Mathematics Faculty: Publications since January 2018
List all publications in the database. :chronological alphabetical combined bibtex listing:
Agarwal, Pankaj K.
 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]
 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]
 Agarwal, PK; Cohen, R; Halperin, D; Mulzer, W, Maintaining the union of unit discs under insertions with nearoptimal overhead,
Leibniz International Proceedings in Informatics, Lipics, vol. 129
(June, 2019), ISBN 9783959771047 [doi] [abs]
 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]
 Agarwal, PK; Pan, J, NearLinear Algorithms for Geometric Hitting Sets and Set Covers,
Discrete & Computational Geometry
(January, 2019) [doi] [abs]
 Agarwal, PK; Kaplan, H; Kipper, G; Mulzer, W; Rote, G; Sharir, M; Xiao, A, Approximate minimumweight matching with outliers under translation,
Leibniz International Proceedings in Informatics, Lipics, vol. 123
(December, 2018), ISBN 9783959770941 [doi] [abs]
 Lowe, A; Agarwal, PK, Floodrisk analysis on terrains under the multiflowdirection model,
Gis: Proceedings of the Acm International Symposium on Advances in Geographic Information Systems
(November, 2018),
pp. 5362, ACM Press, ISBN 9781450358897 [doi] [abs]
 Agarwal, PK; Kyle, FOX; Salzman, O, An efficient algorithm for computing highquality paths amid polygonal obstacles,
Acm Transactions on Algorithms, vol. 14 no. 4
(August, 2018),
pp. 121, Association for Computing Machinery (ACM) [doi] [abs]
 Agarwal, PK; Kaplan, H; Sharir, M, Union of hypercubes and 3D minkowski sums with random sizes,
Leibniz International Proceedings in Informatics, Lipics, vol. 107
(July, 2018), ISBN 9783959770767 [doi] [abs]
 Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Rangemax queries on uncertain data,
Journal of Computer and System Sciences, vol. 94
(June, 2018),
pp. 118134, Elsevier BV [doi] [abs]
 Agarwal, PK; Arge, L; Staals, F, Improved dynamic geodesic nearest neighbor searching in a simple polygon,
Leibniz International Proceedings in Informatics, Lipics, vol. 99
(June, 2018),
pp. 41414 [doi] [abs]
 Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Computing shortest paths in the plane with removable obstacles,
Leibniz International Proceedings in Informatics, Lipics, vol. 101
(June, 2018),
pp. 51515, ISBN 9783959770682 [doi] [abs]
 Agarwal, PK; Kyle, FOX; Nath, A; Sidiropoulos, A; Wang, Y, Computing the gromovhausdorff distance for metric trees,
Acm Transactions on Algorithms, vol. 14 no. 2
(June, 2018),
pp. 120, Association for Computing Machinery (ACM) [doi] [abs]
 Agarwal, PK; Fox, K; Munagala, K; Nath, A; Pan, J; Taylor, E, Subtrajectory clustering: Models and algorithms,
Proceedings of the Acm Sigact Sigmod Sigart Symposium on Principles of Database Systems
(May, 2018),
pp. 7587, ACM Press, ISBN 9781450347068 [doi] [abs]
 Gao, J; Agarwal, PK; Yang, J, Durable topk queries on temporal data,
Proceedings of the Vldb Endowment, vol. 11 no. 13
(January, 2018),
pp. 22232235 [doi] [abs]
 Agarwal, PK; Fox, K; Nath, A, Maintaining reeb graphs of triangulated 2manifolds,
Leibniz International Proceedings in Informatics, Lipics, vol. 93
(January, 2018), ISBN 9783959770552 [doi] [abs]
Agazzi, Andrea
 Agazzi, A; Dembo, A; Eckmann, JP, On the Geometry of Chemical Reaction Networks: Lyapunov Function and Large Deviations,
Journal of Statistical Physics, vol. 172 no. 2
(July, 2018),
pp. 321352 [doi] [abs]
 Agazzi, A; Dembo, A; Eckmann, JP, Large deviations theory for markov jump models of chemical reaction networks,
The Annals of Applied Probability, vol. 28 no. 3
(June, 2018),
pp. 18211855 [doi] [abs]
Arlotto, Alessandro
 Arlotto, A; Wei, Y; Xie, X, An adaptive O(log n)optimal policy for the online selection of a monotone subsequence from a random sample,
Random Structures & Algorithms, vol. 52 no. 1
(January, 2018),
pp. 4153, WILEY [doi] [abs]
 Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem.,
Corr, vol. abs/1809.02016
(2018)
 Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks,
Management Science
(2018), INFORMS
 Arlotto, A; Steele, JM, A central limit theorem for costs in Bulinskaya’s inventory management problem when deliveries face delays,
Methodology and Computing in Applied Probability, vol. 20 no. 3
(2018),
pp. 839854 [doi] [abs]
Autry, Eric A.
 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]
 Autry, EA; Bayliss, A; Volpert, VA, Biological control with nonlocal interactions.,
Mathematical Biosciences, vol. 301
(July, 2018),
pp. 129146 [doi] [abs]
Beale, J. Thomas
 Tlupova, S; Beale, JT, Regularized single and double layer integrals in 3D Stokes flow,
Journal of Computational Physics, vol. 386
(June, 2019),
pp. 568584 [doi] [abs]
 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. 605626 [doi] [abs]
Bendich, Paul L
 Garagić, D; Peskoe, J; Liu, F; Claffey, MS; Bendich, P; Hineman, J; Borggren, N; Harer, J; Zulch, P; Rhodes, BJ, Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration,
Ieee Aerospace Conference Proceedings, vol. 2018March
(June, 2018),
pp. 18, IEEE, ISBN 9781538620144 [doi] [abs]
 Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric crossmodal comparison of heterogeneous sensor data,
Ieee Aerospace Conference Proceedings, vol. 2018March
(June, 2018),
pp. 110, IEEE [doi] [abs]
Bertozzi, Andrea L
 J. B. Greer and A. L. Bertozzi, H1 solutions of a class of fourth order nonlinear equations for image processing,
Discrete And Continuous Dynamical Systems, vol. 10 no. 12
(2004),
pp. 349  366
Bray, Hubert
 Sormani, C; Bray, HL; Minicozzi, WP; Eichmair, M; Huang, LH; Yau, ST; Uhlenbeck, K; Kusner, R; Codá marques, F; Mese, C; Fraser, A, The Mathematics of Richard Schoen,
Notices of the American Mathematical Society, vol. 65 no. 11
(December, 2018),
pp. 11, American Mathematical Society (AMS) [doi]
 Bray, H; Roesch, H, Proof of a Null Geometry Penrose Conjecture,
Notices of the American Mathematical Society., vol. 65
(February, 2018), American Mathematical Society
Bryant, Robert
(search)
 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. 875882 [doi]
Calderbank, Robert
 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. 28502871 [doi] [abs]
 Michelusi, N; Nokleby, M; Mitra, U; Calderbank, R, MultiScale Spectrum Sensing in Dense MultiCell Cognitive Networks,
Ieee Transactions on Communications, vol. 67 no. 4
(April, 2019),
pp. 26732688 [doi] [abs]
 Vahid, A; Calderbank, R, Throughput region of spatially correlated interference packet networks,
Ieee Transactions on Information Theory, vol. 65 no. 2
(February, 2019),
pp. 12201235 [doi] [abs]
 Zhu, W; Qiu, Q; Huang, J; Calderbank, R; Sapiro, G; Daubechies, I, LDMNet: Low Dimensional Manifold Regularized Neural Networks,
Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition
(December, 2018),
pp. 27432751 [doi] [abs]
 Nguyen, DM; Tsiligianni, E; Calderbank, R; Deligiannis, N, Regularizing autoencoderbased matrix completion models via manifold learning,
European Signal Processing Conference, vol. 2018September
(November, 2018),
pp. 18801884, ISBN 9789082797015 [doi] [abs]
 Mappouras, G; Vahid, A; Calderbank, R; Sorin, DJ, Extending flash lifetime in embedded processors by expanding analog choice,
Ieee Transactions on Computer Aided Design of Integrated Circuits and Systems, vol. 37 no. 11
(November, 2018),
pp. 24622473 [doi] [abs]
 Ahn, HK; Qiu, Q; Bosch, E; Thompson, A; Robles, FE; Sapiro, G; Warren, WS; Calderbank, R, Classifying PumpProbe Images of Melanocytic Lesions Using the WEYL Transform,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018April
(September, 2018),
pp. 42094213, ISBN 9781538646588 [doi] [abs]
 Rengaswamy, N; Calderbank, R; Pfister, HD; Kadhe, S, Synthesis of Logical Clifford Operators via Symplectic Geometry,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 791795 [doi] [abs]
 Vahid, A; Calderbank, R, ARQ for Interference Packet Networks,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 781785, ISBN 9781538647806 [doi] [abs]
 Thompson, A; Calderbank, R, Compressed Neighbour Discovery using Sparse Kerdock Matrices,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 22862290, ISBN 9781538647806 [doi] [abs]
 Michelusi, N; Nokleby, M; Mitra, U; Calderbank, R, Multiscale spectrum sensing in millimeter wave cognitive networks,
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, Acssc 2017, vol. 2017October
(April, 2018),
pp. 16401644, ISBN 9781538618233 [doi] [abs]
 Thompson, A; Calderbank, R, Sparse nearequiangular tight frames with applications in full duplex wireless communication,
2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018January
(March, 2018),
pp. 868872, IEEE, ISBN 9781509059904 [doi] [abs]
 Qiu, Q; Cheng, X; Calderbank, R; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters,
35th International Conference on Machine Learning, Icml 2018, vol. 9
(January, 2018),
pp. 66876696 [abs]
Cheng, Cheng
 Cheng, C; Jiang, Y; Sun, Q, Spatially distributed sampling and reconstruction,
Applied and Computational Harmonic Analysis, vol. 47 no. 1
(July, 2019),
pp. 109148, Elsevier BV [doi]
Cheng, Xiuyuan
 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. 674688, Elsevier BV [doi]
 Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection,
Journal of Number Theory, vol. 185
(April, 2018),
pp. 4864, Elsevier BV [doi]
 Yan, B; Sarkar, P; Cheng, X, Provable estimation of the number of blocks in block models,
International Conference on Artificial Intelligence and Statistics, Aistats 2018
(January, 2018),
pp. 11851194 [abs]
 Qiu, Q; Cheng, X; Calderbank, AR; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters., edited by Dy, JG; Krause, A,
Icml, vol. 80
(2018),
pp. 41954204, PMLR
Dasgupta, Samit
 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. 803827 [doi] [abs]
 Dasgupta, S; Kakde, M; Ventullo, K, On the GrossStark Conjecture,
Annals of Mathematics, vol. 188 no. 3
(November, 2018),
pp. 833870, Annals of Mathematics, Princeton U [doi] [abs]
 Dasgupta, S; Voight, J, Sylvester’s problem and mock heegner points,
Proceedings of the American Mathematical Society, vol. 146 no. 8
(January, 2018),
pp. 32573273, American Mathematical Society (AMS) [doi] [abs]
 Dasgupta, S; Spieß, M, Partial zeta values, Gross's tower of fields conjecture, and GrossStark units,
Journal of the European Mathematical Society, vol. 20 no. 11
(January, 2018),
pp. 26432683, European Mathematical Publishing House [doi] [abs]
Daubechies, Ingrid
 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. 541552 [doi] [abs]
 Zhu, W; Qiu, Q; Huang, J; Calderbank, R; Sapiro, G; Daubechies, I, LDMNet: Low Dimensional Manifold Regularized Neural Networks,
Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition
(December, 2018),
pp. 27432751 [doi] [abs]
 Yin, R; Daubechies, I, Directional Wavelet Bases Constructions with Dyadic Quincunx Subsampling,
Journal of Fourier Analysis and Applications, vol. 24 no. 3
(June, 2018),
pp. 872907, Springer Nature [doi] [abs]
 Gao, T; Yapuncich, GS; Daubechies, I; Mukherjee, S; Boyer, DM, Development and Assessment of Fully Automated and Globally Transitive Geometric Morphometric Methods, With Application to a Biological Comparative Dataset With High Interspecific Variation.,
Anatomical Record (Hoboken, N.J. : 2007), vol. 301 no. 4
(April, 2018),
pp. 636658 [doi] [abs]
 Xu, J; Yang, H; Daubechies, I, Recursive diffeomorphismbased regression for shape functions,
Siam Journal on Mathematical Analysis, vol. 50 no. 1
(January, 2018),
pp. 532, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Alaifari, R; Daubechies, I; Grohs, P; Yin, R, Stable Phase Retrieval in Infinite Dimensions,
Foundations of Computational Mathematics
(January, 2018), Springer Nature America, Inc [doi] [abs]
Ding, Xiucai
 Ding, X; Yang, F, A necessary and sufficient condition for edge universality at the largest singular values of covariance matrices,
The Annals of Applied Probability, vol. 28 no. 3
(June, 2018),
pp. 16791738, Institute of Mathematical Statistics [doi]
Dolbow, John E.
