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Mathematics Faculty: Publications since January 2019

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

Agarwal, Pankaj K.

  1. Agarwal, PK; Pan, J, Near-Linear Algorithms for Geometric Hitting Sets and Set Covers, Discrete & Computational Geometry, vol. 63 no. 2 (March, 2020), pp. 460-482 [doi]  [abs]
  2. Lowe, A; Agarwal, PK, Flood-risk analysis on terrains under the multiflow-direction model, Acm Transactions on Spatial Algorithms and Systems, vol. 5 no. 4 (September, 2019) [doi]  [abs]
  3. Agarwal, PK; Chang, HC; Xiao, A, Efficient algorithms for geometric partial matching, Leibniz International Proceedings in Informatics, Lipics, vol. 129 (June, 2019), ISBN 9783959771047 [doi]  [abs]
  4. Agarwal, PK; Aronov, B; Ezra, E; Zahl, J, An efficient algorithm for generalized polynomial partitioning and its applications, Leibniz International Proceedings in Informatics, Lipics, vol. 129 (June, 2019), ISBN 9783959771047 [doi]  [abs]
  5. Agarwal, PK; Cohen, R; Halperin, D; Mulzer, W, Maintaining the union of unit discs under insertions with near-optimal overhead, Leibniz International Proceedings in Informatics, Lipics, vol. 129 (June, 2019), ISBN 9783959771047 [doi]  [abs]
  6. Rav, M; Lowe, A; Agarwal, PK, Flood risk analysis on terrains, Acm Transactions on Spatial Algorithms and Systems, vol. 5 no. 1 (May, 2019) [doi]  [abs]

Agazzi, Andrea

  1. Li, L; Krznar, P; Erban, A; Agazzi, A; Martin-Levilain, J; Supale, S; Kopka, J; Zamboni, N; Maechler, P, Metabolomics Identifies a Biomarker Revealing In Vivo Loss of Functional β-Cell Mass Before Diabetes Onset., Diabetes, vol. 68 no. 12 (December, 2019), pp. 2272-2286 [doi]  [abs]

Akin, Victoria S

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

Autry, Eric A.

  1. Clifton, SM; Hill, K; Karamchandani, AJ; Autry, EA; McMahon, P; Sun, G, Mathematical model of gender bias and homophily in professional hierarchies., Chaos (Woodbury, N.Y.), vol. 29 no. 2 (February, 2019), pp. 023135 [doi]  [abs]

Beale, J. Thomas

  1. Beale, JT, Solving partial differential equations on closed surfaces with planar cartesian grids, Siam Journal on Scientific Computing, vol. 42 no. 2 (January, 2020), pp. A1052-A1070 [doi]  [abs]
  2. Tlupova, S; Beale, JT, Regularized single and double layer integrals in 3D Stokes flow, Journal of Computational Physics, vol. 386 (June, 2019), pp. 568-584 [doi]  [abs]
  3. Beale, JT; Ying, W, Solution of the Dirichlet problem by a finite difference analog of the boundary integral equation, Numerische Mathematik, vol. 141 no. 3 (March, 2019), pp. 605-626 [doi]  [abs]

Bendich, Paul L

  1. Bendich, P; Bubenik, P; Wagner, A, Stabilizing the unstable output of persistent homology computations, Journal of Applied and Computational Topology (November, 2019), pp. 1-30, SPRINGER  [abs]
  2. Tralie, CJ; Bendich, P; Harer, J, Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion, Ieee Aerospace Conference Proceedings, vol. 2019-March (March, 2019), ISBN 9781538668542 [doi]  [abs]
  3. Bendich, P, Topology, geometry, and machine-learning for tracking and sensor fusion, Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics, vol. 11017 (January, 2019), pp. lxxxiii-cii, ISBN 9781510627017

Bertozzi, Andrea L

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

Bray, Hubert

  1. Bray, H; Hamm, B; Hirsch, S; Wheeler, J; Zhang, Y, Flatly foliated relativity, Pure and Applied Mathematics Quarterly, vol. 15 no. 2 (January, 2019), pp. 707-747, International Press of Boston [doi]  [abs]

Bryant, Robert   (search)

  1. Bryant, RL; Clelland, JN, Flat metrics with a prescribed derived coframing, Symmetry, Integrability and Geometry: Methods and Applications, vol. 16 (January, 2020) [doi]  [abs]
  2. Bryant, RL; Eastwood, MG; Gover, AR; Neusser, K, Some differential complexes within and beyond parabolic geometry, Advanced Studies in Pure Mathematics, vol. 82 no. Differential Geometry and Tanaka Theory (November, 2019), pp. 13-40, Mathematical Society of Japan  [abs]
  3. Bryant, R; Buckmire, R; Khadjavi, L; Lind, D, The origins of spectra, an organization for LGBT mathematicians, Notices of the American Mathematical Society, vol. 66 no. 6 (June, 2019), pp. 875-882 [doi]

Calderbank, Robert

  1. Beirami, A; Calderbank, R; Christiansen, MM; Duffy, KR; Medard, M, A Characterization of Guesswork on Swiftly Tilting Curves, Ieee Transactions on Information Theory, vol. 65 no. 5 (May, 2019), pp. 2850-2871 [doi]  [abs]
  2. Michelusi, N; Nokleby, M; Mitra, U; Calderbank, R, Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks, Ieee Transactions on Communications, vol. 67 no. 4 (April, 2019), pp. 2673-2688 [doi]  [abs]
  3. Vahid, A; Calderbank, R, Throughput region of spatially correlated interference packet networks, Ieee Transactions on Information Theory, vol. 65 no. 2 (February, 2019), pp. 1220-1235 [doi]  [abs]

Cheng, Cheng

  1. Cheng, C; Jiang, Y; Sun, Q, Spatially distributed sampling and reconstruction, Applied and Computational Harmonic Analysis, vol. 47 no. 1 (July, 2019), pp. 109-148, Elsevier BV [doi]  [abs]

Cheng, Xiuyuan

  1. Cheng, X; Cloninger, A; Coifman, RR, Two-sample statistics based on anisotropic kernels, Information and Inference (December, 2019), Oxford University Press (OUP) [doi]  [abs]
  2. Cheng, X; Rachh, M; Steinerberger, S, On the diffusion geometry of graph Laplacians and applications, Applied and Computational Harmonic Analysis, vol. 46 no. 3 (May, 2019), pp. 674-688, Elsevier BV [doi]
  3. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RoTDCF: Decomposition of convolutional filters for rotation-equivariant deep networks, 7th International Conference on Learning Representations, Iclr 2019 (January, 2019)  [abs]

Dasgupta, Samit

  1. Dasgupta, S; Spiess, M, On the characteristic polynomial of the gross regulator matrix, Transactions of the American Mathematical Society, vol. 372 no. 2 (January, 2019), pp. 803-827 [doi]  [abs]

Daubechies, Ingrid

  1. Sabetsarvestani, Z; Sober, B; Higgitt, C; Daubechies, I; Rodrigues, MRD, Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece., Science Advances, vol. 5 no. 8 (August, 2019), pp. eaaw7416 [doi]  [abs]
  2. Alaifari, R; Daubechies, I; Grohs, P; Yin, R, Stable Phase Retrieval in Infinite Dimensions, Foundations of Computational Mathematics, vol. 19 no. 4 (August, 2019), pp. 869-900, Springer Nature America, Inc [doi]  [abs]
  3. Shan, S; Kovalsky, SZ; Winchester, JM; Boyer, DM; Daubechies, I, ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal, Methods in Ecology and Evolution, vol. 10 no. 4 (April, 2019), pp. 541-552 [doi]  [abs]

Ding, Xiucai

  1. Ding, X, High dimensional deformed rectangular matrices with applications in matrix denoising, Bernoulli, vol. 26 no. 1 (February, 2020), pp. 387-417, Bernoulli Society for Mathematical Statistics and Probability [doi]
  2. Ding, X, Singular vector distribution of sample covariance matrices, Advances in Applied Probability, vol. 51 no. 01 (March, 2019), pp. 236-267, Cambridge University Press (CUP) [doi]  [abs]

Dolbow, John E.

  1. Geelen, R; Plews, J; Tupek, M; Dolbow, J, An extended/generalized phase-field finite element method for crack growth with global-local enrichment, International Journal for Numerical Methods in Engineering, vol. 121 no. 11 (June, 2020), pp. 2534-2557 [doi]  [abs]
  2. Jiang, W; Spencer, BW; Dolbow, JE, Ceramic nuclear fuel fracture modeling with the extended finite element method, Engineering Fracture Mechanics, vol. 223 (January, 2020) [doi]  [abs]
  3. Guilleminot, J; Dolbow, JE, Data-driven enhancement of fracture paths in random composites, Mechanics Research Communications, vol. 103 (January, 2020) [doi]  [abs]
  4. Geelen, RJM; Liu, Y; Hu, T; Tupek, MR; Dolbow, JE, A phase-field formulation for dynamic cohesive fracture, Computer Methods in Applied Mechanics and Engineering, vol. 348 (May, 2019), pp. 680-711 [doi]  [abs]
  5. Asareh, I; Kim, TY; Song, JH; Dolbow, JE, Corrigendum to “A linear complete extended finite element method for dynamic fracture simulation with non-nodal enrichments” [Finite Elem. Anal. Des. 152, 2018](S0168874X18305080)(10.1016/j.finel.2018.09.002), Finite Elements in Analysis and Design, vol. 157 (May, 2019), pp. 50 [doi]  [abs]
  6. Liu, Y; Peco, C; Dolbow, J, A fully coupled mixed finite element method for surfactants spreading on thin liquid films, Computer Methods in Applied Mechanics and Engineering, vol. 345 (March, 2019), pp. 429-453, Elsevier BV [doi]  [abs]
  7. Peco, C; Liu, Y; Rhea, C; Dolbow, JE, Models and simulations of surfactant-driven fracture in particle rafts, International Journal of Solids and Structures, vol. 156-157 (January, 2019), pp. 194-209, Elsevier BV [doi]  [abs]

Dunson, David B.   (search)

