024), pp. 3357-3385 [doi]  [abs]

Kiselev, Alexander A.

  1. Gong, Y; Kiselev, A, A simple reaction-diffusion system as a possible model for the origin of chemotaxis., Journal of biological dynamics, vol. 17 no. 1 (December, 2023), pp. 2260833 [doi]  [abs]
  2. Kiselev, A; Luo, X, Illposedness of C2 Vortex Patches, Archive for Rational Mechanics and Analysis, vol. 247 no. 3 (June, 2023) [doi]  [abs]
  3. Kiselev, A; Luo, X, On Nonexistence of Splash Singularities for the α -SQG Patches, Journal of Nonlinear Science, vol. 33 no. 2 (April, 2023) [doi]  [abs]
  4. 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]
  5. Kiselev, A; Nazarov, F; Ryzhik, L; Yao, Y, Chemotaxis and reactions in biology, Journal of the European Mathematical Society, vol. 25 no. 7 (January, 2023), pp. 2641-2696 [doi]  [abs]

Lang, Quanjun

  1. Lang, Q; Lu, F, IDENTIFIABILITY OF INTERACTION KERNELS IN MEAN-FIELD EQUATIONS OF INTERACTING PARTICLES, Foundations of Data Science, vol. 5 (January, 2023), pp. 480-502 [doi]  [abs]

Levine, Adam S.

  1. Hedden, M; Levine, AS, A surgery formula for knot Floer homology, Quantum Topology, vol. 15 no. 2 (January, 2024), pp. 229-336 [doi]  [abs]
  2. Levine, AS, A note on rationally slice knots, New York Journal of Mathematics, vol. 29 (January, 2023), pp. 1363-1372  [abs]

Li, Bowen

  1. B. Li, J. Lu, Quantum variational embedding for ground-state energy problems: sum of squares and cluster selection (2023) [arXiv:2305.18571]

Liang, Ying

  1. Liang, Y; Zhang, H, A rigorous theory on electromagnetic diffraction by a planar aperture in a perfectly conducting screen, Journal of Mathematical Physics, vol. 65 no. 7 (July, 2024), AIP Publishing [doi]  [abs]
  2. Ito, K; Liang, Y, A Direct Probing Method of an Inverse Problem for the Eikonal Equation, SIAM Journal on Scientific Computing, vol. 46 no. 2 (April, 2024), pp. A1235-A1251, Society for Industrial & Applied Mathematics (SIAM) [doi]
  3. Li, P; Liang, Y, Stability for Inverse Source Problems of the Stochastic Helmholtz Equation with a White Noise, SIAM Journal on Applied Mathematics, vol. 84 no. 2 (April, 2024), pp. 687-709, Society for Industrial & Applied Mathematics (SIAM) [doi]
  4. Li, P; Liang, Y; Wang, Y, A Data-Assisted Two-Stage Method for the Inverse Random Source Problem, SIAM Journal on Imaging Sciences, vol. 16 no. 4 (December, 2023), pp. 1929-1952, Society for Industrial & Applied Mathematics (SIAM) [doi]

Liss, Kyle L   (search)

  1. Bedrossian, J; Liss, K, Stationary measures for stochastic differential equations with degenerate damping, Probability Theory and Related Fields, vol. 189 no. 1-2 (June, 2024), pp. 101-178 [doi]  [abs]

Liu, Jian-Guo

  1. Liu, J-G; Pego, RL, A Simple Construction of Fat Cantor Sets, The American Mathematical Monthly, vol. 131 no. 6 (July, 2024), pp. 525-525, Informa UK Limited [doi]
  2. Gao, Y; Liu, JG; Li, W, MASTER EQUATIONS FOR FINITE STATE MEAN FIELD GAMES WITH NONLINEAR ACTIVATIONS, Discrete and Continuous Dynamical Systems - Series B, vol. 29 no. 7 (July, 2024), pp. 2837-2879, American Institute of Mathematical Sciences (AIMS) [doi]  [abs] [reputed journal]
  3. Cherepanov, V; Liu, JG; Qian, Z, On the Dynamics of the Boundary Vorticity for Incompressible Viscous Flows, Journal of Scientific Computing, vol. 99 no. 2 (May, 2024) [doi]  [abs]
  4. Stevens, JB; Riley, BA; Je, J; Gao, Y; Wang, C; Mowery, YM; Brizel, DM; Yin, F-F; Liu, J-G; Lafata, KJ, Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics., Med Phys, vol. 51 no. 5 (May, 2024), pp. 3334-3347 [doi]  [abs]
  5. Feng, Y; Li, L; Liu, JG; Xu, X, EXISTENCE OF WEAK SOLUTIONS TO p-NAVIER-STOKES EQUATIONS, Discrete and Continuous Dynamical Systems - Series B, vol. 29 no. 4 (April, 2024), pp. 1868-1890, American Institute of Mathematical Sciences (AIMS) [doi]  [abs] [reputed journal]
  6. Li, L; Liu, JG; Wang, Y, Geometric ergodicity of SGLD via reflection coupling, Stochastics and Dynamics (January, 2024), World Scientific Pub Co Pte Ltd [doi]  [abs]
  7. Feng, Y; Li, L; Liu, JG; Tang, T, SOME GRONWALL INEQUALITIES FOR A CLASS OF DISCRETIZATIONS OF TIME FRACTIONAL EQUATIONS ON NONUNIFORM MESHES, SIAM Journal on Numerical Analysis, vol. 62 no. 5 (January, 2024), pp. 2196-2221 [doi]  [abs]
  8. Gao, Y; Liu, J-G, A Selection Principle for Weak KAM Solutions via Freidlin–Wentzell Large Deviation Principle of Invariant Measures, SIAM Journal on Mathematical Analysis, vol. 55 no. 6 (December, 2023), pp. 6457-6495, Society for Industrial & Applied Mathematics (SIAM) [doi] [reputed journal]
  9. Gao, Y; Liu, J-G, Large Deviation Principle and Thermodynamic Limit of Chemical Master Equation via Nonlinear Semigroup, Multiscale Modeling & Simulation, vol. 21 no. 4 (December, 2023), pp. 1534-1569, Society for Industrial & Applied Mathematics (SIAM) [doi] [reputed journal]
  10. Qi, D; Liu, J-G, High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems., Chaos (Woodbury, N.Y.), vol. 33 no. 10 (October, 2023), pp. 103133 [doi]  [abs] [reputed journal]
  11. 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]
  12. 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] [reputed journal]
  13. Gao, Y; Li, T; Li, X; Liu, JG, TRANSITION PATH THEORY FOR LANGEVIN DYNAMICS ON MANIFOLDS: OPTIMAL CONTROL AND DATA-DRIVEN SOLVER, Multiscale Modeling and Simulation, vol. 21 no. 1 (March, 2023), pp. 1-33, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs] [reputed journal]
  14. 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] [reputed journal]
  15. 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] [reputed journal]
  16. 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]
  17. 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] [reputed journal]
  18. Gao, Y; Liu, J-G, Stochastic Chemical Reaction Systems in Biology, SIAM REVIEW, vol. 65 no. 2 (2023), pp. 593-+ [reputed journal]