 Geelen, RJM; Liu, Y; Hu, T; Tupek, MR; Dolbow, JE, A phasefield formulation for dynamic cohesive fracture,
Computer Methods in Applied Mechanics and Engineering, vol. 348
(May, 2019),
pp. 680711 [doi] [abs]
 Asareh, I; Kim, TY; Song, JH; Dolbow, JE, Corrigendum to “A linear complete extended finite element method for dynamic fracture simulation with nonnodal 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]
 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. 429453, Elsevier BV [doi] [abs]
 Peco, C; Liu, Y; Rhea, C; Dolbow, JE, Models and simulations of surfactantdriven fracture in particle rafts,
International Journal of Solids and Structures, vol. 156157
(January, 2019),
pp. 194209, Elsevier BV [doi] [abs]
 Geelen, RJM; Liu, Y; Dolbow, JE; RodríguezFerran, A, An optimizationbased phasefield method for continuousdiscontinuous crack propagation,
International Journal for Numerical Methods in Engineering, vol. 116 no. 1
(October, 2018),
pp. 120, WILEY [doi] [abs]
 Zhang, Z; Jiang, W; Dolbow, JE; Spencer, BW, A modified momentfitted integration scheme for XFEM applications with historydependent material data,
Computational Mechanics, vol. 62 no. 2
(August, 2018),
pp. 233252, Springer Nature [doi] [abs]
Dunson, David B.
(search)
 Zhang, Z; Allen, GI; Zhu, H; Dunson, D, Tensor network factorizations: Relationships between brain structural connectomes and traits.,
Neuroimage, vol. 197
(August, 2019),
pp. 330343 [doi] [abs]
 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. 19992028, Bernoulli Society for Mathematical Statistics and Probability [doi]
 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. 603627 [doi] [abs]
 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. 19291940 [doi] [abs]
 Zhang, Z; Descoteaux, M; Dunson, DB, Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions,
Journal of the American Statistical Association
(January, 2019) [doi] [abs]
 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]
 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. 85112 [doi] [abs]
 Li, M; Dunson, DB, Comparing and Weighting Imperfect Models Using DProbabilities,
Journal of the American Statistical Association
(January, 2019) [doi] [abs]
 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. 887906 [doi] [abs]
 Canale, A; Durante, D; Dunson, DB, Convex mixture regression for quantitative risk assessment.,
Biometrics, vol. 74 no. 4
(December, 2018),
pp. 13311340 [doi] [abs]
 Sarkar, A; Chabout, J; Macopson, JJ; Jarvis, ED; Dunson, DB, Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax,
Journal of the American Statistical Association, vol. 113 no. 524
(October, 2018),
pp. 15151527, Informa UK Limited [doi] [abs]
 Zhao, S; Engelhardt, BE; Mukherjee, S; Dunson, DB, Fast Moment Estimation for Generalized Latent Dirichlet Models,
Journal of the American Statistical Association, vol. 113 no. 524
(October, 2018),
pp. 15281540 [doi] [abs]
 Duan, LL; Johndrow, JE; Dunson, DB, Scaling up data augmentation MCMC via calibration,
Journal of Machine Learning Research, vol. 19
(October, 2018) [abs]
 Srivastava, S; Li, C; Dunson, DB, Scalable Bayes via barycenter in Wasserstein space,
Journal of Machine Learning Research, vol. 19
(August, 2018),
pp. 135 [abs]
 van den Boom, W; Mao, C; Schroeder, RA; Dunson, DB, Extremaweighted feature extraction for functional data.,
Bioinformatics, vol. 34 no. 14
(July, 2018),
pp. 24572464 [doi] [abs]
 Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian Conditional Density Filtering,
Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, vol. 27 no. 3
(July, 2018),
pp. 657672, Informa UK Limited [doi] [abs]
 Shterev, ID; Dunson, DB; Chan, C; Sempowski, GD, Bayesian MultiPlate HighThroughput Screening of Compounds.,
Scientific Reports, vol. 8 no. 1
(June, 2018),
pp. 9551 [doi] [abs]
 Johndrow, JE; Lum, K; Dunson, DB, Theoretical limits of microclustering for record linkage.,
Biometrika, vol. 105 no. 2
(June, 2018),
pp. 431446 [doi] [abs]
 Dunson, DB, Statistics in the big data era: Failures of the machine,
Statistics & Probability Letters, vol. 136
(May, 2018),
pp. 49, Elsevier BV [doi] [abs]
 Zhang, Z; Descoteaux, M; Zhang, J; Girard, G; Chamberland, M; Dunson, D; Srivastava, A; Zhu, H, Mapping populationbased structural connectomes.,
Neuroimage, vol. 172
(May, 2018),
pp. 130145 [doi] [abs]
 van den Boom, W; Schroeder, RA; Manning, MW; Setji, TL; Fiestan, GO; Dunson, DB, Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries.,
Diabetes Care, vol. 41 no. 4
(April, 2018),
pp. 782788 [doi] [abs]
 Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from twophoton imaging data.,
Plos One, vol. 13 no. 5
(January, 2018),
pp. e0196527 [doi] [abs]
 Miller, JW; Dunson, DB, Robust Bayesian Inference via Coarsening,
Journal of the American Statistical Association
(January, 2018),
pp. 113, Informa UK Limited [doi] [abs]
 Johndrow, JE; Smith, A; Pillai, N; Dunson, DB, MCMC for Imbalanced Categorical Data,
Journal of the American Statistical Association
(January, 2018) [doi] [abs]
 Durante, D; Dunson, DB, Bayesian inference and testing of group differences in brain networks,
Bayesian Analysis, vol. 13 no. 1
(January, 2018),
pp. 2958, Institute of Mathematical Statistics [doi] [abs]
Durrett, Richard T.
 Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice,
Stochastic Processes and Their Applications
(January, 2019) [doi] [abs]
 Wang, Z; Durrett, R, Extrapolating weak selection in evolutionary games.,
Journal of Mathematical Biology, vol. 78 no. 12
(January, 2019),
pp. 135154 [doi] [abs]
 Ma, R; Durrett, R, A simple evolutionary game arising from the study of the role of igfII in pancreatic cancer,
The Annals of Applied Probability, vol. 28 no. 5
(October, 2018),
pp. 28962921, Institute of Mathematical Statistics [doi] [abs]
 Talkington, A; Dantoin, C; Durrett, R, Ordinary Differential Equation Models for Adoptive Immunotherapy.,
Bulletin of Mathematical Biology, vol. 80 no. 5
(May, 2018),
pp. 10591083 [doi] [abs]
 Huo, R; Durrett, R, Latent voter model on locally treelike random graphs,
Stochastic Processes and Their Applications, vol. 128 no. 5
(May, 2018),
pp. 15901614, Elsevier BV [doi] [abs]
 Beckman, E; Dinan, E; Durrett, R; Huo, R; Junge, M, Asymptotic behavior of the brownian frog model,
Electronic Journal of Probability, vol. 23
(January, 2018), Institute of Mathematical Statistics [doi] [abs]
 Basak, A; Durrett, R; Foxall, E, Diffusion limit for the partner model at the critical value,
Electronic Journal of Probability, vol. 23
(January, 2018), Institute of Mathematical Statistics [doi] [abs]
 Cristali, I; Ranjan, V; Steinberg, J; Beckman, E; Durrett, R; Junge, M; Nolen, J, Block size in geometric(P)biased permutations,
Electronic Communications in Probability, vol. 23
(January, 2018),
pp. 110, Institute of Mathematical Statistics [doi] [abs]
Dym, Nadav
 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. 732737 [doi] [abs]
 Dym, N, Spatial recurrence for ergodic fractal measures,
Studia Mathematica, vol. 248 no. 1
(January, 2019),
pp. 129 [doi] [abs]
 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. 716735, Society for Industrial & Applied Mathematics (SIAM) [doi]
 Lazar, R; Dym, N; Kushinsky, Y; Huang, Z; Ju, T; Lipman, Y, Robust optimization for topological surface reconstruction,
Acm Transactions on Graphics, vol. 37 no. 4
(January, 2018),
pp. 110, Association for Computing Machinery (ACM) [doi] [abs]
Freeman, Daniel
 Freeman, D; Odell, E; Sarı, B; Zheng, B, On spreading sequences and asymptotic structures,
Transactions of the American Mathematical Society, vol. 370 no. 10
(April, 2018),
pp. 69336953, American Mathematical Society (AMS) [doi]
Getz, Jayce R.