  1. Dunson, DB; Johndrow, JE, The Hastings algorithm at fifty, Biometrika, vol. 107 no. 1 (March, 2020), pp. 1-23 [doi]  [abs]
  2. Duan, LL; Young, AL; Nishimura, A; Dunson, DB, Bayesian constraint relaxation., Biometrika, vol. 107 no. 1 (March, 2020), pp. 191-204 [doi]  [abs]
  3. Tikhonov, G; Duan, L; Abrego, N; Newell, G; White, M; Dunson, D; Ovaskainen, O, Computationally efficient joint species distribution modeling of big spatial data., Ecology, vol. 101 no. 2 (February, 2020), pp. e02929 [doi]  [abs]
  4. Ferrari, F; Dunson, DB, Bayesian Factor Analysis for Inference on Interactions, Journal of the American Statistical Association (January, 2020) [doi]  [abs]
  5. Dunson, D; Papamarkou, T, Discussion, International Statistical Review (January, 2020) [doi]
  6. Jauch, M; Hoff, PD; Dunson, DB, Random orthogonal matrices and the Cayley transform, Bernoulli, vol. 26 no. 2 (January, 2020), pp. 1560-1586 [doi]  [abs]
  7. Thai, DH; Wu, HT; Dunson, DB, Locally convex kernel mixtures: Bayesian subspace learning, Proceedings 18th Ieee International Conference on Machine Learning and Applications, Icmla 2019 (December, 2019), pp. 272-275, ISBN 9781728145495 [doi]  [abs]
  8. Camerlenghi, F; Dunson, DB; Lijoi, A; Prunster, I; Rodríguez, A, Latent nested nonparametric priors (with discussion), Bayesian Analysis, vol. 14 no. 4 (December, 2019), pp. 1303-1356 [doi]  [abs]
  9. Zhang, Z; Allen, GI; Zhu, H; Dunson, D, Tensor network factorizations: Relationships between brain structural connectomes and traits., Neuroimage, vol. 197 (August, 2019), pp. 330-343 [doi]  [abs]
  10. Li, C; Lin, L; Dunson, DB, On posterior consistency of tail index for Bayesian kernel mixture models, Bernoulli, vol. 25 no. 3 (August, 2019), pp. 1999-2028, Bernoulli Society for Mathematical Statistics and Probability [doi]
  11. Johndrow, JE; Smith, A; Pillai, N; Dunson, DB, MCMC for Imbalanced Categorical Data, Journal of the American Statistical Association, vol. 114 no. 527 (July, 2019), pp. 1394-1403 [doi]  [abs]
  12. Niu, M; Cheung, P; Lin, L; Dai, Z; Lawrence, N; Dunson, D, Intrinsic Gaussian processes on complex constrained domains, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 81 no. 3 (July, 2019), pp. 603-627 [doi]  [abs]
  13. Wang, L; Zhang, Z; Dunson, D, Symmetric Bilinear Regression for Signal Subgraph Estimation, Ieee Transactions on Signal Processing, vol. 67 no. 7 (April, 2019), pp. 1929-1940 [doi]  [abs]
  14. Zhang, Z; Descoteaux, M; Dunson, DB, Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions., Journal of the American Statistical Association, vol. 114 no. 528 (January, 2019), pp. 1505-1517 [doi]  [abs]
  15. Norberg, A; Abrego, N; Blanchet, FG; Adler, FR; Anderson, BJ; Anttila, J; Araújo, MB; Dallas, T; Dunson, D; Elith, J; Foster, SD; Fox, R; Franklin, J; Godsoe, W; Guisan, A; O'Hara, B; Hill, NA; Holt, RD; Hui, FKC; Husby, M; Kålås, JA; Lehikoinen, A; Luoto, M; Mod, HK; Newell, G; Renner, I; Roslin, T; Soininen, J; Thuiller, W; Vanhatalo, J; Warton, D; White, M; Zimmermann, NE; Gravel, D; Ovaskainen, O, A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels, Ecological Monographs, vol. 89 no. 3 (January, 2019) [doi]  [abs]
  16. Wang, L; Zhang, Z; Dunson, D, Common and individual structure of brain networks, The Annals of Applied Statistics, vol. 13 no. 1 (January, 2019), pp. 85-112 [doi]  [abs]
  17. Miller, JW; Dunson, DB, Robust Bayesian inference via coarsening., Journal of the American Statistical Association, vol. 114 no. 527 (January, 2019), pp. 1113-1125, Informa UK Limited [doi]  [abs]
  18. Li, M; Dunson, DB, Comparing and Weighting Imperfect Models Using D-Probabilities, Journal of the American Statistical Association (January, 2019) [doi]  [abs]
  19. Lin, L; Mu, N; Cheung, P; Dunson, D, Extrinsic Gaussian processes for regression and classification on manifolds, Bayesian Analysis, vol. 14 no. 3 (January, 2019), pp. 887-906 [doi]  [abs]
  20. Chae, M; Lin, L; Dunson, DB, Bayesian sparse linear regression with unknown symmetric error, Information and Inference, vol. 8 no. 3 (January, 2019), pp. 621-653 [doi]  [abs]
  21. Mukhopadhyay, M; Dunson, DB, Targeted Random Projection for Prediction From High-Dimensional Features, Journal of the American Statistical Association (January, 2019) [doi]  [abs]
  22. Badea, A; Wu, W; Shuff, J; Wang, M; Anderson, RJ; Qi, Y; Johnson, GA; Wilson, JG; Koudoro, S; Garyfallidis, E; Colton, CA; Dunson, DB, Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer's Disease., Frontiers in Neuroinformatics, vol. 13 (2019), pp. 72 [doi]  [abs]

Durrett, Richard T.

  1. Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice, Stochastic Processes and Their Applications, vol. 130 no. 2 (February, 2020), pp. 488-502 [doi]  [abs]
  2. Huang, X; Durrett, R, The contact process on periodic trees, Electronic Communications in Probability, vol. 25 (January, 2020) [doi]  [abs]
  3. Durrett, R; Junge, M; Tang, S, Coexistence in chase-escape, Electronic Communications in Probability, vol. 25 (January, 2020) [doi]  [abs]
  4. Wang, Z; Durrett, R, Extrapolating weak selection in evolutionary games., Journal of Mathematical Biology, vol. 78 no. 1-2 (January, 2019), pp. 135-154 [doi]  [abs]
  5. Huo, R; Durrett, R, The Zealot voter model, The Annals of Applied Probability, vol. 29 no. 5 (January, 2019), pp. 3128-3154 [doi]  [abs]

Dym, Nadav

  1. Dym, N; Kovalsky, S, Linearly converging quasi branch and bound algorithms for global rigid registration, Proceedings of the Ieee International Conference on Computer Vision, vol. 2019-October (October, 2019), pp. 1628-1636 [doi]  [abs]
  2. Dym, N; Slutsky, R; Lipman, Y, Linear variational principle for Riemann mappings and discrete conformality., Proceedings of the National Academy of Sciences of the United States of America, vol. 116 no. 3 (January, 2019), pp. 732-737 [doi]  [abs]
  3. Dym, N, Spatial recurrence for ergodic fractal measures, Studia Mathematica, vol. 248 no. 1 (January, 2019), pp. 1-29 [doi]  [abs]
  4. Kushinsky, Y; Maron, H; Dym, N; Lipman, Y, Sinkhorn algorithm for lifted assignment problems, Siam Journal on Imaging Sciences, vol. 12 no. 2 (January, 2019), pp. 716-735, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  5. Dym, N; Sober, B; Daubechies, I, Expression of Fractals Through Neural Network Functions., Corr, vol. abs/1905.11345 (2019)
  6. Dym, N; Kovalsky, SZ, Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration., Iccv (2019), pp. 1628-1636, IEEE, ISBN 978-1-7281-4803-8

Gao, Yuan   (search)

  1. Gao, Y, Global strong solution with BV derivatives to singular solid-on-solid model with exponential nonlinearity, Journal of Differential Equations, vol. 267 no. 7 (September, 2019), pp. 4429-4447 [doi]  [abs]
  2. Gao, Y; Liu, J-G; Lu, XY, Gradient flow approach to an exponential thin film equation: global existence and latent singularity, Esaim: Control, Optimisation and Calculus of Variations, vol. 25 (2019), pp. 49-49, E D P SCIENCES [doi]  [abs]

Getz, Jayce R.

  1. Getz, JR; Liu, B, A refined Poisson summation formula for certain Braverman-Kazhdan spaces, Science China Mathematics (January, 2020) [doi]  [abs]
  2. Getz, JR; Liu, B, A summation formula for triples of quadratic spaces, Advances in Mathematics, vol. 347 (April, 2019), pp. 150-191 [doi]  [abs]

Hain, Richard   (search)

  1. Hain, R, Notes on the universal elliptic KZB connection, Pure and Applied Mathematics Quarterly, vol. 16 no. 2 (January, 2020), pp. 229-312 [doi]  [abs]

Harer, John

  1. Smith, LM; Motta, FC; Chopra, G; Moch, JK; Nerem, RR; Cummins, B; Roche, KE; Kelliher, CM; Leman, AR; Harer, J; Gedeon, T; Waters, NC; Haase, SB, An intrinsic oscillator drives the blood stage cycle of the malaria parasite Plasmodium falciparum., Science (New York, N.Y.), vol. 368 no. 6492 (May, 2020), pp. 754-759 [doi]  [abs]
  2. Tralie, CJ; Bendich, P; Harer, J, Multi-Scale Geometric Summaries for Similarity-Based Sensor Fusion, Ieee Aerospace Conference Proceedings, vol. 2019-March (March, 2019), ISBN 9781538668542 [doi]  [abs]

He, Siming

  1. He, S; Tadmor, E, Suppressing Chemotactic Blow-Up Through a Fast Splitting Scenario on the Plane, Archive for Rational Mechanics and Analysis, vol. 232 no. 2 (May, 2019), pp. 951-986, Springer Nature America, Inc [doi]  [abs]

Hebbar, Pratima

  1. Fernando, K; Hebbar, P, Higher order asymptotics for large deviations – Part I, Asymptotic Analysis (February, 2020), pp. 1-39, IOS Press [doi]

Herschlag, Gregory J.

  1. Herschlag, G; Gounley, J; Roychowdhury, S; Draeger, EW; Randles, A, Multi-physics simulations of particle tracking in arterial geometries with a scalable moving window algorithm, Proceedings Ieee International Conference on Cluster Computing, Iccc, vol. 2019-September (September, 2019), ISBN 9781728147345 [doi]  [abs]
  2. Chin, A; Herschlag, G; Mattingly, J, The Signature of Gerrymandering in Rucho v. Common Cause, South Carolina Law Review, vol. 70 (2019)

Junge, Matthew S

  1. Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice, Stochastic Processes and Their Applications (January, 2019) [doi]  [abs]
  2. Beckman, E; Frank, N; Jiang, Y; Junge, M; Tang, S, The frog model on trees with drift, Electronic Communications in Probability, vol. 24 (January, 2019) [doi]  [abs]
  3. Dygert, B; Kinzel, C; Junge, M; Raymond, A; Slivken, E; Zhu, J, The bullet problem with discrete speeds, Electronic Communications in Probability, vol. 24 (January, 2019) [doi]  [abs]

Kiselev, Alexander A.

  1. Kiselev, A; Li, C, Global regularity and fast small-scale formation for Euler patch equation in a smooth domain, Communications in Partial Differential Equations, vol. 44 no. 4 (April, 2019), pp. 279-308 [doi]  [abs]

Kovalsky, Shahar

  1. Dym, N; Kovalsky, S, Linearly converging quasi branch and bound algorithms for global rigid registration, Proceedings of the Ieee International Conference on Computer Vision, vol. 2019-October (October, 2019), pp. 1628-1636 [doi]  [abs]
  2. Shan, S; Kovalsky, SZ; Winchester, JM; Boyer, DM; Daubechies, I, ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal, Methods in Ecology and Evolution, vol. 10 no. 4 (April, 2019), pp. 541-552 [doi]  [abs]
  3. Dov, D; Kovalsky, SZ; Cohen, J; Range, DE; Henao, R; Carin, L, Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images., edited by Doshi-Velez, F; Fackler, J; Jung, K; Kale, DC; Ranganath, R; Wallace, BC; Wiens, J, Mlhc, vol. 106 (2019), pp. 553-570, PMLR
  4. Dov, D; Kovalsky, SZ; Cohen, J; Range, DE; Henao, R; Carin, L, A Deep-Learning Algorithm for Thyroid Malignancy Prediction From Whole Slide Cytopathology Images., Corr, vol. abs/1904.12739 (2019)
  5. Dov, D; Kovalsky, SZ; Cohen, J; Range, D; Henao, R; Carin, L, Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images., Corr, vol. abs/1904.00839 (2019)

Layton, Anita T.