Lu, Jianfeng

  1. Lu, J; Stubbs, KD, Algebraic Localization of Wannier Functions Implies Chern Triviality in Non-periodic Insulators, Annales Henri Poincare, vol. 25 no. 8 (August, 2024), pp. 3911-3926 [doi]  [abs]
  2. Bierman, J; Li, Y; Lu, J, Qubit Count Reduction by Orthogonally Constrained Orbital Optimization for Variational Quantum Excited-State Solvers., Journal of chemical theory and computation, vol. 20 no. 8 (April, 2024), pp. 3131-3143 [doi]  [abs]
  3. Loring, TA; Lu, J; Watson, AB, Locality of the windowed local density of states, Numerische Mathematik, vol. 156 no. 2 (April, 2024), pp. 741-775, Springer Science and Business Media LLC [doi]  [abs]
  4. Li, X; Pura, J; Allen, A; Owzar, K; Lu, J; Harms, M; Xie, J, DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree., Genet Epidemiol, vol. 48 no. 1 (February, 2024), pp. 42-55 [doi]  [abs]
  5. Chen, C; Chen, Z; Lu, J, REPRESENTATION THEOREM FOR MULTIVARIABLE TOTALLY SYMMETRIC FUNCTIONS*, Communications in Mathematical Sciences, vol. 22 no. 5 (January, 2024), pp. 1195-1201 [doi]  [abs]
  6. Jing, Y; Chen, J; Li, L; Lu, J, A Machine Learning Framework for Geodesics Under Spherical Wasserstein–Fisher–Rao Metric and Its Application for Weighted Sample Generation, Journal of Scientific Computing, vol. 98 no. 1 (January, 2024) [doi]  [abs]
  7. Chen, Z; Lu, J; Lu, Y; Zhang, X, ON THE CONVERGENCE OF SOBOLEV GRADIENT FLOW FOR THE GROSS-PITAEVSKII EIGENVALUE PROBLEM, SIAM Journal on Numerical Analysis, vol. 62 no. 2 (January, 2024), pp. 667-691, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  8. Cheng, X; Lu, J; Tan, Y; Xie, Y, Convergence of flow-based generative models via proximal gradient descent in Wasserstein space, IEEE Transactions on Information Theory (January, 2024) [doi]  [abs]
  9. He, Y; Balasubramanian, K; Sriperumbudur, BK; Lu, J, Regularized Stein Variational Gradient Flow, Foundations of Computational Mathematics (January, 2024) [doi]  [abs]
  10. Lu, J; Otto, F; Wang, L, Optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media, Communications in Partial Differential Equations, vol. 49 no. 7-8 (January, 2024), pp. 609-670 [doi]  [abs]
  11. Chen, Z; Lu, J; Zhang, A, ONE-DIMENSIONAL TENSOR NETWORK RECOVERY, SIAM Journal on Matrix Analysis and Applications, vol. 45 no. 3 (January, 2024), pp. 1217-1244 [doi]  [abs]
  12. Zhang, S; Lu, J; Zhao, H, Deep Network Approximation: Beyond ReLU to Diverse Activation Functions, JOURNAL OF MACHINE LEARNING RESEARCH, vol. 25 (2024)
  13. Wang, Z; Zhang, Z; Lu, J; Li, Y, Coordinate Descent Full Configuration Interaction for Excited States., Journal of chemical theory and computation, vol. 19 no. 21 (November, 2023), pp. 7731-7739 [doi]  [abs]
  14. 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]
  15. Lu, J; Wu, Y; Xiang, Y, Score-based Transport Modeling for Mean-Field Fokker-Planck Equations, vol. 503 (April, 2023) [doi]  [abs]
  16. 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]
  17. 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]
  18. Bal, G; Becker, S; Drouot, A; Kammerer, CF; Lu, J; Watson, AB, EDGE STATE DYNAMICS ALONG CURVED INTERFACES, SIAM Journal on Mathematical Analysis, vol. 55 no. 5 (January, 2023), pp. 4219-4254 [doi]  [abs]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. Zhang, S; Lu, J; Zhao, H, On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 41452-41487  [abs]
  27. Chen, H; Lee, H; Lu, J, Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 5367-5382  [abs]
  28. Agazzi, A; Lu, J; Mukherjee, S, Global optimality of Elman-type RNNs in the mean-field regime, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 196-227  [abs]
  29. Marwah, T; Lipton, ZC; Lu, J; Risteski, A, Neural Network Approximations of PDEs Beyond Linearity: A Representational Perspective, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 24139-24172  [abs]
  30. Chen, S; Chewi, S; Lee, H; Li, Y; Lu, J, The probability flow ODE is provably fast, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [abs]
  31. Marwah, T; Pokle, A; Kolter, JZ; Lipton, ZC; Lu, J; Risteski, A, Deep Equilibrium Based Neural Operators for Steady-State PDEs, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [abs]
  32. Chen, Z; Liu, J; Wang, X; Lu, J; Yin, W, ON REPRESENTING LINEAR PROGRAMS BY GRAPH NEURAL NETWORKS, 11th International Conference on Learning Representations, ICLR 2023 (January, 2023)  [abs]
  33. Chen, Z; Liu, J; Wang, X; Lu, J; Yin, W, ON REPRESENTING MIXED-INTEGER LINEAR PROGRAMS BY GRAPH NEURAL NETWORKS, 11th International Conference on Learning Representations, ICLR 2023 (January, 2023)  [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]

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. Earle, G; Mattingly, JC, Convergence of stratified MCMC sampling of non-reversible dynamics, Stochastics and Partial Differential Equations: Analysis and Computations, vol. 12 no. 4 (December, 2024), pp. 2289-2329 [doi]  [abs]
  2. Agazzi, A; Grotto, F; Mattingly, JC, Random splitting of point vortex flows, Electronic Communications in Probability, vol. 29 (January, 2024) [doi]  [abs]
  3. 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]
  4. Herzog, DP; Mattingly, JC; Nguyen, HD, Gibbsian dynamics and the generalized Langevin equation, Electronic Journal of Probability, vol. 28 (January, 2023) [doi]  [abs]

Miller, Ezra

  1. Miller, E; Zhang, J, Geodesic complexity of convex polyhedra (March, 2023)
  2. Miller, E, Stratifications of real vector spaces from constructible sheaves with conical microsupport, Journal of Applied and Computational Topology, vol. 7 no. 3 (2023), pp. 473-489, Springer [doi]
  3. Miller, E; Geist, N, Global dimension of real-exponent polynomial rings, Algebra and Number Theory, vol. 17 no. 10 (2023), pp. 1779-1788, Mathematical Sciences Publishers (MSP) [doi]