 Getz, JR; Liu, B, A summation formula for triples of quadratic spaces,
Advances in Mathematics, vol. 347
(April, 2019),
pp. 150191 [doi] [abs]
 Getz, JR, Secondary terms in asymptotics for the number of zeros of quadratic forms over number fields,
Journal of the London Mathematical Society, vol. 98 no. 2
(October, 2018),
pp. 275305, WILEY [doi] [abs]
 Getz, JR, Nonabelian fourier transforms for spherical representations,
Pacific Journal of Mathematics, vol. 294 no. 2
(January, 2018),
pp. 351373, Mathematical Sciences Publishers [doi] [abs]
Hain, Richard
(search)
 Brown, F; Hain, R, Algebraic de Rham theory for weakly holomorphic modular forms of level one,
Algebra & Number Theory, vol. 12 no. 3
(January, 2018),
pp. 723750 [doi] [abs]
Harer, John
 Tralie, CJ; Bendich, P; Harer, J, MultiScale Geometric Summaries for SimilarityBased Sensor Fusion,
Ieee Aerospace Conference Proceedings, vol. 2019March
(March, 2019) [doi] [abs]
 Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric crossmodal comparison of heterogeneous sensor data,
Ieee Aerospace Conference Proceedings, vol. 2018March
(June, 2018),
pp. 110, IEEE [doi] [abs]
 Garagić, D; Peskoe, J; Liu, F; Claffey, MS; Bendich, P; Hineman, J; Borggren, N; Harer, J; Zulch, P; Rhodes, BJ, Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration,
Ieee Aerospace Conference Proceedings, vol. 2018March
(June, 2018),
pp. 18, IEEE, ISBN 9781538620144 [doi] [abs]
He, Siming
 He, S; Tadmor, E, Suppressing Chemotactic BlowUp Through a Fast Splitting Scenario on the Plane,
Archive for Rational Mechanics and Analysis, vol. 232 no. 2
(May, 2019),
pp. 951986, Springer Nature America, Inc [doi] [abs]
 He, S, Suppression of blowup in parabolicparabolic PatlakKellerSegel via strictly monotone shear flows,
Nonlinearity, vol. 31 no. 8
(July, 2018),
pp. 36513688, IOP Publishing [doi] [abs]
 Bedrossian, J; He, S, Erratum: Suppression of blowup in patlakkellersegel via shear flows (SIAM Journal on Mathematical Analysis (2017) 49 (47224766) DOI: 10.1137/16M1093380),
Siam Journal on Mathematical Analysis, vol. 50 no. 6
(January, 2018),
pp. 63656372 [doi] [abs]
Herschlag, Gregory J.
 Herschlag, G; Lee, S; Vetter, JS; Randles, A, GPU data access on complex geometries for D3Q19 lattice boltzmann method,
Proceedings 2018 Ieee 32nd International Parallel and Distributed Processing Symposium, Ipdps 2018
(August, 2018),
pp. 825834, IEEE, ISBN 9781538643686 [doi] [abs]
Junge, Matthew S
 Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice,
Stochastic Processes and Their Applications
(January, 2019) [doi] [abs]
 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]
 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]
 Brito, G; Fowler, C; Junge, M; Levy, A, Ewens Sampling and Invariable Generation,
Combinatorics, Probability and Computing, vol. 27 no. 6
(November, 2018),
pp. 853891, Cambridge University Press (CUP) [doi] [abs]
 Johnson, T; Junge, M, Stochastic orders and the frog model,
Annales De L'Institut Henri Poincaré, Probabilités Et Statistiques, vol. 54 no. 2
(May, 2018),
pp. 10131030, Institute of Mathematical Statistics [doi] [abs]
 Foxall, E; Hutchcroft, T; Junge, M, Coalescing random walk on unimodular graphs,
Electronic Communications in Probability, vol. 23
(January, 2018), Institute of Mathematical Statistics [doi] [abs]
 Beckman, E; Dinan, E; Durrett, R; Huo, R; Junge, M, Asymptotic behavior of the brownian frog model,
Electronic Journal of Probability, vol. 23
(January, 2018), Institute of Mathematical Statistics [doi] [abs]
 Cristali, I; Ranjan, V; Steinberg, J; Beckman, E; Durrett, R; Junge, M; Nolen, J, Block size in geometric(P)biased permutations,
Electronic Communications in Probability, vol. 23
(January, 2018), Institute of Mathematical Statistics [doi] [abs]
Kiselev, Alexander A.
 Kiselev, A; Li, C, Global regularity and fast smallscale formation for Euler patch equation in a smooth domain,
Communications in Partial Differential Equations, vol. 44 no. 4
(April, 2019),
pp. 279308 [doi] [abs]
 Do, T; Kiselev, A; Xu, X, Stability of Blowup for a 1D Model of Axisymmetric 3D Euler Equation,
Journal of Nonlinear Science, vol. 28 no. 6
(December, 2018),
pp. 21272152, Springer Nature America, Inc [doi] [abs]
 Kiselev, A, Special Issue Editorial: Small Scales and Singularity Formation in Fluid Dynamics,
Journal of Nonlinear Science, vol. 28 no. 6
(December, 2018),
pp. 20472050, Springer Nature America, Inc [doi]
 Do, T; Kiselev, A; Ryzhik, L; Tan, C, Global Regularity for the Fractional Euler Alignment System,
Archive for Rational Mechanics and Analysis, vol. 228 no. 1
(April, 2018),
pp. 137, Springer Nature [doi] [abs]
 Kiselev, A; Tan, C, Finite time blow up in the hyperbolic Boussinesq system,
Advances in Mathematics, vol. 325
(February, 2018),
pp. 3455, Elsevier BV [doi] [abs]
 Kiselev, A; Tan, C, Global regularity for 1D eulerian dynamics with singular interaction forces,
Siam Journal on Mathematical Analysis, vol. 50 no. 6
(January, 2018),
pp. 62086229, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
Kovalsky, Shahar
 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. 541552 [doi] [abs]
Layton, Anita T.
 Ahmed, S; Hu, R; Leete, J; Layton, AT, Understanding sex differences in longterm 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. H1113H1123 [doi] [abs]
 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. 189197 [doi] [abs]
 Layton, AT, Optimizing SGLT inhibitor treatment for diabetes with chronic kidney diseases.,
Biological Cybernetics, vol. 113 no. 12
(April, 2019),
pp. 139148 [doi] [abs]
 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]
 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. F328F331 [doi]
 Layton, AT, Recent advances in renal epithelial transport.,
American Journal of Physiology. Renal Physiology, vol. 316 no. 2
(February, 2019),
pp. F274F276 [doi]
 Leete, J; Layton, AT, Sexspecific longterm blood pressure regulation: Modeling and analysis.,
Computers in Biology and Medicine, vol. 104
(January, 2019),
pp. 139148 [doi] [abs]
 Li, Q; McDonough, AA; Layton, HE; Layton, AT, Functional implications of sexual dimorphism of transporter patterns along the rat proximal tubule: modeling and analysis.,
American Journal of Physiology. Renal Physiology, vol. 315 no. 3
(September, 2018),
pp. F692F700 [doi] [abs]
 Wei, N; Gumz, ML; Layton, AT, Predicted effect of circadian clock modulation of NHE3 of a proximal tubule cell on sodium transport.,
American Journal of Physiology. Renal Physiology, vol. 315 no. 3
(September, 2018),
pp. F665F676 [doi] [abs]
 Layton, AT; Vallon, V, Renal tubular solute transport and oxygen consumption: insights from computational models.,
Current Opinion in Nephrology and Hypertension, vol. 27 no. 5
(September, 2018),
pp. 384389 [doi] [abs]
 Ciocanel, MV; Stepien, TL; Sgouralis, I; Layton, AT, A multicellular vascular model of the renal myogenic response,
Processes, vol. 6 no. 7
(July, 2018) [doi] [abs]
 Layton, AT, Sweet success? SGLT2 inhibitors and diabetes.,
American Journal of Physiology. Renal Physiology, vol. 314 no. 6
(June, 2018),
pp. F1034F1035 [doi]
 Layton, AT; Vallon, V, SGLT2 inhibition in a kidney with reduced nephron number: modeling and analysis of solute transport and metabolism.,
American Journal of Physiology. Renal Physiology, vol. 314 no. 5
(May, 2018),
pp. F969F984 [doi] [abs]
 Leete, J; Gurley, S; Layton, A, Modeling Sex Differences in the Renin Angiotensin System and the Efficacy of Antihypertensive Therapies.,
Computers & Chemical Engineering, vol. 112
(April, 2018),
pp. 253264, Elsevier BV [doi] [abs]
 Layton, AT; Edwards, A; Vallon, V, Renal potassium handling in rats with subtotal nephrectomy: modeling and analysis.,
American Journal of Physiology. Renal Physiology, vol. 314 no. 4
(April, 2018),
pp. F643F657 [doi] [abs]
 Layton, AT; Vallon, V, Cardiovascular benefits of SGLT2 inhibition in diabetes and chronic kidney diseases.,
Acta Physiologica, vol. 222 no. 4
(April, 2018),
pp. e13050 [doi]
 Wei, N; Layton, AT, Theoretical assessment of the Ca 2 + oscillations in the afferent arteriole smooth muscle cell of the rat kidney,
International Journal of Biomathematics, vol. 11 no. 3
(April, 2018),
pp. 18500431850043, World Scientific Pub Co Pte Lt [doi] [abs]
Layton, Harold
 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]
 Li, Q; McDonough, AA; Layton, HE; Layton, AT, Functional implications of sexual dimorphism of transporter patterns along the rat proximal tubule: modeling and analysis.,
American Journal of Physiology. Renal Physiology, vol. 315 no. 3
(September, 2018),
pp. F692F700 [doi] [abs]
Levine, Adam S.