  1. Ahmed, S; Layton, AT, Sex-specific computational models for blood pressure regulation in the rat., American Journal of Physiology. Renal Physiology, vol. 318 no. 4 (April, 2020), pp. F888-F900 [doi]  [abs]
  2. Edwards, A; Palm, F; Layton, AT, A model of mitochondrial O2 consumption and ATP generation in rat proximal tubule cells., American Journal of Physiology. Renal Physiology, vol. 318 no. 1 (January, 2020), pp. F248-F259 [doi]  [abs]
  3. Hu, R; McDonough, AA; Layton, AT, Functional implications of the sex differences in transporter abundance along the rat nephron: modeling and analysis., American Journal of Physiology. Renal Physiology, vol. 317 no. 6 (December, 2019), pp. F1462-F1474 [doi]  [abs]
  4. Layton, AT, Solute and water transport along an inner medullary collecting duct undergoing peristaltic contractions., American Journal of Physiology. Renal Physiology, vol. 317 no. 3 (September, 2019), pp. F735-F742 [doi]  [abs]
  5. Layton, AT, Multiscale models of kidney function and diseases, Current Opinion in Biomedical Engineering, vol. 11 (September, 2019), pp. 1-8 [doi]  [abs]
  6. Sadria, M; Karimi, S; Layton, AT, Network centrality analysis of eye-gaze data in autism spectrum disorder., Computers in Biology and Medicine, vol. 111 (August, 2019), pp. 103332 [doi]  [abs]
  7. Ahmed, S; Hu, R; Leete, J; Layton, AT, Understanding sex differences in long-term blood pressure regulation: insights from experimental studies and computational modeling., American Journal of Physiology Heart and Circulatory Physiology, vol. 316 no. 5 (May, 2019), pp. H1113-H1123 [doi]  [abs]
  8. Fattah, H; Layton, A; Vallon, V, How Do Kidneys Adapt to a Deficit or Loss in Nephron Number?, Physiology (Bethesda, Md.), vol. 34 no. 3 (May, 2019), pp. 189-197 [doi]  [abs]
  9. Layton, AT, Optimizing SGLT inhibitor treatment for diabetes with chronic kidney diseases., Biological Cybernetics, vol. 113 no. 1-2 (April, 2019), pp. 139-148 [doi]  [abs]
  10. Layton, AT; Layton, HE, A computational model of epithelial solute and water transport along a human nephron., Plos Computational Biology, vol. 15 no. 2 (February, 2019), pp. e1006108 [doi]  [abs]
  11. Layton, AT; Sullivan, JC, Recent advances in sex differences in kidney function., American Journal of Physiology. Renal Physiology, vol. 316 no. 2 (February, 2019), pp. F328-F331 [doi]
  12. Layton, AT, Recent advances in renal epithelial transport., American Journal of Physiology. Renal Physiology, vol. 316 no. 2 (February, 2019), pp. F274-F276 [doi]
  13. Leete, J; Layton, AT, Sex-specific long-term blood pressure regulation: Modeling and analysis., Computers in Biology and Medicine, vol. 104 (January, 2019), pp. 139-148 [doi]  [abs]

Layton, Harold

  1. Layton, AT; Layton, HE, A computational model of epithelial solute and water transport along a human nephron., Plos Computational Biology, vol. 15 no. 2 (February, 2019), pp. e1006108 [doi]  [abs]

Levine, Adam S.

  1. Celoria, D; Golla, M; Levine, AS, Heegaard floer homology and concordance bounds on the Thurston norm, Transactions of the American Mathematical Society, vol. 373 no. 1 (January, 2020), pp. 295-318 [doi]  [abs]
  2. Levine, AS; Zemke, I, Khovanov homology and ribbon concordances, Bulletin of the London Mathematical Society, vol. 51 no. 6 (December, 2019), pp. 1099-1103 [doi]  [abs]
  3. Levine, AS; Lidman, T, SIMPLY CONNECTED, SPINELESS 4-MANIFOLDS, Forum of Mathematics, Sigma (January, 2019) [doi]  [abs]
  4. Levine, AS, Indivisible, The Mathematical Intelligencer (January, 2019) [doi]

Li, Yingzhou

  1. Li, Y; Lu, J; Mao, A, Variational training of neural network approximations of solution maps for physical models, Journal of Computational Physics, vol. 409 (May, 2020) [doi]  [abs]
  2. Hu, W; Liu, J; Li, Y; Ding, Z; Yang, C; Yang, J, Accelerating Excitation Energy Computation in Molecules and Solids within Linear-Response Time-Dependent Density Functional Theory via Interpolative Separable Density Fitting Decomposition., Journal of Chemical Theory and Computation, vol. 16 no. 2 (February, 2020), pp. 964-973 [doi]  [abs]
  3. Wang, Z; Li, Y; Lu, J, Coordinate Descent Full Configuration Interaction., Journal of Chemical Theory and Computation, vol. 15 no. 6 (June, 2019), pp. 3558-3569 [doi]  [abs]
  4. Li, Y; Lu, J, Bold diagrammatic Monte Carlo in the lens of stochastic iterative methods, Transactions of Mathematics and Its Applications, vol. 3 no. 1 (February, 2019), pp. 1-17, Oxford University Press (OUP) [doi]  [abs]
  5. Li, Y; Lin, L, Globally constructed adaptive local basis set for spectral projectors of second order differential operators, Multiscale Modeling & Simulation, vol. 17 no. 1 (January, 2019), pp. 92-116, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  6. Yingzhou, LI; Jianfeng, LU; Wang, AZHE, Coordinatewise descent methods for leading eigenvalue problem, Siam Journal on Scientific Computing, vol. 41 no. 4 (January, 2019), pp. A2681-A2716, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  7. Wang, R; Li, Y; Mahoney, MW; Darve, E, Block basis factorization for scalable kernel evaluation, Siam Journal on Matrix Analysis and Applications, vol. 40 no. 4 (January, 2019), pp. 1497-1526, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  8. Li, L; Liu, J-G; Liu, Z; Lu, J, A stochastic version of Stein Variational Gradient Descent for efficient sampling., Corr, vol. abs/1902.03394 (2019)

Liu, Jian-Guo

  1. Jin, S; Li, L; Liu, JG, Random Batch Methods (RBM) for interacting particle systems, Journal of Computational Physics, vol. 400 (January, 2020) [doi]  [abs]
  2. Li, L; Liu, JG; Yu, P, On the mean field limit for Brownian particles with Coulomb interaction in 3D, Journal of Mathematical Physics, vol. 60 no. 11 (November, 2019) [doi]  [abs] [high impact journal]
  3. Liu, JG; Pego, RL, On Local Singularities in Ideal Potential Flows with Free Surface, Chinese Annals of Mathematics, Series B, vol. 40 no. 6 (November, 2019), pp. 925-948 [doi]  [abs] [reputed journal]
  4. Liu, JG; Pego, RL; Pu, Y, Well-posedness and derivative blow-up for a dispersionless regularized shallow water system, Nonlinearity, vol. 32 no. 11 (October, 2019), pp. 4346-4376 [doi]  [abs] [high impact journal]
  5. Liu, JG; Pego, RL; Slepčev, D, Least action principles for incompressible flows and geodesics between shapes, Calculus of Variations and Partial Differential Equations, vol. 58 no. 5 (October, 2019) [doi]  [abs] [high impact journal]
  6. Lafata, KJ; Zhou, Z; Liu, J-G; Hong, J; Kelsey, CR; Yin, F-F, An Exploratory Radiomics Approach to Quantifying Pulmonary Function in CT Images., Scientific Reports, vol. 9 no. 1 (August, 2019), pp. 11509 [doi]  [abs] [high impact journal]
  7. Liu, JG; Tang, M; Wang, L; Zhou, Z, Analysis and computation of some tumor growth models with nutrient: From cell density models to free boundary dynamics, Discrete and Continuous Dynamical Systems Series B, vol. 24 no. 7 (July, 2019), pp. 3011-3035 [doi]  [abs] [high impact journal]
  8. Zhan, Q; Zhuang, M; Zhou, Z; Liu, J-G; Liu, QH, Complete-Q Model for Poro-Viscoelastic Media in Subsurface Sensing: Large-Scale Simulation With an Adaptive DG Algorithm, Ieee Transactions on Geoscience and Remote Sensing, vol. 57 no. 7 (July, 2019), pp. 4591-4599, Institute of Electrical and Electronics Engineers (IEEE) [doi] [high impact journal]
  9. Liu, JG; Niethammer, B; Pego, RL, Self-similar Spreading in a Merging-Splitting Model of Animal Group Size, Journal of Statistical Physics, vol. 175 no. 6 (June, 2019), pp. 1311-1330 [doi]  [abs] [high impact journal]
  10. Liu, JG; Lu, J; Margetis, D; Marzuola, JL, Asymmetry in crystal facet dynamics of homoepitaxy by a continuum model, Physica D: Nonlinear Phenomena, vol. 393 (June, 2019), pp. 54-67 [doi]  [abs] [high impact journal]
  11. Gao, Y; Li, L; Liu, JG, Patched peakon weak solutions of the modified Camassa–Holm equation, Physica D: Nonlinear Phenomena, vol. 390 (March, 2019), pp. 15-35 [doi]  [abs] [high impact journal]
  12. Zhan, Q; Zhuang, M; Fang, Y; Liu, J-G; Liu, QH, Green's function for anisotropic dispersive poroelastic media based on the Radon transform and eigenvector diagonalization., Proceedings. Mathematical, Physical, and Engineering Sciences, vol. 475 no. 2221 (January, 2019), pp. 20180610 [doi]  [abs] [high impact journal]
  13. Lafata, KJ; Hong, JC; Geng, R; Ackerson, BG; Liu, J-G; Zhou, Z; Torok, J; Kelsey, CR; Yin, F-F, Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy., Phys Med Biol, vol. 64 no. 2 (January, 2019), pp. 025007 [doi]  [abs] [high impact journal]
  14. Huang, H; Liu, JG; Lu, J, Learning interacting particle systems: Diffusion parameter estimation for aggregation equations, Mathematical Models and Methods in Applied Sciences, vol. 29 no. 1 (January, 2019), pp. 1-29 [doi]  [abs] [high impact journal]
  15. Frouvelle, A; Liu, JG, Long-Time Dynamics for a Simple Aggregation Equation on the Sphere, Springer Proceedings in Mathematics and Statistics, vol. 282 (January, 2019), pp. 457-479, ISBN 9783030150952 [doi]  [abs] [reputed journal]
  16. De Hoop, MV; Liu, JG; Markowich, PA; Ussembayev, NS, Plane-wave analysis of a hyperbolic system of equations with relaxation in ℝd, Communications in Mathematical Sciences, vol. 17 no. 1 (January, 2019), pp. 61-79 [doi]  [abs] [high impact journal]
  17. Liu, A; Liu, JG; Lu, Y, On the rate of convergence of empirical measure in ∞-Wasserstein distance for unbounded density function, Quarterly of Applied Mathematics, vol. 77 no. 4 (January, 2019), pp. 811-829 [doi]  [abs] [reputed journal]
  18. Li, L; Liu, JG, A discretization of Caputo derivatives with application to time fractional SDEs and gradient flows, Siam Journal on Numerical Analysis, vol. 57 no. 5 (January, 2019), pp. 2095-2120 [doi]  [abs] [high impact journal]
  19. Liu, JG; Strain, RM, Global stability for solutions to the exponential PDE describing epitaxial growth, Interfaces and Free Boundaries, vol. 21 no. 1 (January, 2019), pp. 61-86 [doi]  [abs] [high impact journal]
  20. Lafata, K; Zhou, Z; Liu, JG; Yin, FF, Data clustering based on Langevin annealing with a self-consistent potential, Quarterly of Applied Mathematics, vol. 77 no. 3 (January, 2019), pp. 591-613 [doi]  [abs] [reputed journal]
  21. Hu, W; Li, CJ; Li, L; Liu, J-G, On the diffusion approximation of nonconvex stochastic gradient descent, Annals of Mathematical Sciences and Applications, vol. 4 no. 1 (2019), pp. 3-32, International Press of Boston [doi] [reputed journal]
  22. Gao, Y; Liu, J-G; Lu, XY, Gradient flow approach to an exponential thin film equation: global existence and latent singularity, Esaim: Control, Optimisation and Calculus of Variations, vol. 25 (2019), pp. 49-49, E D P SCIENCES [doi]  [abs] [high impact journal]