Mukherjee, Sayan

  1. Kirveslahti, H; Mukherjee, S, Representing fields without correspondences: the lifted Euler characteristic transform, Journal of Applied and Computational Topology, vol. 8 no. 1 (March, 2024), pp. 1-34 [doi]  [abs]
  2. Patel, N; Paul, CK; Kar, IN; Mukherjee, S, Finite Time Adaptive Backstepping Control Approach for Quadrotors, IFAC-PapersOnLine, vol. 57 (March, 2024), pp. 125-130 [doi]  [abs]
  3. Arya, S; Curry, J; Mukherjee, S, A Sheaf-Theoretic Construction of Shape Space, Foundations of Computational Mathematics (January, 2024) [doi]  [abs]
  4. Roldan-Roa, E; Roldan-Roa, ÉB; Raave, DK; van Herwegen, J; Polytimou, N; Mukherjee, S; Colasante, T; Malti, T; Mori, J, Play My Math: Second Development Cycle of an EdTech Tool Supporting the Teaching and Learning of Fractions Through Music in Algebraic Notation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14786 LNCS (January, 2024), pp. 44-53 [doi]  [abs]
  5. Roldan-Roa, E; Roldan-Roa, ÉB; Raave, DK; Van Herwegen, J; Politimou, N; Mukherjee, S; Colasante, T; Malti, T; Mori, J; Specht, M, Play My Math: First Development Cycle of an EdTech Tool Supporting the Teaching and Learning of Fractions Through Music in Algebraic Notation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15160 LNCS (January, 2024), pp. 241-246 [doi]  [abs]
  6. Baek, Y; Aquino, W; Mukherjee, S, Generalized Bayes approach to inverse problems with model misspecification., Inverse problems, vol. 39 no. 10 (October, 2023), pp. 105011 [doi]  [abs]
  7. Mubayi, D; Mukherjee, S, Triangles in graphs without bipartite suspensions, Discrete Mathematics, vol. 346 no. 6 (June, 2023) [doi]  [abs]
  8. 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., Am J Ophthalmol, vol. 250 (June, 2023), pp. 130-137 [doi]  [abs]
  9. Liu, X; Mukherjee, S, Stability theorems for some Kruskal–Katona type results, European Journal of Combinatorics, vol. 110 (May, 2023) [doi]  [abs]
  10. 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]
  11. Roche, KE; Bjork, JR; Dasari, MR; Grieneisen, L; Jansen, D; Gould, TJ; Gesquiere, LR; Barreiro, LB; Alberts, SC; Blekhman, R; Gilbert, JA; Tung, J; Mukherjee, S; Archie, EA, Universal gut microbial relationships in the gut microbiome of wild baboons., eLife, vol. 12 (May, 2023), pp. e83152 [doi]  [abs]
  12. 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]
  13. 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]
  14. Baek, Y; Berchuck, SI; Mukherjee, S, Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [abs]
  15. Caprio, M; Mukherjee, S, Concentration Inequalities and Optimal Number of Layers for Stochastic Deep Neural Networks, IEEE Access, vol. 11 (January, 2023), pp. 38458-38470 [doi]  [abs]
  16. Agazzi, A; Lu, J; Mukherjee, S, Global optimality of Elman-type RNNs in the mean-field regime, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 196-227  [abs]
  17. Shi, C; Gottschalk, WK; Colton, CA; Mukherjee, S; Lutz, MW, Alzheimer's Disease Protein Relevance Analysis Using Human and Mouse Model Proteomics Data., Front Syst Biol, vol. 3 (2023) [doi]  [abs]

Nelson, Anna C

  1. Nelson, AC; Rolls, MM; Ciocanel, M-V; McKinley, SA, Minimal Mechanisms of Microtubule Length Regulation in Living Cells., Bulletin of mathematical biology, vol. 86 no. 5 (April, 2024), pp. 58, Springer Science and Business Media LLC [doi]  [abs]
  2. Kent, A; Leiderman, K; Nelson, AC; Sindi, SS; Stadt, MM; Xiong, L; Zhang, Y, Studying the Effects of Oral Contraceptives on Coagulation Using a Mathematical Modeling Approach, in The IMA Volumes in Mathematics and its Applications (2024), pp. 83-132, Springer Nature Switzerland, ISBN 9783031585159 [doi]  [abs]
  3. 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]

Nolen, James H.

  1. Iyer, G; Lu, E; Nolen, J, USING BERNOULLI MAPS TO ACCELERATE MIXING OF A RANDOM WALK ON THE TORUS, Quarterly of Applied Mathematics, vol. 82 no. 2 (January, 2024), pp. 359-390, American Mathematical Society (AMS) [doi]  [abs]

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]

Pierce, Lillian B.

  1. Anderson, TC; Maldague, D; Pierce, LB; Yung, PL, On Polynomial Carleson Operators Along Quadratic Hypersurfaces, Journal of Geometric Analysis, vol. 34 no. 10 (October, 2024) [doi]  [abs]
  2. Gressman, PT; Pierce, LB; Roos, J; Yung, PL, A NEW TYPE OF SUPERORTHOGONALITY, Proceedings of the American Mathematical Society, vol. 152 no. 2 (February, 2024), pp. 665-675 [doi]  [abs]
  3. Browning, T; Pierce, LB; Schindler, D, GENERALISED QUADRATIC FORMS OVER TOTALLY REAL NUMBER FIELDS, Journal of the Institute of Mathematics of Jussieu (January, 2024) [doi]  [abs]
  4. Chu, R; Pierce, LB, Generalizations of the Schrödinger maximal operator: building arithmetic counterexamples, Journal d'Analyse Mathematique, vol. 151 no. 1 (December, 2023), pp. 59-114 [doi]  [abs]
  5. Pierce, L; Chu, R, Generalizations of the Schrödinger maximal operator: building arithmetic counterexamples, in https://arxiv.org/abs/2309.05872 (November, 2023)
  6. 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]

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, vol. 39 no. 7 (October, 2024), pp. 1295-1303 [doi]  [abs]
  2. Tanade, C; Khan, NS; Rakestraw, E; Ladd, WD; Draeger, EW; Randles, A, Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins., NPJ digital medicine, vol. 7 no. 1 (September, 2024), pp. 236, Springer Science and Business Media LLC [doi]  [abs]
  3. Chidyagwai, SG; Kaplan, MS; Jensen, CW; Chen, JS; Chamberlain, RC; Hill, KD; Barker, PCA; Slesnick, TC; Randles, A, Surgical Modulation of Pulmonary Artery Shear Stress: A Patient-Specific CFD Analysis of the Norwood Procedure., Cardiovasc Eng Technol, vol. 15 no. 4 (August, 2024), pp. 431-442 [doi]  [abs]
  4. Geddes, J; Randles, A; Tanade, C; Ladd, W; Khan, NS, Velocity Temporal Shape Affects Simulated Flow in Left Coronary Arteries (July, 2024)
  5. Vardhan, M; Tanade, C; Chen, SJ; Mahmood, O; Chakravartti, J; Jones, WS; Kahn, AM; Vemulapalli, S; Patel, M; Leopold, JA; Randles, A, Diagnostic Performance of Coronary Angiography Derived Computational Fractional Flow Reserve., J Am Heart Assoc, vol. 13 no. 13 (July, 2024), pp. e029941 [doi]  [abs]
  6. Seidel, E; Randles, A; Arthur, R; Bergman, K; Carlson, B; Deelman, E; Grout, R; Hendrickson, B; Reed, D, 2024 Advanced Scientific Computing Advisory Committee (ASCR) Facilities Subcommittee Recommendations (May, 2024), USDOE Office of Science (SC) [doi]
  7. Geddes, JR; Randles, A, Optimizing Temporal Waveform Analysis: A Novel Pipeline for Efficient Characterization of Left Coronary Artery Velocity Profiles., ArXiv (February, 2024)  [abs]
  8. Nan, J; Roychowdhury, S; Randles, A, Investigating the Influence of Heterogeneity Within Cell Types on Microvessel Network Transport., Cellular and molecular bioengineering, vol. 16 no. 5-6 (December, 2023), pp. 497-507 [doi]  [abs]
  9. Yousef, A; Randles, A, Enabling In Situ Visualization of Large-Scale Cellular Simulations (November, 2023)
  10. Martin, A; Liu, G; Ladd, W; Lee, S; Gounley, J; Vetter, J; Patel, S; Rizzi, S; Mateevitsi, V; Insley, J; Randles, A, Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations, ACM International Conference Proceeding Series (November, 2023), pp. 1126-1137, ACM, ISBN 9798400707858 [doi]  [abs]
  11. Valero-Lara, P; Vetter, J; Gounley, J; Randles, A, Moment Representation of Regularized Lattice Boltzmann Methods on NVIDIA and AMD GPUs, ACM International Conference Proceeding Series (November, 2023), pp. 1697-1704, ACM, ISBN 9798400707858 [doi]  [abs]
  12. Tanade, C; Rakestraw, E; Ladd, W; Draeger, E; Randles, A, Cloud Computing to Enable Wearable-Driven Longitudinal Hemodynamic Maps., International Conference for High Performance Computing, Networking, Storage and Analysis : [proceedings]. SC (Conference : Supercomputing), vol. 2023 (November, 2023), pp. 82, ACM, ISBN 9798400701092 [doi]  [abs]
  13. Roychowdhury, S; Balogh, P; Mahmud, ST; Puleri, DF; Martin, A; Gounley, J; Draeger, EW; Randles, A, Enhancing Adaptive Physics Refinement Simulations Through the Addition of Realistic Red Blood Cell Counts., International Conference for High Performance Computing, Networking, Storage and Analysis : [proceedings]. SC (Conference : Supercomputing), vol. 2023 (November, 2023), pp. 41, ACM, ISBN 9798400701092 [doi]  [abs]
  14. Randles, A; Draeger, E; Yousef, A, Low-Cost Post Hoc Reconstruction of HPC Simulations at Full Resolution, 2023 IEEE 13th Symposium on Large Data Analysis and Visualization (LDAV) (October, 2023), pp. 17-21, IEEE [doi]
  15. 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]
  16. 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]
  17. 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]
  18. Pepona, M; Gounley, J; Randles, A, Effect of constitutive law on the erythrocyte membrane response to large strains., Computers & mathematics with applications (Oxford, England : 1987), vol. 132 (February, 2023), pp. 145-160 [doi]  [abs]
  19. Yousef, A; Draeger, E; Randles, A, Low-Cost Post Hoc Reconstruction of HPC Simulations at Full Resolution (January, 2023), IEEE
  20. 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]