 Levine, AS; Lidman, T, SIMPLY CONNECTED, SPINELESS 4MANIFOLDS,
Forum of Mathematics, Sigma
(January, 2019) [doi] [abs]
 Levine, AS; Ruberman, D, Heegaard Floer invariants in codimension one,
Transactions of the American Mathematical Society
(2018),
pp. 11, American Mathematical Society (AMS) [doi]
Li, Yingzhou
 Wang, Z; Li, Y; Lu, J, Coordinate Descent Full Configuration Interaction.,
Journal of Chemical Theory and Computation, vol. 15 no. 6
(June, 2019),
pp. 35583569 [doi] [abs]
 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. 117, Oxford University Press (OUP) [doi] [abs]
 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. 92116, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Li, Y; Yang, H; Ying, L, Multidimensional butterfly factorization,
Applied and Computational Harmonic Analysis, vol. 44 no. 3
(May, 2018),
pp. 737758, Elsevier BV [doi] [abs]
 Wang, R; Li, Y; Darve, E, On the Numerical Rank of Radial Basis Function Kernels in High Dimensions,
Siam Journal on Matrix Analysis and Applications, vol. 39 no. 4
(January, 2018),
pp. 18101835, Society for Industrial & Applied Mathematics (SIAM) [doi]
Liu, JianGuo
 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. 5467 [doi] [abs]
 Lafata, KJ; Hong, JC; Geng, R; Ackerson, BG; Liu, JG; Zhou, Z; Torok, J; Kelsey, CR; Yin, FF, Association of pretreatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.,
Phys Med Biol, vol. 64 no. 2
(January, 2019),
pp. 025007 [doi] [abs]
 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. 129 [doi] [abs]
 Gao, Y; Ji, H; Liu, JG; Witelski, TP, A vicinal surface model for epitaxial growth with logarithmic free energy,
Discrete and Continuous Dynamical Systems Series B, vol. 23 no. 10
(December, 2018),
pp. 44334453, American Institute of Mathematical Sciences (AIMS) [doi] [abs]
 Feng, Y; Li, L; Liu, JG; Xu, X, Continuous and discrete one dimensional autonomous fractional odes,
Discrete and Continuous Dynamical Systems Series B, vol. 23 no. 8
(October, 2018),
pp. 31093135, American Institute of Mathematical Sciences (AIMS) [doi] [abs]
 Feng, Y; Li, L; Liu, JG; Xu, X, A note on onedimensional time fractional ODEs,
Applied Mathematics Letters, vol. 83
(September, 2018),
pp. 8794, Elsevier BV [doi] [abs]
 Li, L; Liu, JG; Wang, L, Cauchy problems for Keller–Segel type time–space fractional diffusion equation,
Journal of Differential Equations, vol. 265 no. 3
(August, 2018),
pp. 10441096, Elsevier BV [doi] [abs]
 Liu, JG; Tang, M; Wang, L; Zhou, Z, An accurate front capturing scheme for tumor growth models with a free boundary limit,
Journal of Computational Physics, vol. 364
(July, 2018),
pp. 7394, Elsevier BV [doi] [abs]
 Chen, K; Li, Q; Liu, JG, Online learning in optical tomography: A stochastic approach,
Inverse Problems, vol. 34 no. 7
(May, 2018),
pp. 075010075010, IOP Publishing [doi] [abs]
 Liu, JG; Xu, X, Partial regularity of weak solutions to a PDE system with cubic nonlinearity,
Journal of Differential Equations, vol. 264 no. 8
(April, 2018),
pp. 54895526, ACADEMIC PRESS INC ELSEVIER SCIENCE [doi] [abs]
 Li, L; Liu, JG, pEuler equations and pNavier–Stokes equations,
Journal of Differential Equations, vol. 264 no. 7
(April, 2018),
pp. 47074748, Elsevier BV [doi] [abs]
 Gao, Y; Liu, JG; Lu, XY; Xu, X, Maximal monotone operator theory and its applications to thin film equation in epitaxial growth on vicinal surface,
Calculus of Variations and Partial Differential Equations, vol. 57 no. 2
(April, 2018), Springer Nature [doi] [abs]
 Feng, Y; Li, L; Liu, JG, Semigroups of stochastic gradient descent and online principal component analysis: Properties and diffusion approximations,
Communications in Mathematical Sciences, vol. 16 no. 3
(January, 2018),
pp. 777789 [doi] [abs]
 Li, L; Liu, JG, Some compactness criteria for weak solutions of time fractional pdes,
Siam Journal on Mathematical Analysis, vol. 50 no. 4
(January, 2018),
pp. 39633995, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Gao, Y; Li, L; Liu, JG, A dispersive regularization for the modified camassa–holm equation,
Siam Journal on Mathematical Analysis, vol. 50 no. 3
(January, 2018),
pp. 28072838, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Li, L; Liu, JG, A generalized definition of caputo derivatives and its application to fractional odes,
Siam Journal on Mathematical Analysis, vol. 50 no. 3
(January, 2018),
pp. 28672900, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Li, L; Liu, JG, A note on deconvolution with completely monotone sequences and discrete fractional calculus,
Quarterly of Applied Mathematics, vol. 76 no. 1
(January, 2018),
pp. 189198, American Mathematical Society (AMS) [doi] [abs]
 Coquel, F; Jin, S; Liu, JG; Wang, L, Entropic subcell shock capturing schemes via JinXin relaxation and glimm front sampling for scalar conservation laws,
Mathematics of Computation, vol. 87 no. 311
(January, 2018),
pp. 10831126, American Mathematical Society (AMS) [doi] [abs]
 Liu, JG; Wang, L; Zhou, Z, Positivitypreserving and asymptotic preserving method for 2D KellerSegal equations,
Mathematics of Computation, vol. 87 no. 311
(January, 2018),
pp. 11651189, American Mathematical Society (AMS) [doi] [abs]
 Gao, Y; Liu, JG, The modified CamassaHolm equation in Lagrangian coordinates,
Discrete & Continuous Dynamical Systems B, vol. 23 no. 6
(2018),
pp. 25452592, American Institute of Mathematical Sciences (AIMS) [doi]
Lu, Jianfeng
 Wang, Z; Li, Y; Lu, J, Coordinate Descent Full Configuration Interaction.,
Journal of Chemical Theory and Computation, vol. 15 no. 6
(June, 2019),
pp. 35583569 [doi] [abs]
 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. 5467 [doi] [abs]
 Cao, Y; Lu, J; Lu, Y, Gradient flow structure and exponential decay of the sandwiched Rényi divergence for primitive Lindblad equations with GNSdetailed balance,
Journal of Mathematical Physics, vol. 60 no. 5
(May, 2019),
pp. 052202052202, AIP Publishing [doi] [abs]
 Lu, J; VandenEijnden, 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. 715733 [doi] [abs]
 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. 117, Oxford University Press (OUP) [doi] [abs]
 Martinsson, A; Lu, J; Leimkuhler, B; VandenEijnden, 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. 013207013207, IOP Publishing [doi] [abs]
 Lu, J; Sogge, CD; Steinerberger, S, Approximating pointwise products of Laplacian eigenfunctions,
Journal of Functional Analysis
(January, 2019) [doi] [abs]
 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. 648671, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 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. 129 [doi] [abs]
 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. 717749, American Mathematical Society (AMS) [doi] [abs]
 Cao, Y; Lu, J; Lu, Y, Exponential Decay of Rényi Divergence Under Fokker–Planck Equations,
Journal of Statistical Physics
(January, 2019) [doi] [abs]
 Lu, J; Sachs, M; Steinerberger, S, Quadrature Points via Heat Kernel Repulsion,
Constructive Approximation
(January, 2019) [doi] [abs]
 Cao, Y; Lu, J, Stochastic dynamical lowrank approximation method,
Journal of Computational Physics, vol. 372
(November, 2018),
pp. 564586, Elsevier BV [doi] [abs]
 Chen, H; Lu, J; Ortner, C, Thermodynamic Limit of Crystal Defects with Finite Temperature Tight Binding,
Archive for Rational Mechanics and Analysis, vol. 230 no. 2
(November, 2018),
pp. 701733, Springer Nature America, Inc [doi] [abs]
 Li, X; Liu, J; Lu, J; Zhou, X, Moderate deviation for random elliptic PDE with small noise,
The Annals of Applied Probability, vol. 28 no. 5
(October, 2018),
pp. 27812813, Institute of Mathematical Statistics [doi] [abs]
 Barthel, T; Lu, J, Fundamental Limitations for Measurements in Quantum ManyBody Systems,
Physical Review Letters, vol. 121 no. 8
(August, 2018),
pp. 080406 [doi] [abs]
 Huang, Y; Lu, J; Ming, P, A Concurrent Global–Local Numerical Method for Multiscale PDEs,
Journal of Scientific Computing, vol. 76 no. 2
(August, 2018),
pp. 11881215, Springer Nature [doi] [abs]
 You, Z; Li, L; Lu, J; Ge, H, Integrated tempering enhanced sampling method as the infinite switching limit of simulated tempering.,
The Journal of Chemical Physics, vol. 149 no. 8
(August, 2018),
pp. 084114 [doi] [abs]
 Lin, L; Lu, J; VandenEijnden, E, A Mathematical Theory of Optimal Milestoning (with a Detour via Exact Milestoning),
Communications on Pure and Applied Mathematics, vol. 71 no. 6
(June, 2018),
pp. 11491177, WILEY [doi] [abs]
 Yu, V; Huhn, W; Lin, L; Lu, J; VazquezMayagoitia, A; Yang, C; Blum, V, ELSI: A unified software interface for KohnSham electronic structure solvers,
Abstracts of Papers of the American Chemical Society, vol. 255
(March, 2018),
pp. 1 pages, AMER CHEMICAL SOC
 Lu, J; Zhou, Z, Accelerated sampling by infinite swapping of path integral molecular dynamics with surface hopping.,
The Journal of Chemical Physics, vol. 148 no. 6
(February, 2018),
pp. 064110 [doi] [abs]
 Cai, Z; Lu, J, A quantum kinetic monte carlo method for quantum manybody spin dynamics,
Siam Journal on Scientific Computing, vol. 40 no. 3
(January, 2018),
pp. B706B722, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Lu, J; Yang, H, Phasespace sketching for crystal image analysis based on synchrosqueezed transforms,
Siam Journal on Imaging Sciences, vol. 11 no. 3
(January, 2018),
pp. 19541978, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Delgadillo, R; Lu, J; Yang, X, Frozen Gaussian approximation for high frequency wave propagation in periodic media,
Asymptotic Analysis, vol. 110 no. 34
(January, 2018),
pp. 113135, IOS Press [doi] [abs]
 Yu, VWZ; Corsetti, F; García, A; Huhn, WP; Jacquelin, M; Jia, W; Lange, B; Lin, L; Lu, J; Mi, W; Seifitokaldani, A; VázquezMayagoitia, Á; Yang, C; Yang, H; Blum, V, ELSI: A unified software interface for Kohn–Sham electronic structure solvers,
Computer Physics Communications, vol. 222
(January, 2018),
pp. 267285, Elsevier BV [doi] [abs]
 Lu, J; Zhou, Z, Frozen gaussian approximation with surface hopping for mixed quantumclassical dynamics: A mathematical justification of fewest switches surface hopping algorithms,
Mathematics of Computation, vol. 87 no. 313
(January, 2018),
pp. 21892232, American Mathematical Society (AMS) [doi] [abs]
 Du, Q; Li, XH; Lu, J; Tian, X, A quasinonlocal coupling method for nonlocal and local diffusion models,
Siam Journal on Numerical Analysis, vol. 56 no. 3
(January, 2018),
pp. 13861404, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Dai, S; Li, B; Lu, J, Convergence of PhaseField Free Energy and Boundary Force for Molecular Solvation,
Archive for Rational Mechanics and Analysis, vol. 227 no. 1
(January, 2018),
pp. 105147, Springer Nature [doi] [abs]
 Cai, Z; Lu, J, A surface hopping Gaussian beam method for highdimensional transport systems,
Siam Journal on Scientific Computing, vol. 40 no. 5
(January, 2018),
pp. B1277B1301, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Lai, R; Lu, J, Point cloud discretization of Fokkerplanck operators for committor functions,
Multiscale Modeling & Simulation, vol. 16 no. 2
(January, 2018),
pp. 710726, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Lu, J; Spiliopoulos, K, Analysis of multiscale integrators for multiple attractors and irreversible langevin samplers,
Multiscale Modeling & Simulation, vol. 16 no. 4
(January, 2018),
pp. 18591883, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Fang, D; Lu, J, A diabatic surface hopping algorithm based on time dependent perturbation theory and semiclassical analysis,
Multiscale Modeling & Simulation, vol. 16 no. 4
(January, 2018),
pp. 16031622, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
 Zhu, W; Qiu, Q; Wang, B; Lu, J; Sapiro, G; Daubechies, I, Stop memorizing: A datadependent regularization framework for intrinsic pattern learning.,
Corr, vol. abs/1805.07291
(2018)
Maggioni, Mauro
 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. 18291845, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 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]
 Escande, P; Debarnot, V; Maggioni, M; Mangeat, T; Weiss, P, Learning and exploiting physics of degradations,
Optics Infobase Conference Papers, vol. Part F105MATH 2018
(January, 2018), OSA, ISBN 9781557528209 [doi] [abs]
 Murphy, JM; Maggioni, M, Diffusion geometric methods for fusion of remotely sensed data,
Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics, vol. 10644
(January, 2018), SPIE, ISBN 9781510617995 [doi] [abs]
 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, QEEGbased classification with wavelet packets and microstate features for triage applications in the ER
(2005)
 GL Davis and Mauro Maggioni and FJ Warner and FB Geshwind and AC Coppi and RA DeVerse and RR Coifman, Hyperspectral Analysis of normal and malignant colon tissue microarray sections using a novel DMD system
(2004) (Poster, Optical Imaging NIH workshop, to app. in proc..)