Lu, Jianfeng

  1. Li, Y; Lu, J; Mao, A, Variational training of neural network approximations of solution maps for physical models, Journal of Computational Physics, vol. 409 (May, 2020), pp. 109338-109338, Elsevier BV [doi]  [abs]
  2. Lu, J; Sachs, M; Steinerberger, S, Quadrature Points via Heat Kernel Repulsion, Constructive Approximation, vol. 51 no. 1 (February, 2020), pp. 27-48 [doi]  [abs]
  3. Lu, J; Steinerberger, S, A dimension-free hermite-hadamard inequality via gradient estimates for the torsion function, Proceedings of the American Mathematical Society, vol. 148 no. 2 (January, 2020), pp. 673-679 [doi]  [abs]
  4. Lu, J; Wang, Z, The full configuration interaction quantum monte carlo method through the lens of inexact power iteration, Siam Journal on Scientific Computing, vol. 42 no. 1 (January, 2020), pp. B1-B29 [doi]  [abs]
  5. Cai, Z; Lu, J; Yang, S, Inchworm Monte Carlo Method for Open Quantum Systems, Communications on Pure and Applied Mathematics (January, 2020) [doi]  [abs]
  6. Lu, J; Steinerberger, S, Optimal Trapping for Brownian Motion: a Nonlinear Analogue of the Torsion Function, Potential Analysis (January, 2020) [doi]  [abs]
  7. Lu, J; Watson, AB; Weinstein, MI, Dirac operators and domain walls, Siam Journal on Mathematical Analysis, vol. 52 no. 2 (January, 2020), pp. 1115-1145 [doi]  [abs]
  8. Chen, H; Li, Q; Lu, J, A numerical method for coupling the BGK model and Euler equations through the linearized Knudsen layer, Journal of Computational Physics, vol. 398 (December, 2019) [doi]  [abs]
  9. Lu, J; Sogge, CD; Steinerberger, S, Approximating pointwise products of Laplacian eigenfunctions, Journal of Functional Analysis, vol. 277 no. 9 (November, 2019), pp. 3271-3282 [doi]  [abs]
  10. Cao, Y; Lu, J; Lu, Y, Exponential Decay of Rényi Divergence Under Fokker–Planck Equations, Journal of Statistical Physics, vol. 176 no. 5 (September, 2019), pp. 1172-1184 [doi]  [abs]
  11. Wang, Z; Li, Y; Lu, J, Coordinate Descent Full Configuration Interaction., Journal of Chemical Theory and Computation, vol. 15 no. 6 (June, 2019), pp. 3558-3569 [doi]  [abs]
  12. Liu, JG; Lu, J; Margetis, D; Marzuola, JL, Asymmetry in crystal facet dynamics of homoepitaxy by a continuum model, Physica D: Nonlinear Phenomena, vol. 393 (June, 2019), pp. 54-67 [doi]  [abs]
  13. Cao, Y; Lu, J; Lu, Y, Gradient flow structure and exponential decay of the sandwiched Rényi divergence for primitive Lindblad equations with GNS-detailed balance, Journal of Mathematical Physics, vol. 60 no. 5 (May, 2019), pp. 052202-052202, AIP Publishing [doi]  [abs]
  14. Lin, L; Lu, J; Ying, L, Numerical methods for Kohn-Sham density functional theory, Acta Numerica, vol. 28 (May, 2019), pp. 405-539 [doi]  [abs]
  15. Yu, V; Dawson, W; Garcia, A; Havu, V; Hourahine, B; Huhn, W; Jacquelin, M; Jia, W; Keceli, M; Laasner, R; Li, Y; Lin, L; Lu, J; Roman, J; Vazquez-Mayagoitia, A; Yang, C; Blum, V, Large-scale benchmark of electronic structure solvers with the ELSI infrastructure, Abstracts of Papers of the American Chemical Society, vol. 257 (March, 2019), pp. 1 pages, AMER CHEMICAL SOC
  16. Khoo, Y; Lu, J; Ying, L, Solving for high-dimensional committor functions using artificial neural networks, Research in Mathematical Sciences, vol. 6 no. 1 (March, 2019), Springer Science and Business Media LLC [doi]
  17. Lu, J; Vanden-Eijnden, E, Methodological and Computational Aspects of Parallel Tempering Methods in the Infinite Swapping Limit, Journal of Statistical Physics, vol. 174 no. 3 (February, 2019), pp. 715-733 [doi]  [abs]
  18. Li, Y; Lu, J, Bold diagrammatic Monte Carlo in the lens of stochastic iterative methods, Transactions of Mathematics and Its Applications, vol. 3 no. 1 (February, 2019), pp. 1-17, Oxford University Press (OUP) [doi]  [abs]
  19. Martinsson, A; Lu, J; Leimkuhler, B; Vanden-Eijnden, E, The simulated tempering method in the infinite switch limit with adaptive weight learning, Journal of Statistical Mechanics: Theory and Experiment, vol. 2019 no. 1 (January, 2019), pp. 013207-013207, IOP Publishing [doi]  [abs]
  20. Lu, J; Lu, Y; Nolen, J, Scaling limit of the Stein variational gradient descent: The mean field regime, Siam Journal on Mathematical Analysis, vol. 51 no. 2 (January, 2019), pp. 648-671, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  21. Huang, H; Liu, JG; Lu, J, Learning interacting particle systems: Diffusion parameter estimation for aggregation equations, Mathematical Models and Methods in Applied Sciences, vol. 29 no. 1 (January, 2019), pp. 1-29 [doi]  [abs]
  22. Gauckler, L; Lu, J; Marzuola, JL; Rousset, F; Schratz, K, Trigonometric integrators for quasilinear wave equations, Mathematics of Computation, vol. 88 no. 316 (January, 2019), pp. 717-749, American Mathematical Society (AMS) [doi]  [abs]
  23. Yingzhou, LI; Jianfeng, LU; Wang, AZHE, Coordinatewise descent methods for leading eigenvalue problem, Siam Journal on Scientific Computing, vol. 41 no. 4 (January, 2019), pp. A2681-A2716, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  24. Li, L; Liu, J-G; Liu, Z; Lu, J, A stochastic version of Stein Variational Gradient Descent for efficient sampling., Corr, vol. abs/1902.03394 (2019)
  25. Agazzi, A; Lu, J, Temporal-difference learning for nonlinear value function approximation in the lazy training regime., Corr, vol. abs/1905.10917 (2019)

Maggioni, Mauro

  1. Murphy, JM; Maggioni, M, Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion, Ieee Transactions on Geoscience and Remote Sensing, vol. 57 no. 3 (March, 2019), pp. 1829-1845, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  2. Vogelstein, JT; Bridgeford, EW; Wang, Q; Priebe, CE; Maggioni, M; Shen, C, Discovering and deciphering relationships across disparate data modalities., Elife, vol. 8 (January, 2019) [doi]  [abs]
  3. 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)
  4. 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..)
  5. 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. Lu, Y; Mattingly, JC, Geometric ergodicity of Langevin dynamics with Coulomb interactions, Nonlinearity, vol. 33 no. 2 (January, 2020), pp. 675-699, IOP Publishing [doi]  [abs]
  2. Carter, D; Hunter, Z; Teague, D; Herschlag, G; Mattingly, J, Optimal Legislative County Clustering in North Carolina, Statistics and Public Policy, vol. 7 no. 1 (January, 2020), pp. 19-29 [doi]  [abs]
  3. Herzog, DP; Mattingly, JC, Ergodicity and Lyapunov Functions for Langevin Dynamics with Singular Potentials, Communications on Pure and Applied Mathematics, vol. 72 no. 10 (October, 2019), pp. 2231-2255, WILEY [doi]
  4. Chin, A; Herschlag, G; Mattingly, J, The Signature of Gerrymandering in Rucho v. Common Cause, South Carolina Law Review, vol. 70 (2019)

Miller, Ezra

  1. Katthän, L; Michałek, M; Miller, E, When is a Polynomial Ideal Binomial After an Ambient Automorphism?, Foundations of Computational Mathematics, vol. 19 no. 6 (December, 2019), pp. 1363-1385, Springer Nature America, Inc [doi]  [abs]

Mukherjee, Sayan

  1. Berchuck, SI; Mukherjee, S; Medeiros, FA, Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder., Scientific Reports, vol. 9 no. 1 (December, 2019), pp. 18113 [doi]  [abs]
  2. Cakir, M; Mukherjee, S; Wood, KC, Label propagation defines signaling networks associated with recurrently mutated cancer genes., Scientific Reports, vol. 9 no. 1 (June, 2019), pp. 9401 [doi]  [abs]
  3. Gao, T; Brodzki, J; Mukherjee, S, The Geometry of Synchronization Problems and Learning Group Actions, Discrete & Computational Geometry (January, 2019) [doi]  [abs]
  4. Washburne, AD; Silverman, JD; Morton, JT; Becker, DJ; Crowley, D; Mukherjee, S; David, LA; Plowright, RK, Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data, Ecological Monographs (January, 2019) [doi]  [abs]
  5. Crawford, L; Monod, A; Chen, AX; Mukherjee, S; Rabadán, R, Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis, Journal of the American Statistical Association (January, 2019) [doi]  [abs]

Ng, Lenhard L.

  1. Chantraine, B; Ng, L; Sivek, S, Representations, sheaves and Legendrian (2,m) torus links, Journal of the London Mathematical Society, vol. 100 no. 1 (August, 2019), pp. 41-82, WILEY [doi]  [abs]

Nolen, James H.