Reed, Michael C.

  1. Best, J; Kim, R; Reed, M; Nijhout, HF, A mathematical model of melatonin synthesis and interactions with the circadian clock., Mathematical biosciences, vol. 377 (November, 2024), pp. 109280 [doi]  [abs]
  2. Mena, S; Cruikshank, A; Best, J; Nijhout, HF; Reed, MC; Hashemi, P, Modulation of serotonin transporter expression by escitalopram under inflammation., Communications biology, vol. 7 no. 1 (June, 2024), pp. 710 [doi]  [abs]
  3. Buchanan, AM; Mena, S; Choukari, I; Vasa, A; Crawford, JN; Fadel, J; Maxwell, N; Reagan, L; Cruikshank, A; Best, J; Nijhout, HF; Reed, M; Hashemi, P, Serotonin as a biomarker of toxin-induced Parkinsonism., Molecular medicine (Cambridge, Mass.), vol. 30 no. 1 (March, 2024), pp. 33 [doi]  [abs]
  4. Cruikshank, A; Reed, MC; Frederik Nijhout, H, Sex differences in glutathione metabolism and acetaminophen toxicity, Metabolism and Target Organ Damage, vol. 4 no. 2 (January, 2024) [doi]  [abs]
  5. Suzuki, A; Henao, R; Reed, MC; Nijhout, HF; Tripathi, M; Singh, BK; Yen, PM; Diehl, AM; Abdelmalek, MF, Lower hepatic CBS and PEMT expression in advanced NAFLD: inferencing strategies to lower homocysteine with a mathematical model, Metabolism and Target Organ Damage, vol. 4 no. 3 (January, 2024) [doi]  [abs]
  6. Duncan, W; Antoneli, F; Best, J; Golubitsky, M; Jin, J; Nijhout, HF; Reed, M; Stewart, I, Homeostasis Patterns, SIAM Journal on Applied Dynamical Systems, vol. 23 no. 3 (January, 2024), pp. 2262-2292 [doi]  [abs]
  7. Cruikshank, A; Nijhout, HF; Best, J; Reed, M, Dynamical questions in volume transmission., Journal of biological dynamics, vol. 17 no. 1 (December, 2023), pp. 2269986 [doi]  [abs]
  8. Witt, CE; Mena, S; Holmes, J; Hersey, M; Buchanan, AM; Parke, B; Saylor, R; Honan, LE; Berger, SN; Lumbreras, S; Nijhout, FH; Reed, MC; Best, J; Fadel, J; Schloss, P; Lau, T; Hashemi, P, Serotonin is a common thread linking different classes of antidepressants., Cell chemical biology, vol. 30 no. 12 (December, 2023), pp. 1557-1570.e6 [doi]  [abs]
  9. Witt, CE; Mena, S; Holmes, J; Hersey, M; Buchanan, AM; Parke, B; Saylor, R; Honan, LE; Berger, SN; Lumbreras, S; Nijhout, FH; Reed, MC; Best, J; Fadel, J; Schloss, P; Lau, T; Hashemi, P, Serotonin is a Common Thread Linking Different Classes of Antidepressants., Res Sq (March, 2023) [doi]  [abs]
  10. 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]

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. Deng, H; Robles, C, Completion of two-parameter period maps by nilpotent orbits (December, 2023)  [abs]
  2. Robles, C, Pseudoconvexity at infinity in Hodge theory: a codimension one example (February, 2023)  [abs]
  3. Robles, C, Extension of Hodge norms at infinity (February, 2023)  [abs]

Rossman, Benjamin

  1. Rossman, B, Formula Size-Depth Tradeoffs for Iterated Sub-permutation Matrix Multiplication, Proceedings of the Annual ACM Symposium on Theory of Computing (June, 2024), pp. 1386-1395 [doi]  [abs]
  2. 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]
  3. He, W; Rossman, B, Symmetric Formulas for Products of Permutations, Leibniz International Proceedings in Informatics, LIPIcs, vol. 251 (January, 2023), ISBN 9783959772631 [doi]  [abs]

Rudin, Cynthia D.