 Ronald R Coifman and Mauro Maggioni, Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms no. YALE/DCS/TR1289
(2004)
Malen, Greg
 Malen, G, Homomorphism complexes and kcores,
Discrete Mathematics, vol. 341 no. 9
(September, 2018),
pp. 25672574, Elsevier BV [doi] [abs]
Miller, Ezra
 Katthän, L; Michałek, M; Miller, E, When is a Polynomial Ideal Binomial After an Ambient Automorphism?,
Foundations of Computational Mathematics
(January, 2018), Springer Nature America, Inc [doi] [abs]
Mukherjee, Sayan
 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]
 Gao, T; Brodzki, J; Mukherjee, S, The Geometry of Synchronization Problems and Learning Group Actions,
Discrete & Computational Geometry
(January, 2019) [doi] [abs]
 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]
 Silverman, JD; Durand, HK; Bloom, RJ; Mukherjee, S; David, LA, Correction to: Dynamic linear models guide design and analysis of microbiota studies within artificial human guts.,
Microbiome, vol. 6 no. 1
(November, 2018),
pp. 212 [doi] [abs]
 Silverman, JD; Durand, HK; Bloom, RJ; Mukherjee, S; David, LA, Dynamic linear models guide design and analysis of microbiota studies within artificial human guts.,
Microbiome, vol. 6 no. 1
(November, 2018),
pp. 202 [doi] [abs]
 Barish, S; Nuss, S; Strunilin, I; Bao, S; Mukherjee, S; Jones, CD; Volkan, PC, Combinations of DIPs and Dprs control organization of olfactory receptor neuron terminals in Drosophila.,
Plos Genetics, vol. 14 no. 8
(August, 2018),
pp. e1007560 [doi] [abs]
 Tan, Z; Roche, K; Zhou, X; Mukherjee, S, Scalable algorithms for learning highdimensional linear mixed models,
34th Conference on Uncertainty in Artificial Intelligence 2018, Uai 2018, vol. 1
(January, 2018),
pp. 259268, ISBN 9781510871601 [abs]
Nagy, Akos
 Nagy, Á, Irreducible Ginzburg–Landau Fields in Dimension 2,
The Journal of Geometric Analysis, vol. 28 no. 2
(April, 2018),
pp. 18531868, Springer Nature [doi] [abs]
Ng, Lenhard L.
 Ekholm, T; Ng, L; Shende, V, A complete knot invariant from contact homology,
Inventiones Mathematicae, vol. 211 no. 3
(March, 2018),
pp. 11491200, Springer Nature [doi] [abs]
Nolen, James H.
 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. 648671 [doi] [abs]
 Cristali, I; Ranjan, V; Steinberg, J; Beckman, E; Durrett, R; Junge, M; Nolen, J, Block size in geometric(P)biased permutations,
Electronic Communications in Probability, vol. 23
(January, 2018),
pp. 110, Institute of Mathematical Statistics [doi] [abs]
 Nolen, J; Roquejoffre, JM; Ryzhik, L, Refined longtime asymptotics for Fisher–KPP fronts,
Communications in Contemporary Mathematics
(January, 2018),
pp. 18500721850072, World Scientific Pub Co Pte Lt [doi] [abs]
Orizaga, Saulo
 Glasner, K; Orizaga, S, Multidimensional equilibria and their stability in copolymer–solvent mixtures,
Physica D: Nonlinear Phenomena, vol. 373
(June, 2018),
pp. 112, Elsevier BV [doi] [abs]
Petters, Arlie O.
 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
 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]
 Reeves, G; Pfister, HD, The ReplicaSymmetric Prediction for Random Linear Estimation With Gaussian Matrices Is Exact,
Ieee Transactions on Information Theory, vol. 65 no. 4
(April, 2019),
pp. 22522283 [doi] [abs]
 Schmidt, C; Pfister, HD; Zdeborová, L, Minimal sets to destroy the kcore in random networks.,
Physical Review. E, vol. 99 no. 21
(February, 2019),
pp. 022310 [doi] [abs]
 Yoo, I; Imani, MF; Sleasman, T; Pfister, HD; Smith, DR, Enhancing Capacity of Spatial Multiplexing Systems Using Reconfigurable CavityBacked Metasurface Antennas in Clustered MIMO Channels,
Ieee Transactions on Communications, vol. 67 no. 2
(February, 2019),
pp. 10701084, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Sheikh, A; GraellAmat, A; Liva, G; Häger, C; Pfister, HD, On LowComplexity Decoding of Product Codes for HighThroughput FiberOptic Systems,
International Symposium on Turbo Codes and Iterative Information Processing, Istc, vol. 2018December
(January, 2019) [doi] [abs]
 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]
 Fougstedt, C; Häger, C; Svensson, L; Pfister, HD; LarssonEdefors, P, ASIC Implementation of TimeDomain Digital Backpropagation with DeepLearned Chromatic Dispersion Filters,
European Conference on Optical Communication, Ecoc, vol. 2018September
(November, 2018) [doi] [abs]
 Häger, C; Pfister, HD, Wideband TimeDomain Digital Backpropagation via Subband Processing and Deep Learning,
European Conference on Optical Communication, Ecoc, vol. 2018September
(November, 2018) [doi] [abs]
 Rengaswamy, N; Calderbank, R; Pfister, HD; Kadhe, S, Synthesis of Logical Clifford Operators via Symplectic Geometry,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 791795, IEEE [doi] [abs]
 Hager, C; Pfister, HD, Deep Learning of the Nonlinear Schrödinger Equation in FiberOptic Communications,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 15901594, IEEE [doi] [abs]
 Santi, E; Hager, C; Pfister, HD, Decoding ReedMuller Codes Using Minimum Weight Parity Checks,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 12961300, IEEE [doi] [abs]
 Reeves, G; Pfister, HD; Dytso, A, Mutual Information as a Function of Matrix SNR for Linear Gaussian Channels,
Ieee International Symposium on Information Theory Proceedings, vol. 2018June
(August, 2018),
pp. 17541758, IEEE, ISBN 9781538647806 [doi] [abs]
 Hager, C; Pfister, HD, Approaching MiscorrectionFree Performance of Product Codes with Anchor Decoding,
Ieee Transactions on Communications, vol. 66 no. 7
(July, 2018),
pp. 27972808, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Hager, C; Pfister, HD, Nonlinear interference mitigation via deep neural networks,
2018 Optical Fiber Communications Conference and Exposition, Ofc 2018 Proceedings
(June, 2018),
pp. 13, ISBN 9781943580385 [abs]
 Häger, C; Pfister, HD, Nonlinear interference mitigation via deep neural networks,
Optics Infobase Conference Papers, vol. Part F84OFC 2018
(January, 2018), OSA [doi] [abs]
Pierce, Lillian B.
 Pierce, LB; Yung, PL, A polynomial Carleson operator along the paraboloid,
Revista Matemática Iberoamericana
(2018), European Mathematical Society
Plesser, M. Ronen
 Bertolini, M; Plesser, MR, (0,2) hybrid models,
Journal of High Energy Physics, vol. 2018 no. 9
(September, 2018), Springer Nature America, Inc [doi] [abs]
Pollack, Aaron
 Pollack, A; Shah, S, The spin Lfunction on GSp(6) via a nonunique model,
American Journal of Mathematics, vol. 140 no. 3
(2018),
pp. 753788, Johns Hopkins University Press
 Pollack, A, Unramified GodementJacquet theory for the spin similitude group,
Journal of the Ramanujan Mathematical Society, vol. 33 no. 3
(2018),
pp. 249282, The Ramanujan Mathematical Society
 Pollack, A; Shah, S, Multivariate RankinSelberg integrals on GL(4) and GU(2,2),
Canadian Mathematical Bulletin, vol. 61 no. 4
(2018),
pp. 822835, Canadian Mathematical Society
 Pollack, A, Lifting laws and arithmetic invariant theory,
Cambridge Journal of Mathematics, vol. 6 no. 4
(2018),
pp. 347449
Randles, Amanda
 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. 113 [doi] [abs]
 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]
 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]
 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 imagederived vasculature.,
International Journal for Numerical Methods in Biomedical Engineering, vol. 35 no. 6
(June, 2019),
pp. e3198 [doi] [abs]
 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; LouisRosenberg, 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. 458464 [doi] [abs]
 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. 5664, ISBN 9781728101149 [doi] [abs]
 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. 18 [doi] [abs]
 Dabagh, M; Randles, A, Role of deformable cancer cells on wall shear stressassociatedVEGF secretion by endothelium in microvasculature.,
Plos One, vol. 14 no. 2
(January, 2019),
pp. e0211418 [doi] [abs]
 Gounley, J; Draeger, EW; Oppelstrup, T; Krauss, WD; Gunnels, JA; Chaudhury, R; Nair, P; Frakes, D; Leopold, JA; Randles, A, Computing the anklebrachial index with parallel computational fluid dynamics.,
Journal of Biomechanics, vol. 82
(January, 2019),
pp. 2837 [doi] [abs]
 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. 441455, ISBN 9783030227333 [doi] [abs]
 Hegele, LA; Scagliarini, A; Sbragaglia, M; Mattila, KK; Philippi, PC; Puleri, DF; Gounley, J; Randles, A, HighReynoldsnumber turbulent cavity flow using the lattice Boltzmann method,
Physical Review. E, vol. 98 no. 4
(October, 2018), American Physical Society (APS) [doi] [abs]
 Herschlag, G; Lee, S; Vetter, JS; Randles, A, GPU data access on complex geometries for D3Q19 lattice boltzmann method,
Proceedings 2018 Ieee 32nd International Parallel and Distributed Processing Symposium, Ipdps 2018
(August, 2018),
pp. 825834, IEEE, ISBN 9781538643686 [doi] [abs]
 Rafat, M; Stone, HA; Auguste, DT; Dabagh, M; Randles, A; Heller, M; Rabinov, JD, Impact of diversity of morphological characteristics and Reynolds number on local hemodynamics in basilar aneurysms,
Aiche Journal, vol. 64 no. 7
(July, 2018),
pp. 27922802, WILEY [doi] [abs]
Reed, Michael C.