  1. Lim, TS; Lu, Y; Nolen, JH, Quantitative propagation of chaos in a bimolecular chemical reaction-diffusion model, Siam Journal on Mathematical Analysis, vol. 52 no. 2 (January, 2020), pp. 2098-2133 [doi]  [abs]
  2. Nolen, J; Roquejoffre, J-M; Ryzhik, L, Refined long-time asymptotics for Fisher–KPP fronts, Communications in Contemporary Mathematics, vol. 21 no. 07 (November, 2019), pp. 1850072-1850072, World Scientific Pub Co Pte Lt [doi]  [abs]
  3. Henderson, NT; Pablo, M; Ghose, D; Clark-Cotton, MR; Zyla, TR; Nolen, J; Elston, TC; Lew, DJ, Ratiometric GPCR signaling enables directional sensing in yeast., Plos Biology, vol. 17 no. 10 (October, 2019), pp. e3000484 [doi]  [abs]
  4. Lu, J; Lu, Y; Nolen, J, Scaling limit of the Stein variational gradient descent: The mean field regime, Siam Journal on Mathematical Analysis, vol. 51 no. 2 (January, 2019), pp. 648-671 [doi]  [abs]

Orizaga, Saulo

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

Petters, Arlie O.

  1. Aazami, AB; Keeton, CR; Petters, AO, Magnification cross sections for the elliptic umbilic caustic surface, Universe, vol. 5 no. 7 (July, 2019) [doi]  [abs]

Pfister, Henry

  1. Rengaswamy, N; Calderbank, AR; Newman, M; Pfister, HD, Classical Coding Problem from Transversal $T$ Gates., Corr, vol. abs/2001.04887 (2020)
  2. Rengaswamy, N; Seshadreesan, KP; Guha, S; Pfister, HD, Quantum-Message-Passing Receiver for Quantum-Enhanced Classical Communications., Corr, vol. abs/2003.04356 (2020)
  3. Pfister, HD; Urbanke, RL, Near-Optimal Finite-Length Scaling for Polar Codes Over Large Alphabets, Ieee Transactions on Information Theory, vol. 65 no. 9 (September, 2019), pp. 5643-5655, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  4. Carpi, F; Hager, C; Martalo, M; Raheli, R; Pfister, HD, Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding, 2019 57th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2019 (September, 2019), pp. 922-929, IEEE [doi]  [abs]
  5. Rengaswamy, N; Calderbank, R; Pfister, HD, Unifying the Clifford hierarchy via symmetric matrices over rings, Physical Review A, vol. 100 no. 2 (August, 2019) [doi]  [abs]
  6. Lian, M; Carpi, F; Hager, C; Pfister, HD, Learned Belief-Propagation Decoding with Simple Scaling and SNR Adaptation, Ieee International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 161-165 [doi]  [abs]
  7. Can, T; Rengaswamy, N; Calderbank, R; Pfister, HD, Kerdock Codes Determine Unitary 2-Designs, Ieee International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 2908-2912 [doi]  [abs]
  8. Tal, I; Pfister, HD; Fazeli, A; Vardy, A, Polar Codes for the Deletion Channel: Weak and Strong Polarization, Ieee International Symposium on Information Theory Proceedings, vol. 2019-July (July, 2019), pp. 1362-1366 [doi]  [abs]
  9. Reeves, G; Pfister, HD, The Replica-Symmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact, Ieee Transactions on Information Theory, vol. 65 no. 4 (April, 2019), pp. 2252-2283 [doi]  [abs]
  10. Schmidt, C; Pfister, HD; Zdeborová, L, Minimal sets to destroy the k-core in random networks., Physical Review. E, vol. 99 no. 2-1 (February, 2019), pp. 022310 [doi]  [abs]
  11. Yoo, I; Imani, MF; Sleasman, T; Pfister, HD; Smith, DR, Enhancing Capacity of Spatial Multiplexing Systems Using Reconfigurable Cavity-Backed Metasurface Antennas in Clustered MIMO Channels, Ieee Transactions on Communications, vol. 67 no. 2 (February, 2019), pp. 1070-1084, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  12. Sheikh, A; GraellAmat, A; Liva, G; Häger, C; Pfister, HD, On Low-Complexity Decoding of Product Codes for High-Throughput Fiber-Optic Systems, International Symposium on Turbo Codes and Iterative Information Processing, Istc, vol. 2018-December (January, 2019) [doi]  [abs]
  13. Lian, M; Häger, C; Pfister, HD, What can machine learning teach us about communications?, 2018 Ieee Information Theory Workshop, Itw 2018 (January, 2019) [doi]  [abs]
  14. Häger, C; Pfister, HD; Bütler, RM; Liga, G; Alvarado, A, Revisiting Multi-Step Nonlinearity Compensation with Machine Learning., Corr, vol. abs/1904.09807 (2019)
  15. Rengaswamy, N; Calderbank, AR; Kadhe, S; Pfister, HD, Logical Clifford Synthesis for Stabilizer Codes., Corr, vol. abs/1907.00310 (2019)
  16. Rengaswamy, N; Calderbank, AR; Newman, M; Pfister, HD, On Optimality of CSS Codes for Transversal T., Corr, vol. abs/1910.09333 (2019)

Pierce, Lillian B.

  1. Alaifari, R; Cheng, X; Pierce, LB; Steinerberger, S, On matrix rearrangement inequalities, Proceedings of the American Mathematical Society, vol. 148 no. 5 (January, 2020), pp. 1835-1848, American Mathematical Society (AMS) [doi]  [abs]
  2. Chruściel, PT; De Mesmay, A; Păun, M; Peyre, E; Barthe, F; Helfgott, HA; Kontsevich, M; Villani, C; Guillermou, S; Hernandez, D; Ma, X; Massot, P; Bergeron, N; Oesterlé, J; Pierce, LB; Rousset, F, Séminaire Bourbaki Volume 2016/2017 Exposés 1120-1135, Astérisque, vol. 407 (January, 2019), pp. 1-602 [doi]  [abs]

Pollack, Aaron

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

Rabinoff, Joseph D

  1. Dupuy, T; Katz, E; Rabinoff, J; Zureick-Brown, D, Total p-differentials on schemes over Z/p2, Journal of Algebra, vol. 524 (April, 2019), pp. 110-123 [doi]  [abs]

Randles, Amanda

  1. Feiger, B; Kochar, A; Gounley, J; Bonadonna, D; Daneshmand, M; Randles, A, Determining the impacts of venoarterial extracorporeal membrane oxygenation on cerebral oxygenation using a one-dimensional blood flow simulator., Journal of Biomechanics, vol. 104 (May, 2020), pp. 109707 [doi]  [abs]
  2. Shi, H; Ames, J; Randles, A, Harvis: an interactive virtual reality tool for hemodynamic modification and simulation, Journal of Computational Science, vol. 43 (May, 2020) [doi]  [abs]
  3. Vardhan, M; Gounley, J; Hegele, L; Draeger, EW; Randles, A, Moment representation in the lattice Boltzmann method on massively parallel hardware, International Conference for High Performance Computing, Networking, Storage and Analysis, Sc (November, 2019), ISBN 9781450362290 [doi]  [abs]
  4. Ames, J; Rizzi, S; Insley, J; Patel, S; Hernández, B; Draeger, EW; Randles, A, Low-Overhead in Situ Visualization Using Halo Replay, 2019 Ieee 9th Symposium on Large Data Analysis and Visualization, Ldav 2019 (October, 2019), pp. 16-26, ISBN 9781728126050 [doi]  [abs]
  5. Herschlag, G; Gounley, J; Roychowdhury, S; Draeger, EW; Randles, A, Multi-physics simulations of particle tracking in arterial geometries with a scalable moving window algorithm, Proceedings Ieee International Conference on Cluster Computing, Iccc, vol. 2019-September (September, 2019), ISBN 9781728147345 [doi]  [abs]
  6. Lee, S; Gounley, J; Randles, A; Vetter, JS, Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures, Journal of Parallel and Distributed Computing, vol. 129 (July, 2019), pp. 1-13 [doi]  [abs]
  7. Dabagh, M; Nair, P; Gounley, J; Frakes, D; Gonzalez, LF; Randles, A, Hemodynamic and morphological characteristics of a growing cerebral aneurysm., Neurosurgical Focus, vol. 47 no. 1 (July, 2019), pp. E13 [doi]  [abs]
  8. Vardhan, M; Gounley, J; Chen, SJ; Kahn, AM; Leopold, JA; Randles, A, The importance of side branches in modeling 3D hemodynamics from angiograms for patients with coronary artery disease., Scientific Reports, vol. 9 no. 1 (June, 2019), pp. 8854 [doi]  [abs]
  9. Feiger, B; Vardhan, M; Gounley, J; Mortensen, M; Nair, P; Chaudhury, R; Frakes, D; Randles, A, Suitability of lattice Boltzmann inlet and outlet boundary conditions for simulating flow in image-derived vasculature., International Journal for Numerical Methods in Biomedical Engineering, vol. 35 no. 6 (June, 2019), pp. e3198 [doi]  [abs]
  10. Grigoryan, B; Paulsen, SJ; Corbett, DC; Sazer, DW; Fortin, CL; Zaita, AJ; Greenfield, PT; Calafat, NJ; Gounley, JP; Ta, AH; Johansson, F; Randles, A; Rosenkrantz, JE; Louis-Rosenberg, JD; Galie, PA; Stevens, KR; Miller, JS, Multivascular networks and functional intravascular topologies within biocompatible hydrogels., Science (New York, N.Y.), vol. 364 no. 6439 (May, 2019), pp. 458-464 [doi]  [abs]
  11. Vardhan, M; Das, A; Gouruev, J; Randles, A, Computational fluid modeling to understand the role of anatomy in bifurcation lesion disease, Proceedings 25th Ieee International Conference on High Performance Computing Workshops, Hipcw 2018 (February, 2019), pp. 56-64, ISBN 9781728101149 [doi]  [abs]
  12. Gounley, J; Vardhan, M; Randles, A, A Framework for Comparing Vascular Hemodynamics at Different Points in Time., Computer Physics Communications, vol. 235 (February, 2019), pp. 1-8 [doi]  [abs]
  13. Dabagh, M; Randles, A, Role of deformable cancer cells on wall shear stress-associated-VEGF secretion by endothelium in microvasculature., Plos One, vol. 14 no. 2 (January, 2019), pp. e0211418 [doi]  [abs]
  14. Gounley, J; Draeger, EW; Oppelstrup, T; Krauss, WD; Gunnels, JA; Chaudhury, R; Nair, P; Frakes, D; Leopold, JA; Randles, A, Computing the ankle-brachial index with parallel computational fluid dynamics., Journal of Biomechanics, vol. 82 (January, 2019), pp. 28-37 [doi]  [abs]
  15. Gounley, J; Draeger, EW; Randles, A, Immersed Boundary Method Halo Exchange in a Hemodynamics Application, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11536 LNCS (January, 2019), pp. 441-455, ISBN 9783030227333 [doi]  [abs]

Reed, Michael C.

