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Mathematics : Publications since January 2018

List all publications in the database.    :recent first  combined  bibtex listing:

Agarwal, Pankaj K.

  1. Gao, J; Agarwal, PK; Yang, J, Durable top-k queries on temporal data, Proceedings of the Vldb Endowment, vol. 11 no. 13 (January, 2018), pp. 2223-2235 [doi]  [abs]
  2. Agarwal, PK; Fox, K; Nath, A, Maintaining reeb graphs of triangulated 2-manifolds, Leibniz International Proceedings in Informatics, Lipics, vol. 93 (January, 2018), ISBN 9783959770552 [doi]  [abs]
  3. 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. 75-87, ACM Press, ISBN 9781450347068 [doi]  [abs]
  4. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Range-max queries on uncertain data, Journal of Computer and System Sciences, vol. 94 (June, 2018), pp. 118-134, Elsevier BV [doi]  [abs]
  5. 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. 41-414 [doi]  [abs]
  6. 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. 51-515, ISBN 9783959770682 [doi]  [abs]
  7. Agarwal, PK; Kyle, FOX; Nath, A; Sidiropoulos, A; Wang, Y, Computing the gromov-hausdorff distance for metric trees, Acm Transactions on Algorithms, vol. 14 no. 2 (June, 2018), pp. 1-20, Association for Computing Machinery (ACM) [doi]  [abs]
  8. 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]
  9. Agarwal, PK; Kyle, FOX; Salzman, O, An efficient algorithm for computing high-quality paths amid polygonal obstacles, Acm Transactions on Algorithms, vol. 14 no. 4 (August, 2018), pp. 1-21, Association for Computing Machinery (ACM) [doi]  [abs]
  10. Lowe, A; Agarwal, PK, Flood-risk analysis on terrains under the multiflow-direction model, Gis: Proceedings of the Acm International Symposium on Advances in Geographic Information Systems (November, 2018), pp. 53-62, ACM Press, ISBN 9781450358897 [doi]  [abs]
  11. Agarwal, PK; Kaplan, H; Kipper, G; Mulzer, W; Rote, G; Sharir, M; Xiao, A, Approximate minimum-weight matching with outliers under translation, Leibniz International Proceedings in Informatics, Lipics, vol. 123 (December, 2018), ISBN 9783959770941 [doi]  [abs]

Agazzi, Andrea

  1. 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. 1821-1855 [doi]  [abs]
  2. 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. 321-352 [doi]  [abs]

Arlotto, Alessandro

  1. Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem., Corr, vol. abs/1809.02016 (2018)
  2. Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks, Management Science (2018), INFORMS
  3. 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. 839-854 [doi]  [abs]
  4. 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. 41-53, WILEY [doi]  [abs]

Autry, Eric A.

  1. Autry, EA; Bayliss, A; Volpert, VA, Biological control with nonlocal interactions., Mathematical Biosciences, vol. 301 (July, 2018), pp. 129-146 [doi]  [abs]
  2. Clifton, SM; Hill, K; Karamchandani, AJ; Autry, EA; McMahon, P; Sun, G, Mathematical model of gender bias and homophily in professional hierarchies., Chaos (Woodbury, N.Y.), vol. 29 no. 2 (February, 2019), pp. 023135 [doi]  [abs]

Beale, J. Thomas

  1. Beale, JT; Ying, W, Solution of the Dirichlet problem by a finite difference analog of the boundary integral equation, Numerische Mathematik, vol. 141 no. 3 (March, 2019), pp. 605-626 [doi]  [abs]
  2. Tlupova, S; Beale, JT, Regularized single and double layer integrals in 3D Stokes flow, Journal of Computational Physics, vol. 386 (June, 2019), pp. 568-584 [doi]  [abs]

Beckman, Erin

  1. 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]
  2. 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) [doi]  [abs]

Bendich, Paul L

  1. 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. 2018-March (June, 2018), pp. 1-8, IEEE, ISBN 9781538620144 [doi]  [abs]
  2. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric cross-modal comparison of heterogeneous sensor data, Ieee Aerospace Conference Proceedings, vol. 2018-March (June, 2018), pp. 1-10, IEEE [doi]  [abs]

Bertozzi, Andrea L

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

Bray, Hubert

  1. Bray, H; Roesch, H, Proof of a Null Geometry Penrose Conjecture, Notices of the American Mathematical Society., vol. 65 (February, 2018), American Mathematical Society
  2. Sormani, C; Bray, HL; Minicozzi, WP; Eichmair, M; Huang, L-H; Yau, S-T; 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. 1-1, American Mathematical Society (AMS) [doi]