 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]
 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. 5058 [doi] [abs]
 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]
 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]
 SadreMarandi, F; Dahdoul, T; Reed, MC; Nijhout, HF, Sex differences in hepatic onecarbon metabolism.,
Bmc Systems Biology, vol. 12 no. 1
(October, 2018),
pp. 89 [doi] [abs]
 Duncan, W; Best, J; Golubitsky, M; Nijhout, HF; Reed, M, Homeostasis despite instability.,
Mathematical Biosciences, vol. 300
(June, 2018),
pp. 130137 [doi] [abs]
Robles, Colleen M
 Robles, C, Characterization of Calabi–Yau variations of Hodge structure over tube domains by characteristic forms,
Mathematische Annalen, vol. 371 no. 34
(August, 2018),
pp. 12291253, Springer Nature [doi] [abs]
Rudin, Cynthia D.
 Rudin, C; Shaposhnik, Y, GloballyConsistent RuleBased SummaryExplanations for Machine Learning Models: Application to CreditRisk Evaluation
(May, 2019)
 Bravo, F; Rudin, C; Shaposhnik, Y; Yuan, Y, Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs
(May, 2019)
 Ban, GY; Rudin, C, The big Data newsvendor: Practical insights from machine learning,
Operations Research, vol. 67 no. 1
(January, 2019),
pp. 90108 [doi] [abs]
 Bei, Y; Damian, A; Hu, S; Menon, S; Ravi, N; Rudin, C, New techniques for preserving global structure and denoising with low information loss in singleimage superresolution,
Ieee Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018June
(December, 2018),
pp. 987994, ISBN 9781538661000 [doi] [abs]
 Rudin, C; Ertekin, Ş, Learning customized and optimized lists of rules with mathematical programming,
Mathematical Programming Computation, vol. 10 no. 4
(December, 2018),
pp. 659702, Springer Nature America, Inc [doi] [abs]
 Rudin, C; Ustunb, B, Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice,
Interfaces, vol. 48 no. 5
(September, 2018),
pp. 449466, Institute for Operations Research and the Management Sciences (INFORMS) [doi] [abs]
 Vu, MAT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience.,
Journal of Neuroscience, vol. 38 no. 7
(February, 2018),
pp. 16011607 [doi] [abs]
 Angelino, E; LarusStone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists for categorical data,
Journal of Machine Learning Research, vol. 18
(January, 2018),
pp. 178 [abs]
 Li, O; Liu, H; Chen, C; Rudin, C, Deep learning for casebased reasoning through prototypes: A neural network that explains its predictions,
32nd Aaai Conference on Artificial Intelligence, Aaai 2018
(January, 2018),
pp. 35303537, ISBN 9781577358008 [abs]
 Chen, C; Rudin, C, An optimization approach to learning falling rule lists,
International Conference on Artificial Intelligence and Statistics, Aistats 2018
(January, 2018),
pp. 604612 [abs]
 Rudin, C; Wang, Y, Direct learning to rank and rerank,
International Conference on Artificial Intelligence and Statistics, Aistats 2018
(January, 2018),
pp. 775783 [abs]
Ryser, Marc D.
 Ryser, MD; Hendrix, LH; Worni, M; Liu, Y; Hyslop, T; Hwang, ES, Incidence of Ductal Carcinoma In Situ in the United States, 20002014.,
Cancer Epidemiol Biomarkers Prev, vol. 28 no. 8
(August, 2019),
pp. 13161323 [doi] [abs]
 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. 7783 [doi] [abs]
 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. 12611271 [doi] [abs]
 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
(February, 2019) [doi] [abs]
 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. 197205 [doi] [abs]
 Ryser, MD; Yu, M; Grady, W; Siegmund, K; Shibata, D, Epigenetic Heterogeneity in Human Colorectal Tumors Reveals Preferential Conservation And Evidence of Immune Surveillance.,
Scientific Reports, vol. 8 no. 1
(November, 2018),
pp. 17292 [doi] [abs]
 Ryser, MD; Min, BH; Siegmund, KD; Shibata, D, Spatial mutation patterns as markers of early colorectal tumor cell mobility.,
Proc Natl Acad Sci U S A, vol. 115 no. 22
(May, 2018),
pp. 57745779 [doi] [abs]
 Grimm, LJ; Ryser, MD; Hyslop, T, Role of Preoperative Variables in Reducing the Rate of Occult Invasive Disease for Women Considering Active Surveillance for Ductal Carcinoma In Situ.,
Jama Surg, vol. 153 no. 3
(March, 2018),
pp. 290291 [doi]
 Ryser, MD; Horton, JK; Hwang, ES, How Low Can We Goand Should We? Risk Reduction for MinimalVolume DCIS.,
Annals of Surgical Oncology, vol. 25 no. 2
(February, 2018),
pp. 354355 [doi]
Saper, Leslie
 Saper, L, ℒmodules and microsupport,
To Appear in Annals of Mathematics
(2018)
Sapiro, Guillermo
 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 PreCancers With a LowCost, PointofCare, Pocket Colposcope.,
Ieee Trans Biomed Eng, vol. 66 no. 8
(August, 2019),
pp. 23062318, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 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. 305306 [doi]
 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. 619628 [doi] [abs]
 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 SubthalamicNucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery.,
Neurosurgery, vol. 84 no. 3
(March, 2019),
pp. 749757 [doi] [abs]
 Kim, J; Duchin, Y; Shamir, RR; Patriat, R; Vitek, J; Harel, N; Sapiro, G, Automatic localization of the subthalamic nucleus on patientspecific 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. 679698 [doi] [abs]
 Azami, H; Arnold, SE; Sanei, S; Chang, Z; Sapiro, G; Escudero, J; Gupta, AS, Multiscale fluctuationbased dispersion entropy and its applications to neurological diseases,
Ieee Access, vol. 7
(January, 2019),
pp. 6871868733 [doi] [abs]
 Lezama, J; Qiu, Q; Musé, P; Sapiro, G, OLE: Orthogonal Lowrank Embedding, A Plug and Play Geometric Loss for Deep Learning,
Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition
(December, 2018),
pp. 81098118 [doi] [abs]
 Zhu, W; Qiu, Q; Huang, J; Calderbank, R; Sapiro, G; Daubechies, I, LDMNet: Low Dimensional Manifold Regularized Neural Networks,
Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition
(December, 2018),
pp. 27432751 [doi] [abs]
 Dawson, G; Campbell, K; Hashemi, J; Lippmann, SJ; Smith, V; Carpenter, K; Egger, H; Espinosa, S; Vermeer, S; Baker, J; Sapiro, G, Atypical postural control can be detected via computer vision analysis in toddlers with autism spectrum disorder.,
Scientific Reports, vol. 8 no. 1
(November, 2018),
pp. 17008 [doi] [abs]
 Aguerrebere, C; Delbracio, M; Bartesaghi, A; Sapiro, G, A Practical Guide to MultiImage Alignment,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018April
(September, 2018),
pp. 19271931, IEEE [doi] [abs]
 Ahn, HK; Qiu, Q; Bosch, E; Thompson, A; Robles, FE; Sapiro, G; Warren, WS; Calderbank, R, Classifying PumpProbe Images of Melanocytic Lesions Using the WEYL Transform,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018April
(September, 2018),
pp. 42094213, IEEE, ISBN 9781538646588 [doi] [abs]
 Giryes, R; Eldar, YC; Bronstein, AM; Sapiro, G, The Learned Inexact Project Gradient Descent Algorithm,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018April
(September, 2018),
pp. 67676771, IEEE, ISBN 9781538646588 [doi] [abs]
 Hashemi, J; Dawson, G; Carpenter, KLH; Campbell, K; Qiu, Q; Espinosa, S; Marsan, S; Baker, JP; Egger, HL; Sapiro, G, Computer Vision Analysis for Quantification of Autism Risk Behaviors,
Ieee Transactions on Affective Computing
(August, 2018),
pp. 11, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Bartesaghi, A; Aguerrebere, C; Falconieri, V; Banerjee, S; Earl, LA; Zhu, X; Grigorieff, N; Milne, JLS; Sapiro, G; Wu, X; Subramaniam, S, Atomic Resolution CryoEM Structure of βGalactosidase.,
Structure (London, England : 1993), vol. 26 no. 6
(June, 2018),
pp. 848856.e3 [doi] [abs]
 Giryes, R; Eldar, YC; Bronstein, AM; Sapiro, G, Tradeoffs between convergence speed and reconstruction accuracy in inverse problems,
Ieee Transactions on Signal Processing, vol. 66 no. 7
(April, 2018),
pp. 16761690, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Vu, MAT; Adalı, T; Ba, D; Buzsáki, G; Carlson, D; Heller, K; Liston, C; Rudin, C; Sohal, VS; Widge, AS; Mayberg, HS; Sapiro, G; Dzirasa, K, A Shared Vision for Machine Learning in Neuroscience.,
Journal of Neuroscience, vol. 38 no. 7
(February, 2018),
pp. 16011607 [doi] [abs]
 Pisharady, PK; Sotiropoulos, SN; DuarteCarvajalino, JM; Sapiro, G; Lenglet, C, Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.,
Neuroimage, vol. 167
(February, 2018),
pp. 488503 [doi] [abs]
 Qiu, Q; Hashemi, J; Sapiro, G, Intelligent synthesis driven model calibration: framework and face recognition application,
Proceedings 2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017, vol. 2018January
(January, 2018),
pp. 25642572, IEEE, ISBN 9781538610343 [doi] [abs]
 Simhal, AK; Gong, B; Trimmer, JS; Weinberg, RJ; Smith, SJ; Sapiro, G; Micheva, KD, A Computational Synaptic Antibody Characterization Tool for Array Tomography.,
Frontiers in Neuroanatomy, vol. 12
(January, 2018),
pp. 51 [doi] [abs]
 Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from twophoton imaging data.,
Plos One, vol. 13 no. 5
(January, 2018),
pp. e0196527 [doi] [abs]
 Asiedu, MN; Simhal, A; Lam, CT; Mueller, J; Chaudhary, U; Schmitt, JW; Sapiro, G; Ramanujam, N, Image processing and machine learning techniques to automate diagnosis of Lugol's iodine cervigrams for a lowcost pointofcare digital colposcope,
Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10485
(January, 2018), SPIE, ISBN 9781510614550 [doi] [abs]
 Duchin, Y; Shamir, RR; Patriat, R; Kim, J; Vitek, JL; Sapiro, G; Harel, N, Patientspecific anatomical model for deep brain stimulation based on 7 Tesla MRI.,
Plos One, vol. 13 no. 8
(January, 2018),
pp. e0201469 [doi] [abs]
 Qiu, Q; Lezama, J; Bronstein, A; Sapiro, G, ForestHash: Semantic Hashing with Shallow Random Forests and Tiny Convolutional Networks,
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11206 LNCS
(January, 2018),
pp. 442459, Springer International Publishing [doi] [abs]
 Qiu, Q; Cheng, X; Calderbank, R; Sapiro, G, DCFNet: Deep Neural Network with Decomposed Convolutional Filters,
35th International Conference on Machine Learning, Icml 2018, vol. 9
(January, 2018),
pp. 66876696 [abs]
 Bovery, MDMJ; Dawson, G; Hashemi, J; Sapiro, G, A Scalable OfftheShelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder,
Ieee Transactions on Affective Computing
(January, 2018),
pp. 11, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Chiew, KS; Hashemi, J; Gans, LK; Lerebours, L; Clement, NJ; Vu, MAT; Sapiro, G; Heller, NE; Adcock, RA, Motivational valence alters memory formation without altering exploration of a reallife spatial environment.,
Plos One, vol. 13 no. 3
(2018),
pp. e0193506 [doi] [abs]
Sober, Barak
 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 HighFluorescence Setting—A Proof of Concept,
Archaeometry, vol. 61 no. 2
(April, 2019),
pp. 459469, WILEY [doi] [abs]
Stern, Mark A.