  1. Zhang, H; Mahabadi, RK; Rudin, C; Guilleminot, J; Brinson, LC, Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects, Computers and Structures, vol. 305 (December, 2024) [doi]  [abs]
  2. Chen, SF; Guo, Z; Ding, C; Hu, X; Rudin, C, Sparse learned kernels for interpretable and efficient medical time series processing, Nature Machine Intelligence, vol. 6 no. 10 (October, 2024), pp. 1132-1144 [doi]  [abs]
  3. Semenova, L; Wang, Y; Falcinelli, S; Archin, N; Cooper-Volkheimer, AD; Margolis, DM; Goonetilleke, N; Murdoch, DM; Rudin, CD; Browne, EP, Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy., Elife, vol. 13 (September, 2024) [doi]  [abs]
  4. Hahn, S; Yin, J; Zhu, R; Xu, W; Jiang, Y; Mak, S; Rudin, C, SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (August, 2024), pp. 5050-5060 [doi]  [abs]
  5. Ding, C; Guo, Z; Chen, Z; Lee, RJ; Rudin, C; Hu, X, SiamQuality: a ConvNet-based foundation model for photoplethysmography signals., Physiological measurement, vol. 45 no. 8 (August, 2024) [doi]  [abs]
  6. Parikh, H; Sun, H; Amerineni, R; Rosenthal, ES; Volfovsky, A; Rudin, C; Westover, MB; Zafar, SF, How many patients do you need? Investigating trial designs for anti-seizure treatment in acute brain injury patients., Annals of clinical and translational neurology, vol. 11 no. 7 (July, 2024), pp. 1681-1690 [doi]  [abs]
  7. Bravo, F; Rudin, C; Shaposhnik, Y; Yuan, Y, Interpretable Prediction Rules for Congestion Risk in Intensive Care Units, Stochastic Systems, vol. 14 no. 2 (June, 2024), pp. 111-130 [doi]  [abs]
  8. Ashokkumar, M; Mei, W; Peterson, JJ; Harigaya, Y; Murdoch, DM; Margolis, DM; Kornfein, C; Oesterling, A; Guo, Z; Rudin, CD; Jiang, Y; Browne, EP, Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation., Genomics Proteomics Bioinformatics, vol. 22 no. 1 (May, 2024) [doi]  [abs]
  9. Ding, C; Guo, Z; Rudin, C; Xiao, R; Shah, A; Do, DH; Lee, RJ; Clifford, G; Nahab, FB; Hu, X, Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms., IEEE journal of biomedical and health informatics, vol. 28 no. 5 (May, 2024), pp. 2650-2661 [doi]  [abs]
  10. Zhang, R; Xin, R; Seltzer, M; Rudin, C, Optimal Sparse Survival Trees., Proceedings of machine learning research, vol. 238 (May, 2024), pp. 352-360  [abs]
  11. Seale-Carlisle, T; Jain, S; Lee, C; Levenson, C; Ramprasad, S; Garrett, B; Roy, S; Rudin, C; Volfovsky, A, Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference, Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38 no. 20 (March, 2024), pp. 22331-22340 [doi]  [abs]
  12. Donnelly, J; Moffett, L; Barnett, AJ; Trivedi, H; Schwartz, F; Lo, J; Rudin, C, AsymMirai: Interpretable Mammography-based Deep Learning Model for 1-5-year Breast Cancer Risk Prediction., Radiology, vol. 310 no. 3 (March, 2024), pp. e232780 [doi]  [abs]
  13. Sun, Y; Chen, Z; Orlandi, V; Wang, T; Rudin, C, Sparse and Faithful Explanations Without Sparse Models, Proceedings of Machine Learning Research, vol. 238 (January, 2024), pp. 2071-2079  [abs]
  14. Katta, S; Parikh, H; Rudin, C; Volfovsky, A, Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data, Proceedings of Machine Learning Research, vol. 238 (January, 2024), pp. 3340-3348  [abs]
  15. Parikh, H; Lanners, Q; Akras, Z; Zafar, SF; Westover, MB; Rudin, C; Volfovsky, A, Safe and Interpretable Estimation of Optimal Treatment Regimes, Proceedings of Machine Learning Research, vol. 238 (January, 2024), pp. 2134-2142  [abs]
  16. Rudin, C; Zhong, C; Semenova, L; Seltzer, M; Parr, R; Liu, J; Katta, S; Donnelly, J; Chen, H; Boner, Z, Position: Amazing Things Come From Having Many Good Models, Proceedings of Machine Learning Research, vol. 235 (January, 2024), pp. 42783-42795  [abs]
  17. Yang, J; Barnett, AJ; Donnelly, J; Kishore, S; Fang, J; Schwartz, FR; Chen, C; Lo, JY; Rudin, C, FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (January, 2024), pp. 5003-5009 [doi]  [abs]
  18. Mahabadi, RK; Chen, Z; Daraio, C; Rudin, C; Brinson, LC, A robust framework for the generation of random metamaterials based on a graph algorithm, Proceedings of SPIE - The International Society for Optical Engineering, vol. 13109 (January, 2024) [doi]  [abs]
  19. Garrett, BL; Rudin, C, The Right to a Glass Box: Rethinking the Use of Artificial Intelligence in Criminal Justice, Cornell Law Review, vol. 109 no. 3 (2024), pp. 561-627
  20. Liu, J; Rosen, S; Zhong, C; Rudin, C, OKRidge: Scalable Optimal k-Sparse Ridge Regression., Advances in neural information processing systems, vol. 36 (December, 2023), pp. 41076-41258  [abs]
  21. Semenova, L; Chen, H; Parr, R; Rudin, C, A Path to Simpler Models Starts With Noise., Advances in neural information processing systems, vol. 36 (December, 2023), pp. 3362-3401  [abs]
  22. Zhong, C; Chen, Z; Liu, J; Seltzer, M; Rudin, C, Exploring and Interacting with the Set of Good Sparse Generalized Additive Models., Advances in neural information processing systems, vol. 36 (December, 2023), pp. 56673-56699  [abs]
  23. Falcinelli, SD; Cooper-Volkheimer, AD; Semenova, L; Wu, E; Richardson, A; Ashokkumar, M; Margolis, DM; Archin, NM; Rudin, CD; Murdoch, D; Browne, EP, Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy., J Infect Dis, vol. 228 no. 11 (November, 2023), pp. 1600-1609 [doi]  [abs]
  24. Garrett, BL; Rudin, C, Interpretable algorithmic forensics., Proceedings of the National Academy of Sciences of the United States of America, vol. 120 no. 41 (October, 2023), pp. e2301842120 [doi]  [abs]
  25. 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]
  26. 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]
  27. 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 and Design, vol. 232 (August, 2023) [doi]  [abs]
  28. 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]
  29. McDonald, SM; Augustine, EK; Lanners, Q; Rudin, C; Catherine Brinson, L; Becker, ML, Applied machine learning as a driver for polymeric biomaterials design., Nature communications, vol. 14 no. 1 (August, 2023), pp. 4838 [doi]  [abs]
  30. 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]
  31. 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]
  32. Zhang, R; Xin, R; Seltzer, M; Rudin, C, Optimal Sparse Regression Trees., Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, vol. 37 no. 9 (June, 2023), pp. 11270-11279 [doi]  [abs]
  33. Zhou, L; Rudin, C; Gombolay, M; Spohrer, J; Zhou, M; Paul, S, From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues, AIS Transactions on Human-Computer Interaction, vol. 15 no. 1 (January, 2023), pp. 111-135 [doi]  [abs]
  34. 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]
  35. 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]
  36. Agnew, E; Qiu, M; Zhu, L; Wiseman, S; Rudin, C, The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation, Proceedings of the Annual Meeting of the Association for Computational Linguistics, vol. 2 (January, 2023), pp. 1627-1638, ISBN 9781959429715  [abs]
  37. Chen, Z; Tan, S; Chajewska, U; Rudin, C; Caruana, R, Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?, Proceedings of Machine Learning Research, vol. 209 (January, 2023), pp. 86-99  [abs]
  38. Donnelly, J; Rudin, C; Katta, S; Browne, EP, The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [abs]
  39. Ma, C; Zhao, B; Chen, C; Rudin, C, This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations, Advances in Neural Information Processing Systems, vol. 36 (January, 2023)  [abs]

Ryser, Marc D.