  1. Abdalla, A; West, A; Jin, Y; Saylor, RA; Qiang, B; Peña, E; Linden, DJ; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, Fast serotonin voltammetry as a versatile tool for mapping dynamic tissue architecture: I. Responses at carbon fibers describe local tissue physiology., Journal of Neurochemistry, vol. 153 no. 1 (April, 2020), pp. 33-50 [doi]  [abs]
  2. Lawley, SD; Reed, MC; Nijhout, HF, Spiracular fluttering increases oxygen uptake., Plos One, vol. 15 no. 5 (January, 2020), pp. e0232450 [doi]  [abs]
  3. Nijhout, HF; Best, JA; Reed, MC, Systems biology of robustness and homeostatic mechanisms., Wiley Interdisciplinary Reviews. Systems Biology and Medicine, vol. 11 no. 3 (May, 2019), pp. e1440 [doi]  [abs]
  4. West, A; Best, J; Abdalla, A; Nijhout, HF; Reed, M; Hashemi, P, Voltammetric evidence for discrete serotonin circuits, linked to specific reuptake domains, in the mouse medial prefrontal cortex., Neurochemistry International, vol. 123 (February, 2019), pp. 50-58 [doi]  [abs]
  5. Saylor, RA; Hersey, M; West, A; Buchanan, AM; Berger, SN; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, In vivo Hippocampal Serotonin Dynamics in Male and Female Mice: Determining Effects of Acute Escitalopram Using Fast Scan Cyclic Voltammetry., Frontiers in Neuroscience, vol. 13 (January, 2019), pp. 362 [doi]  [abs]
  6. Saylor, RA; Hersey, M; West, A; Buchanan, AM; Berger, SN; Nijhout, HF; Reed, MC; Best, J; Hashemi, P, Corrigendum: In vivo Hippocampal Serotonin Dynamics in Male and Female Mice: Determining Effects of Acute Escitalopram Using Fast Scan Cyclic Voltammetry., Frontiers in Neuroscience, vol. 13 (January, 2019), pp. 726 [doi]  [abs]

Robles, Colleen M

  1. Kerr, M; Pearlstein, GJ; Robles, C, Polarized relations on horizontal SL(2)'s, Documenta Mathematica, vol. 24 (January, 2019), pp. 1295-1360 [doi]  [abs]

Rossman, Benjamin

  1. Rossman, B, Criticality of regular formulas, Leibniz International Proceedings in Informatics, Lipics, vol. 137 (July, 2019), ISBN 9783959771160 [doi]  [abs]
  2. Rossman, B; Srinivasan, S, Separation of AC0[⊕] formulas and circuits, Theory of Computing, vol. 15 (January, 2019) [doi]  [abs]
  3. Rossman, B, Subspace-invariant AC0 formulas, Logical Methods in Computer Science, vol. 15 no. 3 (January, 2019), pp. 3:1-3:12 [doi]  [abs]

Rudin, Cynthia D.

  1. Fisher, A; Rudin, C; Dominici, F, All models are wrong, but many are useful: Learning a variable’s importance by studying an entire class of prediction models simultaneously, Journal of Machine Learning Research, vol. 20 (December, 2019)  [abs]
  2. Wang, F; Rudin, C; Mccormick, TH; Gore, JL, Modeling recovery curves with application to prostatectomy., Biostatistics (Oxford, England), vol. 20 no. 4 (October, 2019), pp. 549-564 [doi]  [abs]
  3. Rudin, C, Do Simpler Models Exist and How Can We Find Them?, Proceedings of the 25th Acm Sigkdd International Conference on Knowledge Discovery & Data Mining (July, 2019), ACM, ISBN 9781450362016 [doi]
  4. Ustun, B; Rudin, C, Learning optimized risk scores, Journal of Machine Learning Research, vol. 20 (June, 2019)  [abs]
  5. Rudin, C; Shaposhnik, Y, Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation (May, 2019)
  6. Bravo, F; Rudin, C; Shaposhnik, Y; Yuan, Y, Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs (May, 2019)
  7. Dieng, A; Liu, Y; Roy, S; Rudin, C; Volfovsky, A, Interpretable Almost-Exact Matching for Causal Inference., Proceedings of Machine Learning Research, vol. 89 (April, 2019), pp. 2445-2453  [abs]
  8. Ban, GY; Rudin, C, The big Data newsvendor: Practical insights from machine learning, Operations Research, vol. 67 no. 1 (January, 2019), pp. 90-108 [doi]  [abs]
  9. Usaid Awan, M; Liu, Y; Morucci, M; Roy, S; Rudin, C; Volfovsky, A, Interpretable almost-matching-exactly with instrumental variables, 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019 (January, 2019)  [abs]
  10. Tracà, S; Rudin, C; Yan, W, Reducing exploration of dying arms in mortal bandits, 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019 (January, 2019)  [abs]
  11. Usaid Awan, M; Liu, Y; Morucci, M; Roy, S; Rudin, C; Volfovsky, A, Interpretable almost-matching-exactly with instrumental variables, 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019 (January, 2019)  [abs]
  12. Tracà, S; Rudin, C; Yan, W, Reducing exploration of dying arms in mortal bandits, 35th Conference on Uncertainty in Artificial Intelligence, Uai 2019 (January, 2019)  [abs]

Ryser, Marc D.

  1. Rozenblatt-Rosen, O; Regev, A; Oberdoerffer, P; Nawy, T; Hupalowska, A; Rood, JE; Ashenberg, O; Cerami, E; Coffey, RJ; Demir, E; Ding, L; Esplin, ED; Ford, JM; Goecks, J; Ghosh, S; Gray, JW; Guinney, J; Hanlon, SE; Hughes, SK; Hwang, ES; Iacobuzio-Donahue, CA; Jané-Valbuena, J; Johnson, BE; Lau, KS; Lively, T; Mazzilli, SA; Pe'er, D; Santagata, S; Shalek, AK; Schapiro, D; Snyder, MP; Sorger, PK; Spira, AE; Srivastava, S; Tan, K; West, RB; Williams, EH; Human Tumor Atlas Network,, The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution., Cell, vol. 181 no. 2 (April, 2020), pp. 236-249 [doi]  [abs]
  2. Ryser, MD; Mallo, D; Hall, A; Hardman, T; King, LM; Tatishchev, S; Sorribes, IC; Maley, CC; Marks, JR; Hwang, ES; Shibata, D, Minimal barriers to invasion during human colorectal tumor growth., Nature Communications, vol. 11 no. 1 (March, 2020), pp. 1280 [doi]  [abs]
  3. Ryser, MD; Hwang, ES, Response to Habel and Buist., J Natl Cancer Inst, vol. 112 no. 2 (February, 2020), pp. 216-217 [doi]
  4. Fridman, I; Kumaresan, V; Vijendra, P; Seshadri, P; Garland, S; Kim, G; Fagerlin, A; Ubel, PA; Ryser, MD, INFORMATION PROCESSING AND PATIENT DECISION MAKING: A BIG DATA APPROACH TO TREATMENT CHOICE IN PROSTATE CANCER PATIENTS, Medical Decision Making : an International Journal of the Society for Medical Decision Making, vol. 40 no. 1 (January, 2020), pp. E183-E184, SAGE PUBLICATIONS INC
  5. Williamson, T; Ryser, MD; Abdelgadir, J; Lemmon, M; Barks, MC; Zakare, R; Ubel, PA, Surgical decision making in the setting of severe traumatic brain injury: A survey of neurosurgeons., Plos One, vol. 15 no. 3 (2020), pp. e0228947 [doi]  [abs]
  6. Ryser, MD; Weaver, DL; Zhao, F; Worni, M; Grimm, LJ; Gulati, R; Etzioni, R; Hyslop, T; Lee, SJ; Hwang, ES, Cancer Outcomes in DCIS Patients Without Locoregional Treatment., J Natl Cancer Inst, vol. 111 no. 9 (September, 2019), pp. 952-960 [doi]  [abs]
  7. Ryser, MD; Hendrix, LH; Worni, M; Liu, Y; Hyslop, T; Hwang, ES, Incidence of Ductal Carcinoma In Situ in the United States, 2000-2014., Cancer Epidemiol Biomarkers Prev, vol. 28 no. 8 (August, 2019), pp. 1316-1323 [doi]  [abs]
  8. Grimm, LJ; Miller, MM; Thomas, SM; Liu, Y; Lo, JY; Hwang, ES; Hyslop, T; Ryser, MD, Growth Dynamics of Mammographic Calcifications: Differentiating Ductal Carcinoma in Situ from Benign Breast Disease., Radiology, vol. 292 no. 1 (July, 2019), pp. 77-83 [doi]  [abs]
  9. Shen, Y; Dong, W; Gulati, R; Ryser, MD; Etzioni, R, Estimating the frequency of indolent breast cancer in screening trials., Stat Methods Med Res, vol. 28 no. 4 (April, 2019), pp. 1261-1271 [doi]  [abs]
  10. Ryser, MD; Gulati, R; Eisenberg, MC; Shen, Y; Hwang, ES; Etzioni, RB, Identification of the Fraction of Indolent Tumors and Associated Overdiagnosis in Breast Cancer Screening Trials., American Journal of Epidemiology, vol. 188 no. 1 (January, 2019), pp. 197-205 [doi]  [abs]