 Lipnowski, M; Stern, M, Geometry of the Smallest 1form Laplacian Eigenvalue on Hyperbolic Manifolds,
Geometrical and Functional Analysis Gafa, vol. 28 no. 6
(December, 2018),
pp. 17171755, Springer Nature [doi] [abs]
Tarokh, Vahid
 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. 2019March
(May, 2019),
pp. 139142 [doi] [abs]
 Ding, J; Zhou, J; Tarokh, V, Asymptotically Optimal Prediction for TimeVarying Data Generating Processes,
Ieee Transactions on Information Theory, vol. 65 no. 5
(May, 2019),
pp. 30343067 [doi] [abs]
 Angjelichinoski, M; Banerjee, T; Choi, J; Pesaran, B; Tarokh, V, Minimaxoptimal decoding of movement goals from local field potentials using complex spectral features.,
Journal of Neural Engineering, vol. 16 no. 4
(April, 2019),
pp. 046001 [doi] [abs]
 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. 13531365, IEEEINST ELECTRICAL ELECTRONICS ENGINEERS INC [doi] [abs]
 Banerjee, T; Whipps, G; Gurram, P; Tarokh, V, Cyclostationary statistical models and algorithms for anomaly detection using multimodal data,
2018 Ieee Global Conference on Signal and Information Processing, Globalsip 2018 Proceedings, vol. abs/1807.06945
(February, 2019),
pp. 126130 [doi] [abs]
 Shahrampour, S; Beirami, A; Tarokh, V, Supervised Learning Using Datadependent Random Features with Application to Seizure Detection,
Proceedings of the Ieee Conference on Decision and Control, vol. 2018December
(January, 2019),
pp. 11681173, ISBN 9781538613955 [doi] [abs]
 Shao, S; Jacob, PE; Ding, J; Tarokh, V, Bayesian Model Comparison with the Hyvärinen Score: Computation and Consistency,
Journal of the American Statistical Association
(January, 2019) [doi] [abs]
 Ding, J; Tarokh, V; Yang, Y, Model Selection Techniques: An Overview,
Ieee Signal Processing Magazine, vol. 35 no. 6
(November, 2018),
pp. 1634, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Ding, J; Diao, E; Zhou, J; Tarokh, V, A Penalized Method for the Predictive Limit of Learning,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018April
(September, 2018),
pp. 44144418, IEEE, ISBN 9781538646588 [doi] [abs]
 Banerjee, T; Choi, J; Pesaran, B; Ba, D; Tarokh, V, Wavelet Shrinkage and Thresholding Based Robust Classification for BrainComputer Interface,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018April
(September, 2018),
pp. 836840, IEEE [doi] [abs]
 Xiang, Y; Ding, J; Tarokh, V, Evolutionary Spectra Based on the Multitaper Method with Application to Stationarity Test,
2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018April
(September, 2018),
pp. 39943998, IEEE [doi] [abs]
 Banerjee, T; Whipps, G; Gurram, P; Tarokh, V, Sequential Event Detection Using Multimodal Data in Nonstationary Environments,
2018 21st International Conference on Information Fusion, Fusion 2018
(September, 2018),
pp. 19401947, IEEE [doi] [abs]
 Shahrampour, S; Noshad, M; Ding, J; Tarokh, V, Online Learning for Multimodal Data Fusion with Application to Object Recognition,
Ieee Transactions on Circuits and Systems Ii: Express Briefs, vol. 65 no. 9
(September, 2018),
pp. 12591263, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Banerjee, T; Choi, J; Pesaran, B; Ba, D; Tarokh, V, Classification of Local Field Potentials using Gaussian Sequence Model,
2018 Ieee Statistical Signal Processing Workshop, Ssp 2018
(August, 2018),
pp. 218222, IEEE [doi] [abs]
 Magnusson, S; Enyioha, C; Li, N; Fischione, C; Tarokh, V, Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization,
Ieee Journal of Selected Topics in Signal Processing, vol. 12 no. 4
(August, 2018),
pp. 717732, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Ding, J; Shahrampour, S; Heal, K; Tarokh, V, Analysis of Multistate Autoregressive Models,
Ieee Transactions on Signal Processing, vol. 66 no. 9
(May, 2018),
pp. 24292440, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Magnusson, S; Enyioha, C; Li, N; Fischione, C; Tarokh, V, Convergence of Limited Communication Gradient Methods,
Ieee Transactions on Automatic Control, vol. 63 no. 5
(May, 2018),
pp. 13561371, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Soloveychik, I; Xiang, Y; Tarokh, V, PseudoWigner Matrices,
Ieee Transactions on Information Theory, vol. 64 no. 4
(April, 2018),
pp. 31703178, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Soloveychik, I; Xiang, Y; Tarokh, V, Symmetric PseudoRandom Matrices,
Ieee Transactions on Information Theory, vol. 64 no. 4
(April, 2018),
pp. 31793196, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Ding, J; Zhou, J; Tarokh, V, Optimal prediction of data with unknown abrupt change points,
2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018January
(March, 2018),
pp. 928932, IEEE, ISBN 9781509059904 [doi] [abs]
 DIng, J; Xiang, Y; Shen, L; Tarokh, V, Detecting structural changes in dependent data,
2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018January
(March, 2018),
pp. 750754, IEEE, ISBN 9781509059904 [doi] [abs]
 Han, Q; Ding, J; Airoldi, E; Tarokh, V, Modeling nonlinearity in multidimensional dependent data,
2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018January
(March, 2018),
pp. 206210, IEEE, ISBN 9781509059904 [doi] [abs]
 Soloveychik, I; Xiang, Y; Tarokh, V, Explicit symmetric pseudorandom matrices,
Ieee International Symposium on Information Theory Proceedings, vol. 2018January
(January, 2018),
pp. 424428, IEEE, ISBN 9781509030972 [doi] [abs]
 Shahrampour, S; Tarokh, V, Nonlinear sequential accepts and rejects for identification of top arms in stochastic bandits,
55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol. 2018January
(January, 2018),
pp. 228235, IEEE [doi] [abs]
 Soloveychik, I; Tarokh, V; Paulson, JA, On the spectral norms of pseudowigner and related matrices,
55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol. 2018January
(January, 2018),
pp. 6166, IEEE [doi] [abs]
 Shahrampour, S; Tarokh, V, Learning bounds for greedy approximation with explicit feature maps from multiple kernels, edited by Bengio, S; Wallach, H; Larochelle, H; Grauman, K; CesaBianchi, N; Garnett, R,
Advances in Neural Information Processing Systems, vol. 2018December
(January, 2018),
pp. 46904701, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) [abs]
 Enyioha, C; Magnússon, S; Heal, K; Li, N; Fischione, C; Tarokh, V, On variability of renewable energy and online power allocation,
Ieee Transactions on Power Systems, vol. 33 no. 1
(January, 2018),
pp. 451462, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Shahrampour, S; Beirami, A; Tarokh, V, On datadependent random features for improved generalization in supervised learning,
32nd Aaai Conference on Artificial Intelligence, Aaai 2018, vol. abs/1712.07102
(January, 2018),
pp. 40264033 [abs]
 Magnússon, S; Enyioha, C; Li, N; Fischione, C; Tarokh, V, Convergence of Limited Communication Gradient Methods.,
Ieee Trans. Automat. Contr., vol. 63
(2018),
pp. 13561371
 Banerjee, T; Choi, JS; Pesaran, B; Ba, D; Tarokh, V, Classification of Local Field Potentials using Gaussian Sequence Model.,
Ssp
(2018),
pp. 683687, IEEE, ISBN 9781538615713
 Banerjee, T; Whipps, GT; Gurram, P; Tarokh, V, Sequential Event Detection Using Multimodal Data in Nonstationary Environments.,
Fusion
(2018),
pp. 19401947, IEEE, ISBN 9780996452762
 Magnússon, S; Enyioha, C; Li, N; Fischione, C; Tarokh, V, Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization.,
J. Sel. Topics Signal Processing, vol. 12
(2018),
pp. 717732
 Shahrampour, S; Beirami, A; Tarokh, V, On DataDependent Random Features for Improved Generalization in Supervised Learning., edited by McIlraith, SA; Weinberger, KQ,
Aaai
(2018),
pp. 40264033, AAAI Press
 Soloveychik, I; Tarokh, V, Stationary Geometric Graphical Model Selection.,
Corr, vol. abs/1806.03571
(2018)
 Ding, J; Tarokh, V; Yang, JY, Bridging AIC and BIC: A New Criterion for Autoregression.,
Ieee Trans. Information Theory, vol. 64
(2018),
pp. 40244043
 Shahrampour, S; Noshad, M; Ding, J; Tarokh, V, Online Learning for Multimodal Data Fusion With Application to Object Recognition.,
Ieee Trans. on Circuits and Systems, vol. 65II
(2018),
pp. 12591263
Tralie, Christopher
 Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric crossmodal comparison of heterogeneous sensor data,
2018 Ieee Aerospace Conference
(March, 2018), IEEE [doi] [abs]
 Tralie, CJ; Perea, JA, (Quasi)Periodicity Quantification in Video Data, Using Topology,
Siam Journal on Imaging Sciences, vol. 11 no. 2
(January, 2018),
pp. 10491077, Society for Industrial & Applied Mathematics (SIAM) [doi] [abs]
TurnageButterbaugh, Caroline
 Conrey, JB; TurnageButterbaugh, CL, On rgaps between zeros of the Riemann zetafunction,
Bulletin of the London Mathematical Society
(January, 2018) [doi] [abs]
Vafaee, Faramarz
 Donald, A; McCoy, D; Vafaee, F, On Lspace knots obtained from unknotting arcs in alternating diagrams,
New York Journal of Mathematics, vol. 25
(January, 2019),
pp. 518540, ELECTRONIC JOURNALS PROJECT
 Greene, JE; Lewallen, S; Vafaee, F, (1, 1) Lspace knots,
Compositio Mathematica, vol. 154 no. 5
(May, 2018),
pp. 918933, Oxford University Press (OUP) [doi] [abs]
Venakides, Stephanos
 Komineas, S; Melcher, C; Venakides, S, Traveling domain walls in chiral ferromagnets,
Nonlinearity, vol. 32 no. 7
(July, 2019),
pp. 23922412, IOP Publishing [doi]
 PérezArancibia, C, Domain Decomposition for QuasiPeriodic Scattering by Layered Media via Robust BoundaryIntegral Equations at All Frequencies,
Communications in Computational Physics, vol. 26 no. 1
(June, 2019),
pp. 265310, Global Science Press [doi]
 Aristotelous, AC; Crawford, JM; Edwards, GS; Kiehart, DP; Venakides, S, Mathematical models of dorsal closure.,
Progress in Biophysics and Molecular Biology, vol. 137
(September, 2018),
pp. 111131 [doi] [abs]
 PerezArancibia, C; Shipman, S; Turc, C; Venakides, S, DDM solutions of quasiperiodic transmission problems in layered
media via robust boundary integral equations at all frequencies,
Communications in Computational Physics
(May, 2018), Global Science Press
Viel, Shira
 Barnard, E; Meehan, E; Reading, N; Viel, S, Universal Geometric Coefficients for the FourPunctured Sphere,
Annals of Combinatorics, vol. 22 no. 1
(March, 2018),
pp. 144, Springer Nature [doi] [abs]
Wang, Jiuya
 Boston, N; Wang, J, The 2class tower of ℚ(√5460),
in Springer Proceedings in Mathematics and Statistics, vol. 251
(January, 2018),
pp. 7180, Springer International Publishing, ISBN 9783319973784 [doi] [abs]
Watson, Alexander
 Watson, A; Weinstein, MI, Wavepackets in Inhomogeneous Periodic Media: Propagation Through a OneDimensional Band Crossing,
Communications in Mathematical Physics, vol. 363 no. 2
(October, 2018),
pp. 655698, Springer Nature America, Inc [doi] [abs]
Wickelgren, Kirsten G.