  1. Nguyen, DL; Shelley Hwang, E; Ryser, MD; Grimm, LJ, Imaging Changes and Outcomes of Patients Undergoing Active Monitoring for Ductal Carcinoma In Situ: Seven-Year Follow-up Study., Acad Radiol, vol. 31 no. 7 (July, 2024), pp. 2654-2662 [doi]  [abs]
  2. Iyer, M; Mallo, D; Maley, CC; Fortunato, A; Cisneros, L; King, LM; Ryser, MD; Lo, JY; Hall, A; Marks, JR; Hwang, S, Abstract A038: Evaluating DCIS progression: A comparative analysis of CNA predictive power derived from lpWGS and WES data, Cancer Research, vol. 84 no. 3_Supplement_2 (February, 2024), pp. A038-A038, American Association for Cancer Research (AACR) [doi]  [abs]
  3. Liu, X; Ren, Y; Ryser, M; Grimm, LJ; Lo, JY, A Residual-Attention Multimodal Fusion Network (ResAMF-Net) for Detection and Classification of Breast Cancer, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12927 (January, 2024), ISBN 9781510671584 [doi]  [abs]
  4. Schmitz, RSJM; van den Belt-Dusebout, AW; Clements, K; Ren, Y; Cresta, C; Timbres, J; Liu, Y-H; Byng, D; Lynch, T; Menegaz, BA; Collyar, D; Hyslop, T; Thomas, S; Love, JK; Schaapveld, M; Bhattacharjee, P; Ryser, MD; Sawyer, E; Hwang, ES; Thompson, A; Wesseling, J; Lips, EH; Schmidt, MK; Grand Challenge PRECISION consortium, Association of DCIS size and margin status with risk of developing breast cancer post-treatment: multinational, pooled cohort study., BMJ, vol. 383 (October, 2023), pp. e076022 [doi]  [abs]
  5. Ryser, MD; Greenwald, MA; Sorribes, IC; King, LM; Hall, A; Geradts, J; Weaver, DL; Mallo, D; Holloway, S; Monyak, D; Gumbert, G; Vaez-Ghaemi, S; Wu, E; Murgas, K; Grimm, LJ; Maley, CC; Marks, JR; Shibata, D; Hwang, ES, Growth Dynamics of Ductal Carcinoma in Situ Recapitulate Normal Breast Development., bioRxiv (October, 2023) [doi]  [abs]
  6. 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]
  7. 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]
  8. Etzioni, R; Gulati, R; Owens, L; Lange, J; Ryser, MD, Abstract IA018: 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_Supplement (January, 2023), pp. IA018-IA018, American Association for Cancer Research (AACR) [doi]  [abs]
  9. 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
  10. Lynch, T; Basila, D; Schnitt, SJ; Marks, JR; Strand, SH; Hyslop, T; Badve, SS; Watson, MA; Le-Petross, HT; Grimm, L; West, RB; Weiss, A; Rapperport, A; King, L; Factor, RE; Ryser, MD; Partridge, AH; Hwang, E-SS; Thompson, AM; Collyar, DE, From the lab to the clinic: Lessons learned from a translational working group., JOURNAL OF CLINICAL ONCOLOGY, vol. 41 no. 16 (2023)

Santillan, Sophia

  1. Santillan, ST; Franzoni, LP; Tantum, SL, Work in Progress: Evaluation of 360 Coaching to Support Whole-Student Advising in the First-Year, ASEE Annual Conference and Exposition, Conference Proceedings (June, 2023)
  2. Cervi, C; Santillan, ST; Virgin, LN, Interrogating the Configuration Space of Postbuckled Beams, Journal of Engineering Mechanics, vol. 149 no. 3 (March, 2023) [doi]  [abs]
  3. Stach, E; Lipp, G; McGuire, P; Santillan, S, IT'S CURLING NIGHT IN NEW ORLEANS!, ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE), vol. 8 (January, 2023) [doi]  [abs]

Sapiro, Guillermo

  1. Schlesinger, O; Kundu, R; Isaev, D; Choi, JY; Goetz, SM; Turner, DA; Sapiro, G; Peterchev, AV; Di Martino, JM, Scalp surface estimation and head registration using sparse sampling and 3D statistical models., Comput Biol Med, vol. 178 (August, 2024), pp. 108689 [doi]  [abs]
  2. 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, vol. 54 no. 6 (June, 2024), pp. 2286-2297 [doi]  [abs]
  3. Krishnappa Babu, PR; Di Martino, JM; Carpenter, KLH; Compton, S; Davis, N; Eichner, B; Espinosa, S; Franz, L; Perochon, S; Dawson, G; Sapiro, G, Large-scale Validation of a Scalable and Portable Behavioral Digital Screening Tool for Autism at Home, Conference on Human Factors in Computing Systems - Proceedings (May, 2024) [doi]  [abs]
  4. 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), vol. 23 no. 2 (April, 2024), pp. 459-470 [doi]  [abs]
  5. Goldberg, CB; Adams, L; Blumenthal, D; Brennan, PF; Brown, N; Butte, AJ; Cheatham, M; deBronkart, D; Dixon, J; Drazen, J; Evans, BJ; Hoffman, SM; Holmes, C; Lee, P; Manrai, AK; Omenn, GS; Perlin, JB; Ramoni, R; Sapiro, G; Sarkar, R; Sood, H; Vayena, E; Kohane, IS; RAISE Consortium, To do no harm - and the most good - with AI in health care., Nature medicine, vol. 30 no. 3 (March, 2024), pp. 623-627 [doi]
  6. Gil, NM; Bertran, M; Sapiro, G, Achieving Group Distributional Robustness and Minimax Group Fairness with Interpolating Classifiers, Proceedings of Machine Learning Research, vol. 238 (January, 2024), pp. 2629-2637  [abs]
  7. Farzam, A; Tannenbaum, A; Sapiro, G, From Geometry to Causality-Ricci Curvature and the Reliability of Causal Inference on Networks, Proceedings of Machine Learning Research, vol. 235 (January, 2024), pp. 13086-13108  [abs]
  8. Huang, L; Qiu, Q; Sapiro, G, PQ-VAE: Learning Hierarchical Discrete Representations with Progressive Quantization, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (January, 2024), pp. 7550-7558 [doi]  [abs]
  9. Franz, L; Viljoen, M; Askew, S; Brown, M; Dawson, G; Di Martino, JM; Sapiro, G; Sebolai, K; Seris, N; Shabalala, N; Stahmer, A; Turner, EL; de Vries, PJ, Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial., PLoS One, vol. 19 no. 1 (2024), pp. e0291883 [doi]  [abs]
  10. Nazaret, A; Tonekaboni, S; Darnell, G; Ren, SY; Sapiro, G; Miller, AC, Modeling personalized heart rate response to exercise and environmental factors with wearables data, npj Digital Medicine, vol. 6 no. 1 (December, 2023) [doi]  [abs]
  11. Perochon, S; Di Martino, JM; Carpenter, KLH; Compton, S; Davis, N; Eichner, B; Espinosa, S; Franz, L; Krishnappa Babu, PR; Sapiro, G; Dawson, G, Early detection of autism using digital behavioral phenotyping., Nat Med, vol. 29 no. 10 (October, 2023), pp. 2489-2497 [doi]  [abs]
  12. Schlesinger, O; Kundu, R; Goetz, S; Sapiro, G; Peterchev, AV; Di Martino, JM, Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors., Clin Image Based Proced Fairness AI Med Imaging Ethical Philos Issues Med Imaging (2023), vol. 14242 (October, 2023), pp. 12-20, ISBN 9783031452482 [doi]  [abs]
  13. Nazaret, A; Sapiro, G, A large-scale observational study of the causal effects of a behavioral health nudge., Science advances, vol. 9 no. 38 (September, 2023), pp. eadi1752 [doi]  [abs]
  14. 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]
  15. 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]
  16. 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., Sci Rep, vol. 13 no. 1 (May, 2023), pp. 7158 [doi]  [abs]
  17. 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 Digit Med, vol. 6 no. 1 (February, 2023), pp. 17 [doi]  [abs]
  18. 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 Netw Open, vol. 6 no. 2 (February, 2023), pp. e2254303 [doi]  [abs]
  19. 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., J Child Psychol Psychiatry, vol. 64 no. 1 (January, 2023), pp. 156-166 [doi]  [abs]
  20. 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]
  21. 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 Trans Affect Comput, vol. 14 no. 2 (2023), pp. 919-930 [doi]  [abs]

Schoen, Chadmark L.