Sapiro, Guillermo

  1. Tenenbaum, EJ; Carpenter, KLH; Sabatos-DeVito, M; Hashemi, J; Vermeer, S; Sapiro, G; Dawson, G, A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder., Autism Res (March, 2020) [doi]  [abs]
  2. Dawson, G; Campbell, K; Hashemi, J; Lippmann, SJ; Smith, V; Carpenter, K; Egger, H; Espinosa, S; Vermeer, S; Baker, J; Sapiro, G, Author Correction: Atypical postural control can be detected via computer vision analysis in toddlers with autism spectrum disorder., Scientific Reports, vol. 10 no. 1 (January, 2020), pp. 616 [doi]  [abs]
  3. Giryes, R; Sapiro, G; Bronstein, AM, Erratum: Deep neural networks with random Gaussian weights: A universal classification strategy? (IEEE Transactions on Signal Processing (2016) 64:13 (3444-3457) DOI: 10.1109/TSP.2016.2546221), Ieee Transactions on Signal Processing, vol. 68 (January, 2020), pp. 529-531 [doi]  [abs]
  4. Wang, Z; DIng, S; Li, Y; Zhao, M; Roychowdhury, S; Wallin, A; Sapiro, G; Qiu, Q, Range adaptation for 3d object detection in LiDAR, Proceedings 2019 International Conference on Computer Vision Workshop, Iccvw 2019 (October, 2019), pp. 2320-2328 [doi]  [abs]
  5. Chang, Z; DI Martino, JM; Qiu, Q; Espinosa, S; Sapiro, G, Salgaze: Personalizing gaze estimation using visual saliency, Proceedings 2019 International Conference on Computer Vision Workshop, Iccvw 2019 (October, 2019), pp. 1169-1178 [doi]  [abs]
  6. Simhal, AK; Zuo, Y; Perez, MM; Madison, DV; Sapiro, G; Micheva, KD, Multifaceted Changes in Synaptic Composition and Astrocytic Involvement in a Mouse Model of Fragile X Syndrome., Scientific Reports, vol. 9 no. 1 (September, 2019), pp. 13855 [doi]  [abs]
  7. Martinez, N; Bertran, M; Sapiro, G; Wu, HT, Non-Contact Photoplethysmogram and Instantaneous Heart Rate Estimation from Infrared Face Video, Proceedings International Conference on Image Processing, Icip, vol. 2019-September (September, 2019), pp. 2020-2024, ISBN 9781538662496 [doi]  [abs]
  8. Asiedu, MN; Simhal, A; Chaudhary, U; Mueller, JL; Lam, CT; Schmitt, JW; Venegas, G; Sapiro, G; Ramanujam, N, Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope., Ieee Trans Biomed Eng, vol. 66 no. 8 (August, 2019), pp. 2306-2318, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  9. Dawson, G; Sapiro, G, Potential for Digital Behavioral Measurement Tools to Transform the Detection and Diagnosis of Autism Spectrum Disorder., Jama Pediatr, vol. 173 no. 4 (April, 2019), pp. 305-306 [doi]
  10. Campbell, K; Carpenter, KL; Hashemi, J; Espinosa, S; Marsan, S; Borg, JS; Chang, Z; Qiu, Q; Vermeer, S; Adler, E; Tepper, M; Egger, HL; Baker, JP; Sapiro, G; Dawson, G, Computer vision analysis captures atypical attention in toddlers with autism., Autism, vol. 23 no. 3 (April, 2019), pp. 619-628 [doi]  [abs]
  11. Shamir, RR; Duchin, Y; Kim, J; Patriat, R; Marmor, O; Bergman, H; Vitek, JL; Sapiro, G; Bick, A; Eliahou, R; Eitan, R; Israel, Z; Harel, N, Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery., Neurosurgery, vol. 84 no. 3 (March, 2019), pp. 749-757 [doi]  [abs]
  12. Sapiro, G; Hashemi, J; Dawson, G, Computer vision and behavioral phenotyping: an autism case study, Current Opinion in Biomedical Engineering, vol. 9 (March, 2019), pp. 14-20 [doi]  [abs]
  13. Kim, J; Duchin, Y; Shamir, RR; Patriat, R; Vitek, J; Harel, N; Sapiro, G, Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation., Human Brain Mapping, vol. 40 no. 2 (February, 2019), pp. 679-698 [doi]  [abs]
  14. Azami, H; Arnold, SE; Sanei, S; Chang, Z; Sapiro, G; Escudero, J; Gupta, AS, Multiscale fluctuation-based dispersion entropy and its applications to neurological diseases, Ieee Access, vol. 7 (January, 2019), pp. 68718-68733 [doi]  [abs]
  15. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RoTDCF: Decomposition of convolutional filters for rotation-equivariant deep networks, 7th International Conference on Learning Representations, Iclr 2019 (January, 2019)  [abs]
  16. Fellous, J-M; Sapiro, G; Rossi, A; Mayberg, H; Ferrante, M, Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation., Frontiers in Neuroscience, vol. 13 (January, 2019), pp. 1346 [doi]  [abs]
  17. Cheng, X; Qiu, Q; Calderbank, R; Sapiro, G, RoTDCF: Decomposition of convolutional filters for rotation-equivariant deep networks, 7th International Conference on Learning Representations, Iclr 2019 (January, 2019)  [abs]
  18. Bertran, M; Martinez, N; Papadaki, A; Qiu, Q; Rodrigues, M; Reeves, G; Sapiro, G, Adversarially learned representations for information obfuscation and inference, 36th International Conference on Machine Learning, Icml 2019, vol. 2019-June (January, 2019), pp. 960-974, ISBN 9781510886988  [abs]

Schoen, Chadmark L.

  1. Schoen, C, On certain complex projective manifolds with Hodge numbers H10 = 4 and h20 = 5, The Michigan Mathematical Journal, vol. 68 no. 3 (January, 2019), pp. 565-596 [doi]

Sober, Barak

  1. Faigenbaum-Golovin, S; Shaus, A; Sober, B; Turkel, E; Piasetzky, E; Finkelstein, I, Algorithmic handwriting analysis of the Samaria inscriptions illuminates bureaucratic apparatus in biblical Israel., Plos One, vol. 15 no. 1 (January, 2020), pp. e0227452 [doi]  [abs]
  2. Sabetsarvestani, Z; Sober, B; Higgitt, C; Daubechies, I; Rodrigues, MRD, Artificial intelligence for art investigation: Meeting the challenge of separating x-ray images of the Ghent Altarpiece., Science Advances, vol. 5 no. 8 (August, 2019), pp. eaaw7416 [doi]  [abs]
  3. Shaus, A; Sober, B; Tzang, O; Ioffe, Z; Cheshnovsky, O; Finkelstein, I; Piasetzky, E, Raman Binary Mapping of Iron Age Ostracon in an Unknown Material Composition and High-Fluorescence Setting—A Proof of Concept, Archaeometry, vol. 61 no. 2 (April, 2019), pp. 459-469, WILEY [doi]  [abs]
  4. Mendel-Geberovich, A; Faigenbaum-Golovin, S; Shaus, A; Sober, B; Cordonsky, M; Piasetzky, E; Finkelstein, I; Milevski, I, A renewed reading of Hebrew ostraca from cave A-2 at ramat beit shemesh (Nahal Yarmut), based on multispectral imaging, Vetus Testamentum, vol. 69 no. 4-5 (January, 2019), pp. 682-701 [doi]  [abs]
  5. Dym, N; Sober, B; Daubechies, I, Expression of Fractals Through Neural Network Functions., Corr, vol. abs/1905.11345 (2019)

Sorribes Rodriguez, Inmaculada C

  1. Ryser, MD; Mallo, D; Hall, A; Hardman, T; King, LM; Tatishchev, S; Sorribes, IC; Maley, CC; Marks, JR; Hwang, ES; Shibata, D, Minimal barriers to invasion during human colorectal tumor growth., Nature Communications, vol. 11 no. 1 (March, 2020), pp. 1280, Springer Science and Business Media LLC [doi]  [abs]
  2. Sorribes, IC; Handelman, SK; Jain, HV, Mitigating temozolomide resistance in glioblastoma via DNA damage-repair inhibition., Journal of the Royal Society, Interface, vol. 17 no. 162 (January, 2020), pp. 20190722, The Royal Society [doi]  [abs]
  3. Sorribes Rodriguez, I, Gliomas diagnosis, progress, and treatment: a mathematical approach, edited by Jain, H (May, 2019)  [abs]
  4. Sorribes, IC; Moore, MNJ; Byrne, HM; Jain, HV, A Biomechanical Model of Tumor-Induced Intracranial Pressure and Edema in Brain Tissue., Biophysical Journal, vol. 116 no. 8 (April, 2019), pp. 1560-1574 [doi]  [abs]
  5. Sorribes, IC; Basu, A; Brady, R; Enriquez-Navas, PM; Feng, X; Kather, JN; Nerlakanti, N; Stephens, R; Strobl, M; Tavassoly, I; Vitos, N; Lemanne, D; Manley, B; O’Farrelly, C; Enderling, H, Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma – Learning to adapt (February, 2019) [doi]  [abs]

Stern, Mark A.

  1. Cherkis, SA; Larrain-Hubach, A; Stern, M, Instantons on multi-Taub-NUT Spaces I: Asymptotic Form and Index Theorem, Journal of Differential Geometry (December, 2019), International Press  [abs]
  2. Cerbo, LFD; Stern, M, Price Inequalities and Betti Number Growth on Manifolds without Conjugate Points, Communications in Analysis and Geometry (August, 2019), International Press  [abs]

Tarokh, Vahid

  1. Angjelichinoski, M; Choi, J; Banerjee, T; Pesaran, B; Tarokh, V, Cross-subject decoding of eye movement goals from local field potentials., Journal of Neural Engineering, vol. 17 no. 1 (February, 2020), pp. 016067 [doi]  [abs]
  2. Jeong, S; Li, X; Yang, J; Li, Q; Tarokh, V, Sparse representation-based denoising for high-resolution brain activation and functional connectivity modeling: A task fMRI study, Ieee Access, vol. 8 (January, 2020), pp. 36728-36740 [doi]  [abs]
  3. Zhou, Y; Wang, Z; Ji, K; Liang, Y; Tarokh, V, Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization., Corr, vol. abs/2002.11582 (2020)
  4. Zhang, Y; Ravier, RJ; Zavlanos, MM; Tarokh, V, A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret, Proceedings of the Ieee Conference on Decision and Control, vol. 2019-December (December, 2019), pp. 2449-2454 [doi]  [abs]
  5. DIao, E; DIng, J; Tarokh, V, Restricted Recurrent Neural Networks, Proceedings 2019 Ieee International Conference on Big Data, Big Data 2019, vol. abs/1908.07724 (December, 2019), pp. 56-63 [doi]  [abs]
  6. Feng, Y; Zhou, Y; Tarokh, V, Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing, Proceedings 2019 Ieee International Conference on Big Data, Big Data 2019 (December, 2019), pp. 2240-2246, ISBN 9781728108582 [doi]  [abs]
  7. Ravier, RJ; Calderbank, AR; Tarokh, V, Prediction in Online Convex Optimization for Parametrizable Objective Functions, Proceedings of the Ieee Conference on Decision and Control, vol. 2019-December (December, 2019), pp. 2455-2460 [doi]  [abs]
  8. Shao, S; Jacob, PE; Ding, J; Tarokh, V, Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency, Journal of the American Statistical Association, vol. 114 no. 528 (October, 2019), pp. 1826-1837 [doi]  [abs]
  9. Zhou, Y; Feng, Y; Tarokh, V; Gintautas, V; McClelland, J; Garagic, D, Multi-Level Mean-Shift Clustering for Single-Channel Radio Frequency Signal Separation, Ieee International Workshop on Machine Learning for Signal Processing, Mlsp, vol. 2019-October (October, 2019), pp. 1-6, IEEE, ISBN 9781728108247 [doi]  [abs]
  10. Angjelichinoski, M; Banerjee, T; Choi, J; Pesaran, B; Tarokh, V, Minimax-optimal decoding of movement goals from local field potentials using complex spectral features., Journal of Neural Engineering, vol. 16 no. 4 (August, 2019), pp. 046001 [doi]  [abs]
  11. Krishnamurthy, S; Bliss, DW; Richmond, CD; Tarokh, V, Peak sidelobe level gumbel distribution of antenna arrays with random phase centers, Ieee Transactions on Antennas and Propagation, vol. 67 no. 8 (August, 2019), pp. 5399-5410 [doi]  [abs]
  12. Banerjee, T; Allsop, S; Tye, KM; Ba, D; Tarokh, V, Sequential Detection of Regime Changes in Neural Data, International Ieee/Embs Conference on Neural Engineering, Ner, vol. 2019-March (May, 2019), pp. 139-142 [doi]  [abs]
  13. Ding, J; Zhou, J; Tarokh, V, Asymptotically Optimal Prediction for Time-Varying Data Generating Processes, Ieee Transactions on Information Theory, vol. 65 no. 5 (May, 2019), pp. 3034-3067 [doi]  [abs]
  14. Xiang, Y; Ding, J; Tarokh, V, Estimation of the evolutionary spectra with application to stationarity test, Ieee Transactions on Signal Processing, vol. 67 no. 5 (March, 2019), pp. 1353-1365, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC [doi]  [abs]
  15. Banerjee, T; Whipps, G; Gurram, P; Tarokh, V, Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data, 2018 Ieee Global Conference on Signal and Information Processing, Globalsip 2018 Proceedings, vol. abs/1807.06945 (February, 2019), pp. 126-130 [doi]  [abs]
  16. Shahrampour, S; Beirami, A; Tarokh, V, Supervised Learning Using Data-dependent Random Features with Application to Seizure Detection, Proceedings of the Ieee Conference on Decision and Control, vol. 2018-December (January, 2019), pp. 1168-1173, ISBN 9781538613955 [doi]  [abs]
  17. Zhou, Y; Yang, J; Zhang, H; Liang, Y; Tarokh, V, SGD converges to global minimum in deep learning via star-convex path, 7th International Conference on Learning Representations, Iclr 2019 (January, 2019)  [abs]
  18. Zhou, Y; Yang, J; Zhang, H; Liang, Y; Tarokh, V, SGD converges to global minimum in deep learning via star-convex path, 7th International Conference on Learning Representations, Iclr 2019 (January, 2019)  [abs]
  19. Zhang, Y; Ravier, RJ; Zavlanos, MM; Tarokh, V, A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret., Cdc (2019), pp. 2449-2454, IEEE
  20. Diao, E; Ding, J; Tarokh, V, Distributed Lossy Image Compression with Recurrent Networks., Corr, vol. abs/1903.09887 (2019)
  21. Diao, E; Ding, J; Tarokh, V, Restricted Recurrent Neural Networks., Bigdata (2019), pp. 56-63, IEEE, ISBN 978-1-7281-0858-2