Calderbank, Robert

  1. Thompson, A; Calderbank, R, Sparse near-equiangular tight frames with applications in full duplex wireless communication, 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018-January (March, 2018), pp. 868-872, IEEE, ISBN 9781509059904 [doi]  [abs]

Cao, Yu

  1. Cao, Y; Lu, J, Stochastic dynamical low-rank approximation method, Journal of Computational Physics, vol. 372 (November, 2018), pp. 564-586, Elsevier BV [doi]  [abs]

Cheng, Cheng

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

Cheng, Xiuyuan

  1. 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. 4195-4204, JMLR.org
  2. Cheng, X; Mishne, G; Steinerberger, S, The geometry of nodal sets and outlier detection, Journal of Number Theory, vol. 185 (April, 2018), pp. 48-64, Elsevier BV [doi]
  3. Cheng, X; Rachh, M; Steinerberger, S, On the diffusion geometry of graph Laplacians and applications, Applied and Computational Harmonic Analysis, vol. 46 no. 3 (May, 2019), pp. 674-688, Elsevier BV [doi]

Cruz, Joshua

  1. Cruz, J; Giusti, C; Itskov, V; Kronholm, B, On Open and Closed Convex Codes, Discrete & Computational Geometry, vol. 61 no. 2 (March, 2019), pp. 247-270, Springer Nature [doi]

Dasgupta, Samit

  1. Samit Dasgupta, ; Mahesh Kakde, ; Kevin Ventullo,, On the Gross–Stark Conjecture, Annals of Mathematics, vol. 188 no. 3 (2018), pp. 833-833, Annals of Mathematics, Princeton U [doi]  [abs]
  2. Dasgupta, S; Voight, J, Sylvester’s problem and mock heegner points, Proceedings of the American Mathematical Society, vol. 146 no. 8 (January, 2018), pp. 3257-3273, American Mathematical Society (AMS) [doi]  [abs]
  3. Dasgupta, S; Spieß, M, Partial zeta values, Gross's tower of fields conjecture, and Gross-Stark units, Journal of the European Mathematical Society, vol. 20 no. 11 (January, 2018), pp. 2643-2683, European Mathematical Publishing House [doi]  [abs]

Daubechies, Ingrid

  1. Xu, J; Yang, H; Daubechies, I, Recursive diffeomorphism-based regression for shape functions, Siam Journal on Mathematical Analysis, vol. 50 no. 1 (January, 2018), pp. 5-32, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  2. 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]
  3. 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. 636-658 [doi]  [abs]
  4. 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. 872-907, Springer Nature [doi]  [abs]
  5. 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. 2743-2751 [doi]  [abs]
  6. Shan, S; Kovalsky, SZ; Winchester, JM; Boyer, DM; Daubechies, I, ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal, Methods in Ecology and Evolution, vol. 10 no. 4 (April, 2019), pp. 541-552 [doi]  [abs]

Dolbow, John E.

  1. Zhang, Z; Jiang, W; Dolbow, JE; Spencer, BW, A modified moment-fitted integration scheme for X-FEM applications with history-dependent material data, Computational Mechanics, vol. 62 no. 2 (August, 2018), pp. 233-252, Springer Nature [doi]  [abs]
  2. Geelen, RJM; Liu, Y; Dolbow, JE; Rodríguez-Ferran, A, An optimization-based phase-field method for continuous-discontinuous crack propagation, International Journal for Numerical Methods in Engineering, vol. 116 no. 1 (October, 2018), pp. 1-20, WILEY [doi]  [abs]
  3. Peco, C; Liu, Y; Rhea, C; Dolbow, JE, Models and simulations of surfactant-driven fracture in particle rafts, International Journal of Solids and Structures, vol. 156-157 (January, 2019), pp. 194-209, Elsevier BV [doi]  [abs]
  4. Liu, Y; Peco, C; Dolbow, J, A fully coupled mixed finite element method for surfactants spreading on thin liquid films, Computer Methods in Applied Mechanics and Engineering, vol. 345 (March, 2019), pp. 429-453, Elsevier BV [doi]  [abs]
  5. Geelen, RJM; Liu, Y; Hu, T; Tupek, MR; Dolbow, JE, A phase-field formulation for dynamic cohesive fracture, Computer Methods in Applied Mechanics and Engineering, vol. 348 (May, 2019), pp. 680-711 [doi]  [abs]
  6. Asareh, I; Kim, TY; Song, JH; Dolbow, JE, Corrigendum to “A linear complete extended finite element method for dynamic fracture simulation with non-nodal enrichments” [Finite Elem. Anal. Des. 152, 2018](S0168874X18305080)(10.1016/j.finel.2018.09.002), Finite Elements in Analysis and Design, vol. 157 (May, 2019), pp. 50 [doi]  [abs]