  1. Schoen, C, HODGE NUMBERS OF DESINGULARIZED FIBER PRODUCTS OF ELLIPTIC SURFACES, Proceedings of the American Mathematical Society, vol. 152 no. 8 (August, 2024), pp. 3215-3228 [doi]  [abs]
  2. 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]

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]

Tong, Yu

  1. Li, H; Tong, Y; Gefen, T; Ni, H; Ying, L, Heisenberg-limited Hamiltonian learning for interacting bosons, npj Quantum Information, vol. 10 no. 1 (December, 2024) [doi]  [abs]
  2. Cai, Y; Tong, Y; Preskill, J, Stochastic Error Cancellation in Analog Quantum Simulation, Leibniz International Proceedings in Informatics, LIPIcs, vol. 310 (September, 2024) [doi]  [abs]
  3. Zhan, Y; Elben, A; Huang, H-Y; Tong, Y, Learning Conservation Laws in Unknown Quantum Dynamics, PRX Quantum, vol. 5 no. 1 (March, 2024), American Physical Society (APS) [doi]  [abs]
  4. Abrahamsen, N; Tong, Y; Bao, N; Su, Y; Wiebe, N, Entanglement area law for one-dimensional gauge theories and bosonic systems, Physical Review A, vol. 108 no. 4 (October, 2023), American Physical Society (APS) [doi]  [abs]
  5. Low, GH; Su, Y; Tong, Y; Tran, MC, Complexity of Implementing Trotter Steps, PRX Quantum, vol. 4 no. 2 (May, 2023), American Physical Society (APS) [doi]
  6. 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]
  7. Lee, S; Lee, J; Zhai, H; Tong, Y; Dalzell, AM; Kumar, A; Helms, P; Gray, J; Cui, Z-H; Liu, W; Kastoryano, M; Babbush, R; Preskill, J; Reichman, DR; Campbell, ET; Valeev, EF; Lin, L; Chan, GK-L, Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry., Nature communications, vol. 14 no. 1 (April, 2023), pp. 1952 [doi]  [abs]
  8. Fang, D; Lin, L; Tong, Y, Time-marching based quantum solvers for time-dependent linear differential equations, Quantum, vol. 7 (January, 2023) [doi]  [abs]

Viel, Shira

  1. Akin, V; Viel, S, Equity in Grading Systems: Moving Away From “Fair” Towards Transparency and Inclusion in Coordinated Calculus Courses, in Justice through the lens of calculus: Framing new possibilities for diversity, equity, and inclusion., edited by Voigt, M; Hagaman, J; Gehrtz, J; Ratliff, B; Alexander, N; Levy, R, vol. 96 (September, 2023), MAA Press
  2. Hunt, S; Daily, SB; Viel, S; Boyd-Sinkler, K, Examining the Impact of Introductory Mathematics Courses on Undergraduate Students' Desire to Pursue a STEM Major, ASEE Annual Conference and Exposition, Conference Proceedings (June, 2023)
  3. 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]

Wei, Fan

  1. Blekherman, G; Raymond, A; Wei, F, Undecidability of polynomial inequalities in weighted graph homomorphism densities, Forum of Mathematics, Sigma, vol. 12 (March, 2024) [doi]  [abs]
  2. Ruotolo, J; Wang, K; Wei, F, An Asymptotically Sharp Bound on the Maximum Number of Independent Transversals, Electronic Journal of Combinatorics, vol. 31 no. 1 (January, 2024) [doi]  [abs]
  3. 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]
  4. Liu, CH; Wei, F, Phase transition of degeneracy in minor-closed families, Advances in Applied Mathematics, vol. 146 (May, 2023) [doi]  [abs]
  5. 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]
  6. 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]
  7. Przybyło, J; Wei, F, Short Proof of the Asymptotic Confirmation of the Faudree-Lehel Conjecture, Electronic Journal of Combinatorics, vol. 30 no. 4 (January, 2023) [doi]  [abs]

Wickelgren, Kirsten G.

  1. Davis, R; Pries, R; Wickelgren, K, The Galois action on the lower central series of the fundamental group of the Fermat curve, Israel Journal of Mathematics, vol. 261 no. 1 (June, 2024), pp. 171-203 [doi]  [abs]
  2. Bilu, M; Ho, W; Srinivasan, P; Vogt, I; Wickelgren, K, QUADRATIC ENRICHMENT OF THE LOGARITHMIC DERIVATIVE OF THE ZETA FUNCTION, Transactions of the American Mathematical Society Series B, vol. 11 (January, 2024), pp. 1183-1225 [doi]  [abs]
  3. 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]
  4. Bachmann, T; Wickelgren, K, EULER CLASSES: SIX-FUNCTORS FORMALISM, DUALITIES, INTEGRALITY AND LINEAR SUBSPACES OF COMPLETE INTERSECTIONS, Journal of the Institute of Mathematics of Jussieu, vol. 22 no. 2 (March, 2023), pp. 681-746 [doi]  [abs]

Witelski, Thomas P.   (search)

  1. Orizaga, S; Witelski, T, IMEX methods for thin-film equations and Cahn–Hilliard equations with variable mobility, Computational Materials Science, vol. 243 (July, 2024) [doi]  [abs]
  2. Ji, H; Witelski, TP, COARSENING OF THIN FILMS WITH WEAK CONDENSATION, SIAM Journal on Applied Mathematics, vol. 84 no. 2 (January, 2024), pp. 362-386, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  3. 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]
  4. 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]