Vafaee, Faramarz

  1. Ballinger, W; Hsu, C; Mackey, W; Ni, Y; Ochse, T; Vafaee, F, The prism manifold realization problem, Algebraic & Geometric Topology, vol. 20 no. 2 (April, 2020), pp. 757-816, Mathematical Sciences Publishers [doi]
  2. Ni, Y; Vafaee, F, Null surgery on knots in L-spaces, Transactions of the American Mathematical Society, vol. 372 no. 12 (December, 2019), pp. 8279-8306 [doi]  [abs]
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Venakides, Stephanos

  1. Komineas, S; Melcher, C; Venakides, S, Traveling domain walls in chiral ferromagnets, Nonlinearity, vol. 32 no. 7 (May, 2019), pp. 2392-2412, IOP Publishing [doi]  [abs]
  2. Pérez-Arancibia, C; Shipman, SP; Turc, C; Venakides, S, Domain decomposition for quasi-periodic scattering by layered media via robust boundary-integral equations at all frequencies, Communications in Computational Physics, vol. 26 no. 1 (January, 2019), pp. 265-310, Global Science Press [doi]  [abs]
  3. Venakides, S; Komineas, S; Melcher, C, The profile of chiral skyrmions of large radius, Nonlinearity (2019), London Mathematical Society  [abs]
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Wang, Min

  1. Wang, Y; Cheung, SW; Chung, ET; Efendiev, Y; Wang, M, Deep multiscale model learning, Journal of Computational Physics, vol. 406 (April, 2020), pp. 109071-109071, Elsevier BV [doi]  [abs]
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Watson, Alexander

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

Wickelgren, Kirsten G.

  1. Kass, JL; Wickelgren, K, A classical proof that the algebraic homotopy class of a rational function is the residue pairing, Linear Algebra and Its Applications, vol. 595 (June, 2020), pp. 157-181 [doi]  [abs]
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  4. Wickelgren, K; Williams, B, The simplicial EHP sequence in A1–algebraic topology, Geometry & Topology, vol. 23 no. 4 (January, 2019), pp. 1691-1777 [doi]  [abs]
  5. Wickelgren, K; Williams, B, Unstable Motivic Homotopy Theory, in Handbook of Homotopy Theory (2019), CRC Press, ISBN 0815369700  [abs]

Witelski, Thomas P.   (search)

  1. Dijksman, JA; Mukhopadhyay, S; Behringer, RP; Witelski, TP, Thermal Marangoni-driven dynamics of spinning liquid films, Physical Review Fluids, vol. 4 no. 8 (August, 2019) [doi]  [abs]
  2. Bowen, M; Witelski, TP, Pressure-dipole solutions of the thin-film equation, European Journal of Applied Mathematics, vol. 30 no. 2 (April, 2019), pp. 358-399 [doi]  [abs]

Wong, Jeffrey T

  1. Wong, J; Lindstrom, M; Bertozzi, AL, Fast equilibration dynamics of viscous particle-laden flow in an inclined channel, Journal of Fluid Mechanics, vol. 879 (November, 2019), pp. 28-53 [doi]  [abs]

Wu, Hau-Tieng

  1. Wang, S-C; Wu, H-T; Huang, P-H; Chang, C-H; Ting, C-K; Lin, Y-T, Novel Imaging Revealing Inner Dynamics for Cardiovascular Waveform Analysis via Unsupervised Manifold Learning., Anesthesia and Analgesia, vol. 130 no. 5 (May, 2020), pp. 1244-1254 [doi]  [abs]
  2. Liu, G-R; Lustenberger, C; Lo, Y-L; Liu, W-T; Sheu, Y-C; Wu, H-T, Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages., Sensors (Basel, Switzerland), vol. 20 no. 7 (April, 2020) [doi]  [abs]
  3. Liu, Y-W; Kao, S-L; Wu, H-T; Liu, T-C; Fang, T-Y; Wang, P-C, Transient-evoked otoacoustic emission signals predicting outcomes of acute sensorineural hearing loss in patients with Ménière's disease., Acta Oto Laryngologica, vol. 140 no. 3 (March, 2020), pp. 230-235 [doi]  [abs]
  4. Malik, J; Soliman, EZ; Wu, H-T, An adaptive QRS detection algorithm for ultra-long-term ECG recordings., Journal of Electrocardiology, vol. 60 (February, 2020), pp. 165-171 [doi]  [abs]
  5. Lo, Y-L; Wu, H-T; Lin, Y-T; Kuo, H-P; Lin, T-Y, Hypoventilation patterns during bronchoscopic sedation and their clinical relevance based on capnographic and respiratory impedance analysis., Journal of Clinical Monitoring and Computing, vol. 34 no. 1 (February, 2020), pp. 171-179 [doi]  [abs]
  6. Lobmaier, SM; Müller, A; Zelgert, C; Shen, C; Su, PC; Schmidt, G; Haller, B; Berg, G; Fabre, B; Weyrich, J; Wu, HT; Frasch, MG; Antonelli, MC, Fetal heart rate variability responsiveness to maternal stress, non-invasively detected from maternal transabdominal ECG., Archives of Gynecology and Obstetrics, vol. 301 no. 2 (February, 2020), pp. 405-414 [doi]  [abs]
  7. Liu, GR; Lo, YL; Malik, J; Sheu, YC; Wu, HT, Diffuse to fuse EEG spectra – Intrinsic geometry of sleep dynamics for classification, Biomedical Signal Processing and Control, vol. 55 (January, 2020) [doi]  [abs]
  8. Su, P-C; Miller, S; Idriss, S; Barker, P; Wu, H-T, Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage., Physiological Measurement, vol. 40 no. 11 (December, 2019), pp. 115005 [doi]  [abs]
  9. Thai, DH; Wu, HT; Dunson, DB, Locally convex kernel mixtures: Bayesian subspace learning, Proceedings 18th Ieee International Conference on Machine Learning and Applications, Icmla 2019 (December, 2019), pp. 272-275, ISBN 9781728145495 [doi]  [abs]
  10. Talmon, R; Wu, HT, Latent common manifold learning with alternating diffusion: Analysis and applications, Applied and Computational Harmonic Analysis, vol. 47 no. 3 (November, 2019), pp. 848-892, Elsevier BV [doi]  [abs]
  11. Korolj, A; Wu, H-T; Radisic, M, A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems., Biomaterials, vol. 219 (October, 2019), pp. 119363 [doi]  [abs]
  12. Martinez, N; Bertran, M; Sapiro, G; Wu, HT, Non-Contact Photoplethysmogram and Instantaneous Heart Rate Estimation from Infrared Face Video, Proceedings International Conference on Image Processing, Icip, vol. 2019-September (September, 2019), pp. 2020-2024, ISBN 9781538662496 [doi]  [abs]
  13. Alagapan, S; Shin, HW; Fröhlich, F; Wu, H-T, Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography., Journal of Neural Engineering, vol. 16 no. 3 (June, 2019), pp. 036010 [doi]  [abs]
  14. Lu, Y; Wu, HT; Malik, J, Recycling cardiogenic artifacts in impedance pneumography, Biomedical Signal Processing and Control, vol. 51 (May, 2019), pp. 162-170 [doi]  [abs]
  15. Chen, H-Y; Pan, H-C; Chen, Y-C; Chen, Y-C; Lin, Y-H; Yang, S-H; Chen, J-L; Wu, H-T, Traditional Chinese medicine use is associated with lower end-stage renal disease and mortality rates among patients with diabetic nephropathy: a population-based cohort study., Bmc Complementary and Alternative Medicine, vol. 19 no. 1 (April, 2019), pp. 81 [doi]  [abs]
  16. Zhang, JT; Cheng, MY; Wu, HT; Zhou, B, A new test for functional one-way ANOVA with applications to ischemic heart screening, Computational Statistics & Data Analysis, vol. 132 (April, 2019), pp. 3-17, Elsevier BV [doi]  [abs]
  17. Tan, C; Zhang, L; Wu, H-T, A Novel Blaschke Unwinding Adaptive-Fourier-Decomposition-Based Signal Compression Algorithm With Application on ECG Signals., Ieee Journal of Biomedical and Health Informatics, vol. 23 no. 2 (March, 2019), pp. 672-682, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  18. Katz, O; Talmon, R; Lo, YL; Wu, HT, Alternating diffusion maps for multimodal data fusion, Information Fusion, vol. 45 (January, 2019), pp. 346-360, Elsevier BV [doi]  [abs]
  19. Chang, CH; Fang, YL; Wang, YJ; Wu, HT; Lin, YT, Differentiation of skin incision and laparoscopic trocar insertion via quantifying transient bradycardia measured by electrocardiogram, Journal of Clinical Monitoring and Computing (January, 2019) [doi]  [abs]
  20. Shnitzer, T; Lederman, RR; Liu, GR; Talmon, R; Wu, HT, Diffusion operators for multimodal data analysis, Handbook of Numerical Analysis, vol. 20 (January, 2019), pp. 1-39 [doi]  [abs]
  21. Lin, Y-T; Lo, Y-L; Lin, C-Y; Frasch, MG; Wu, H-T, Unexpected sawtooth artifact in beat-to-beat pulse transit time measured from patient monitor data., Plos One, vol. 14 no. 9 (January, 2019), pp. e0221319 [doi]  [abs]

Wu, Nan

  1. Wu, N; Zhu, Z, An Upper Bound for the Smallest Area of a Minimal Surface in Manifolds of Dimension Four, The Journal of Geometric Analysis, vol. 30 no. 1 (January, 2020), pp. 573-600 [doi]  [abs]

 

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