 Kass, JL; Wickelgren, K, The class of EisenbudKhimshiashviliLevine is the local A ^{1} Brouwer degree,
Duke Mathematical Journal, vol. 168 no. 3
(February, 2019),
pp. 429469 [doi] [abs]
 Bergner, JE; Joachimi, R; Lesh, K; Stojanoska, V; Wickelgren, K, Classification of problematic subgroups of U(n),
Transactions of the American Mathematical Society, vol. 371 no. 10
(January, 2019),
pp. 67396777 [doi] [abs]
 Wickelgren, K; Williams, B, The simplicial EHP sequence in A^{1}–algebraic topology,
Geometry & Topology, vol. 23 no. 4
(January, 2019),
pp. 16911777 [doi] [abs]
 Davis, R; Pries, R; Stojanoska, V; Wickelgren, K, The Galois action and cohomology of a relative homology group of Fermat curves,
Journal of Algebra, vol. 505
(July, 2018),
pp. 3369 [doi] [abs]
 Kass, JL; Wickelgren, K, An Étale realization which does NOT exist,
in Contemporary Mathematics, vol. 707
(January, 2018),
pp. 1129 [doi] [abs]
Witelski, Thomas P.
(search)
 Bowen, M; Witelski, TP, Pressuredipole solutions of the thinfilm equation,
European Journal of Applied Mathematics, vol. 30 no. 2
(April, 2019),
pp. 358399 [doi] [abs]
 Gao, Y; Ji, H; Liu, JG; Witelski, TP, A vicinal surface model for epitaxial growth with logarithmic free energy,
Discrete and Continuous Dynamical Systems Series B, vol. 23 no. 10
(December, 2018),
pp. 44334453, American Institute of Mathematical Sciences (AIMS) [doi] [abs]
 Chiou, JG; Ramirez, SA; Elston, TC; Witelski, TP; Schaeffer, DG; Lew, DJ, Principles that govern competition or coexistence in RhoGTPase driven polarization.,
Plos Computational Biology, vol. 14 no. 4
(April, 2018),
pp. e1006095 [doi] [abs]
 Ji, H; Witelski, TP, Instability and dynamics of volatile thin films,
Physical Review Fluids, vol. 3 no. 2
(February, 2018), American Physical Society (APS) [doi] [abs]
Wong, Jeffrey T
 Taranets, RM; Wong, JT, Existence of weak solutions for particleladen flow with surface tension,
Discrete and Continuous Dynamical Systems Series A, vol. 38 no. 10
(October, 2018),
pp. 49794996, American Institute of Mathematical Sciences (AIMS) [doi] [abs]
 Mavromoustaki, A; Wang, L; Wong, J; Bertozzi, AL, Surface tension effects for particle settling and resuspension in viscous thin films,
Nonlinearity, vol. 31 no. 7
(May, 2018),
pp. 31513173, IOP Publishing [doi] [abs]
Wu, HauTieng
 Alagapan, S; Shin, HW; Fröhlich, F; Wu, HT, 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]
 Lu, Y; Wu, HT; Malik, J, Recycling cardiogenic artifacts in impedance pneumography,
Biomedical Signal Processing and Control, vol. 51
(May, 2019),
pp. 162170 [doi] [abs]
 Chen, HY; Pan, HC; Chen, YC; Chen, YC; Lin, YH; Yang, SH; Chen, JL; Wu, HT, Traditional Chinese medicine use is associated with lower endstage renal disease and mortality rates among patients with diabetic nephropathy: a populationbased cohort study.,
Bmc Complementary and Alternative Medicine, vol. 19 no. 1
(April, 2019),
pp. 81 [doi] [abs]
 Zhang, JT; Cheng, MY; Wu, HT; Zhou, B, A new test for functional oneway ANOVA with applications to ischemic heart screening,
Computational Statistics & Data Analysis, vol. 132
(April, 2019),
pp. 317, Elsevier BV [doi] [abs]
 Tan, C; Zhang, L; Wu, HT, A Novel Blaschke Unwinding AdaptiveFourierDecompositionBased Signal Compression Algorithm With Application on ECG Signals.,
Ieee Journal of Biomedical and Health Informatics, vol. 23 no. 2
(March, 2019),
pp. 672682, Institute of Electrical and Electronics Engineers (IEEE) [doi] [abs]
 Lo, YL; Wu, HT; Lin, YT; Kuo, HP; Lin, TY, Hypoventilation patterns during bronchoscopic sedation and their clinical relevance based on capnographic and respiratory impedance analysis,
Journal of Clinical Monitoring and Computing
(January, 2019) [doi] [abs]
 Katz, O; Talmon, R; Lo, YL; Wu, HT, Alternating diffusion maps for multimodal data fusion,
Information Fusion, vol. 45
(January, 2019),
pp. 346360, Elsevier BV [doi] [abs]
 Lin, CY; Wu, HT, Embeddings of Riemannian manifolds with finite eigenvector fields of connection Laplacian,
Calculus of Variations and Partial Differential Equations, vol. 57 no. 5
(October, 2018), Springer Nature America, Inc [doi] [abs]
 EscalonaVargas, D; Wu, HT; Frasch, MG; Eswaran, H, A Comparison of Five Algorithms for Fetal Magnetocardiography Signal Extraction.,
Cardiovascular Engineering and Technology, vol. 9 no. 3
(September, 2018),
pp. 483487, Springer Nature [doi] [abs]
 Malik, J; Lo, YL; Wu, HT, Sleepwake classification via quantifying heart rate variability by convolutional neural network.,
Physiological Measurement, vol. 39 no. 8
(August, 2018),
pp. 085004, IOP Publishing [doi] [abs]
 Wu, HT; Wu, JC; Huang, PC; Lin, TY; Wang, TY; Huang, YH; Lo, YL, Phenotypebased and selflearning interindividual sleep apnea screening with a level IVlike monitoring system,
Frontiers in Physiology, vol. 9 no. JUL
(July, 2018), FRONTIERS MEDIA SA [doi] [abs]
 Wu, HT; Liu, YW, Analyzing transientevoked otoacoustic emissions by concentration of frequency and time,
The Journal of the Acoustical Society of America, vol. 144 no. 1
(July, 2018),
pp. 448466, Acoustical Society of America (ASA) [doi] [abs]
 Liu, TC; Wu, HT; Chen, YH; Fang, TY; Wang, PC; Liu, YW, Analysis of clickevoked otoacoustic emissions by concentration of frequency and time: Preliminary results from normal hearing and Ménière's disease ears,
Aip Conference Proceedings, vol. 1965
(May, 2018), Author(s), ISBN 9780735416703 [doi] [abs]
 Wu, HT; Soliman, EZ, A new approach for analysis of heart rate variability and QT variability in longterm ECG recording.,
Biomedical Engineering Online, vol. 17 no. 1
(May, 2018),
pp. 54 [doi] [abs]
 Wu, JC; Wang, CW; Huang, YH; Wu, HT; Huang, PC; Lo, YL, A Portable Monitoring System with Automatic Event Detection for Sleep Apnea LevelIV Evaluation,
Proceedings Ieee International Symposium on Circuits and Systems, vol. 2018May
(April, 2018), ISBN 9781538648810 [doi] [abs]
 Lin, CY; Su, L; Wu, HT, WaveShape Function Analysis: When Cepstrum Meets Time–Frequency Analysis,
Journal of Fourier Analysis and Applications, vol. 24 no. 2
(April, 2018),
pp. 451505, Springer Nature [doi] [abs]
 Shen, C; Frasch, MG; Wu, HT; Herry, CL; Cao, M; Desrochers, A; Fecteau, G; Burns, P, Noninvasive acquisition of fetal ECG from the maternal xyphoid process: a feasibility study in pregnant sheep and a call for open data sets.,
Physiological Measurement, vol. 39 no. 3
(March, 2018),
pp. 035005 [doi] [abs]
 Frasch, MG; Lobmaier, SM; Stampalija, T; Desplats, P; Pallarés, ME; Pastor, V; Brocco, MA; Wu, HT; Schulkin, J; Herry, CL; Seely, AJE; Metz, GAS; Louzoun, Y; Antonelli, MC, Noninvasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multiscale multispecies perspective on data collection and analysis,
Neuroscience and Biobehavioral Reviews
(January, 2018) [doi] [abs]
 Wu, HT; Wu, N, Think globally, fit locally under the manifold setup: Asymptotic analysis of locally linear embedding,
The Annals of Statistics, vol. 46 no. 6B
(January, 2018),
pp. 38053837, Institute of Mathematical Statistics [doi] [abs]
 Talmon, R; Wu, HT, Latent common manifold learning with alternating diffusion: Analysis and applications,
Applied and Computational Harmonic Analysis
(January, 2018), Elsevier BV [doi] [abs]
 Kowalski, M; Meynard, A; Wu, HT, Convex Optimization approach to signals with fast varying instantaneous frequency,
Applied and Computational Harmonic Analysis, vol. 44 no. 1
(January, 2018),
pp. 89122, Elsevier BV [doi] [abs]
Wu, Nan
 Wu, HT; Wu, N, Think globally, fit locally under the manifold setup: Asymptotic analysis of locally linear embedding,
The Annals of Statistics, vol. 46 no. 6B
(January, 2018),
pp. 38053837, Institute of Mathematical Statistics [doi] [abs]
Young, Alex
 Lega, J; Sethuraman, S; Young, AL, On Collisions Times of ‘SelfSorting’ Interacting Particles in OneDimension with Random Initial Positions and Velocities,
Journal of Statistical Physics, vol. 170 no. 6
(March, 2018),
pp. 10881122, Springer Nature [doi] [abs]
Zhu, Wei
 Zhu, W; Qiu, Q; Huang, J; Calderbank, R; Sapiro, G; Daubechies, I, LDMNet: Low Dimensional Manifold Regularized Neural Networks,
Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, vol. abs/1711.06246
(December, 2018),
pp. 27432751, IEEE [doi] [abs]