Wong, Biji

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

Wu, Hau-Tieng

  1. Liu, GR; Sheu, YC; Wu, HT, When scattering transform meets non-Gaussian random processes, a double scaling limit result, Bernoulli, vol. 30 no. 3 (August, 2024), pp. 2346-2371 [doi]  [abs]
  2. Shimizu, R; Wu, H-T, Unveil sleep spindles with concentration of frequency and time (ConceFT)., Physiological measurement, vol. 45 no. 8 (August, 2024) [doi]  [abs]
  3. Chew, J; Hirn, M; Krishnaswamy, S; Needell, D; Perlmutter, M; Steach, H; Viswanath, S; Wu, HT, Geometric scattering on measure spaces, Applied and Computational Harmonic Analysis, vol. 70 (May, 2024) [doi]  [abs]
  4. McErlean, J; Malik, J; Lin, Y-T; Talmon, R; Wu, H-T, Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration., Physiological measurement, vol. 45 no. 3 (April, 2024) [doi]  [abs]
  5. Chung, YM; Huang, WK; Wu, HT, Topological data analysis assisted automated sleep stage scoring using airflow signals, Biomedical Signal Processing and Control, vol. 89 (March, 2024), pp. 105760-105760, Elsevier BV [doi]  [abs]
  6. Ding, X; Wu, HT, How do kernel-based sensor fusion algorithms behave under high-dimensional noise?, Information and Inference, vol. 13 no. 1 (March, 2024), Oxford University Press (OUP) [doi]  [abs]
  7. Liu, T-C; Chen, Y-C; Chen, P-L; Tu, P-H; Yeh, C-H; Yeap, M-C; Wu, Y-H; Chen, C-C; Wu, H-T, Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising., Journal of neuroscience methods, vol. 403 (March, 2024), pp. 110038 [doi]  [abs]
  8. Chiu, N-T; Chuang, B; Anakmeteeprugsa, S; Shelley, KH; Alian, AA; Wu, H-T, Signal quality assessment of peripheral venous pressure., Journal of clinical monitoring and computing, vol. 38 no. 1 (February, 2024), pp. 101-112 [doi]  [abs]
  9. Shnitzer, T; Wu, HT; Talmon, R, Spatiotemporal analysis using Riemannian composition of diffusion operators, Applied and Computational Harmonic Analysis, vol. 68 (January, 2024) [doi]  [abs]
  10. Wu, HT; Zhou, Z, Frequency Detection and Change Point Estimation for Time Series of Complex Oscillation, Journal of the American Statistical Association (January, 2024) [doi]  [abs]
  11. Su, Y-W; Hao, C-C; Liu, G-R; Sheu, Y-C; Wu, H-T, Reconsider photoplethysmogram signal quality assessment in the free living environment, in medRxiv, vol. 45 no. 6 (2024) [doi]  [abs]
  12. Borik, S; Wu, H-T; Shelley, K; Alian, A, Graph connection Laplacian allows for enhanced outcomes of consumer camera based photoplethysmography imaging, in medRxiv, vol. 96 (2024), pp. 106574-106574, Elsevier BV [doi]  [abs]
  13. Marino, F; Wu, H-T; Etzkorn, L; Rooney, M; Soliman, E; Deal, J; Crainiceanu, C; Spira, A; Wanigatunga, A; Schrack, J; Chen, LY, Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: the ARIC Neurocognitive Study, in medRxiv, vol. 24 no. 13 (2024), pp. 4060, MDPI AG [doi]  [abs]
  14. 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, vol. 37 no. 6 (December, 2023), pp. 1521-1531 [doi]  [abs]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. Chen, HY; Wu, HT; Chen, CY, Quality Aware Sleep Stage Classification over RIP Signals with Persistence Diagrams, 2023 IEEE 19th International Conference on Body Sensor Networks, BSN 2023 - Proceedings (January, 2023), ISBN 9798350338416 [doi]  [abs]
  25. Su, PC; Wu, HT, Modern Signal Processing Tools for Fetal Electrocardiogram Extraction from Single-Channel Transabdominal Maternal Electrocardiogram, in Innovative Technologies and Signal Processing in Perinatal Medicine: Volume 2, vol. 2 (January, 2023), pp. 149-169 [doi]  [abs]
  26. 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]

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., Nucleic Acids Res, vol. 52 no. 9 (May, 2024), pp. e46 [doi]  [abs]
  2. 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 (April, 2024) [doi]  [abs]
  3. Li, X; Pura, J; Allen, A; Owzar, K; Lu, J; Harms, M; Xie, J, DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree., Genet Epidemiol, vol. 48 no. 1 (February, 2024), pp. 42-55 [doi]  [abs]
  4. Moris, D; Barfield, R; Chan, C; Chasse, S; Stempora, L; Xie, J; Plichta, JK; Thacker, J; Harpole, DH; Purves, T; Lagoo-Deenadayalan, S; Hwang, E-SS; Kirk, AD, Immune Phenotype and Postoperative Complications After Elective Surgery., Ann Surg, vol. 278 no. 6 (December, 2023), pp. 873-882 [doi]  [abs]
  5. 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]
  6. Li, X; Sung, A; Xie, J, DART: Distance Assisted Recursive Testing, Journal of Machine Learning Research, vol. 24 no. 169 (April, 2023)  [abs]
  7. Pura, JA; Li, X; Chan, C; Xie, J, TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS., Ann Appl Stat, vol. 17 no. 1 (March, 2023), pp. 621-640 [doi]  [abs]
  8. 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]
  9. Hou, L; Ji, Z; Wang, J; Xie, J, Editorial: Statistical and computational methods for single-cell sequencing analysis., Front Genet, vol. 14 (2023), pp. 1235174 [doi]
  10. Li, X; Sung, AD; Xie, J, DART: Distance Assisted Recursive Testing, JOURNAL OF MACHINE LEARNING RESEARCH, vol. 24 (2023)

Xu, Xiaoqian

  1. Feng, Y; Li, L; Liu, JG; Xu, X, EXISTENCE OF WEAK SOLUTIONS TO p-NAVIER-STOKES EQUATIONS, Discrete and Continuous Dynamical Systems - Series B, vol. 29 no. 4 (April, 2024), pp. 1868-1890 [doi]  [abs]
  2. Feng, Y; Tang, M; Xu, X; Zhou, Z, Tumor boundary instability induced by nutrient consumption and supply, Zeitschrift fur Angewandte Mathematik und Physik, vol. 74 no. 3 (June, 2023) [doi]  [abs]
  3. Feng, Y; Hu, B; Xu, X, DISSIPATION ENHANCEMENT FOR A DEGENERATED PARABOLIC EQUATION, Communications in Mathematical Sciences, vol. 21 no. 1 (January, 2023), pp. 173-193 [doi]  [abs]

Yu, Jiajia

  1. Yu, J; Xiao, Q; Chen, T; Lai, R, A bilevel optimization method for inverse mean-field games, Inverse Problems, vol. 40 no. 10 (October, 2024), pp. 105016-105016, IOP Publishing [doi]  [abs]
  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, vol. 93 no. 346 (March, 2024), pp. 603-642, American Mathematical Society (AMS) [doi]  [abs]
  3. 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]  [abs]
  4. 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]  [abs]

Zhang, Shijun

  1. Zhang, S; Lu, J; Zhao, H, On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 41452-41487  [abs]

Zhao, Hongkai

  1. Li, S; Zhang, C; Zhang, Z; Zhao, H, A data-driven and model-based accelerated Hamiltonian Monte Carlo method for Bayesian elliptic inverse problems, Statistics and Computing, vol. 33 no. 4 (August, 2023) [doi]  [abs]
  2. He, Y; Zhao, H; Zhong, Y, How Much Can One Learn a Partial Differential Equation from Its Solution?, Foundations of Computational Mathematics (January, 2023) [doi]  [abs]
  3. Zhang, S; Lu, J; Zhao, H, On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network, Proceedings of Machine Learning Research, vol. 202 (January, 2023), pp. 41452-41487  [abs]
  4. Zhao, H; Zhong, Y, How much can one learn from a single solution of a PDE?, Pure and Applied Functional Analysis, vol. 8 no. 2 (2023), pp. 751-773

Zhou, Ling

  1. Mémoli, F; Stefanou, A; Zhou, L, Persistent cup product structures and related invariants, Journal of Applied and Computational Topology, vol. 8 no. 1 (March, 2024), pp. 93-148, Springer Science and Business Media LLC [doi]  [abs]

Zhu, Hanye

  1. Dong, H; Yang, Z; Zhu, H, Asymptotics of the solution to the perfect conductivity problem with p-Laplacian, Mathematische Annalen (January, 2024) [doi]  [abs]
  2. Dong, H; Zhu, H, Gradient estimates for singular $p$-Laplace type equations with measure data, Journal of the European Mathematical Society, vol. 26 no. 10 (December, 2023), pp. 3939-3985, European Mathematical Society - EMS - Publishing House GmbH [doi]  [abs]
  3. Dong, H; Yang, Z; Zhu, H, The Insulated Conductivity Problem with p-Laplacian, Archive for Rational Mechanics and Analysis, vol. 247 no. 5 (October, 2023) [doi]  [abs]

 

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