Dunson, David B.   (search)

  1. Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from two-photon imaging data., Plos One, vol. 13 no. 5 (January, 2018), pp. e0196527 [doi]  [abs]
  2. Miller, JW; Dunson, DB, Robust Bayesian Inference via Coarsening, Journal of the American Statistical Association (January, 2018), pp. 1-13, Informa UK Limited [doi]  [abs]
  3. Johndrow, JE; Smith, A; Pillai, N; Dunson, DB, MCMC for Imbalanced Categorical Data, Journal of the American Statistical Association (January, 2018) [doi]  [abs]
  4. Durante, D; Dunson, DB, Bayesian inference and testing of group differences in brain networks, Bayesian Analysis, vol. 13 no. 1 (January, 2018), pp. 29-58, Institute of Mathematical Statistics [doi]  [abs]
  5. van den Boom, W; Schroeder, RA; Manning, MW; Setji, TL; Fiestan, G-O; Dunson, DB, Effect of A1C and Glucose on Postoperative Mortality in Noncardiac and Cardiac Surgeries., Diabetes Care, vol. 41 no. 4 (April, 2018), pp. 782-788 [doi]  [abs]
  6. Dunson, DB, Statistics in the big data era: Failures of the machine, Statistics & Probability Letters, vol. 136 (May, 2018), pp. 4-9, Elsevier BV [doi]  [abs]
  7. Zhang, Z; Descoteaux, M; Zhang, J; Girard, G; Chamberland, M; Dunson, D; Srivastava, A; Zhu, H, Mapping population-based structural connectomes., Neuroimage, vol. 172 (May, 2018), pp. 130-145 [doi]  [abs]
  8. Johndrow, JE; Lum, K; Dunson, DB, Theoretical limits of microclustering for record linkage., Biometrika, vol. 105 no. 2 (June, 2018), pp. 431-446 [doi]  [abs]
  9. Shterev, ID; Dunson, DB; Chan, C; Sempowski, GD, Bayesian Multi-Plate High-Throughput Screening of Compounds., Scientific Reports, vol. 8 no. 1 (June, 2018), pp. 9551 [doi]  [abs]
  10. van den Boom, W; Mao, C; Schroeder, RA; Dunson, DB, Extrema-weighted feature extraction for functional data., Bioinformatics (Oxford, England), vol. 34 no. 14 (July, 2018), pp. 2457-2464 [doi]  [abs]
  11. 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. 657-672, Informa UK Limited [doi]  [abs]
  12. Srivastava, S; Li, C; Dunson, DB, Scalable Bayes via barycenter in Wasserstein space, Journal of Machine Learning Research, vol. 19 (August, 2018), pp. 1-35  [abs]
  13. Duan, LL; Johndrow, JE; Dunson, DB, Scaling up data augmentation MCMC via calibration, Journal of Machine Learning Research, vol. 19 (October, 2018)  [abs]
  14. 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. 1515-1527, Informa UK Limited [doi]  [abs]
  15. 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. 1528-1540 [doi]  [abs]
  16. Canale, A; Durante, D; Dunson, DB, Convex mixture regression for quantitative risk assessment., Biometrics, vol. 74 no. 4 (December, 2018), pp. 1331-1340 [doi]  [abs]
  17. 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]
  18. Wang, L; Zhang, Z; Dunson, D, Common and individual structure of brain networks, The Annals of Applied Statistics, vol. 13 no. 1 (January, 2019), pp. 85-112 [doi]  [abs]
  19. 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) (January, 2019) [doi]  [abs]
  20. Wang, L; Zhang, Z; Dunson, D, Symmetric Bilinear Regression for Signal Subgraph Estimation, Ieee Transactions on Signal Processing, vol. 67 no. 7 (April, 2019), pp. 1929-1940 [doi]  [abs]
  21. Zhang, Z; Allen, GI; Zhu, H; Dunson, D, Tensor network factorizations: Relationships between brain structural connectomes and traits., Neuroimage, vol. 197 (April, 2019), pp. 330-343 [doi]  [abs]

Durrett, Richard T.

  1. 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]
  2. 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]
  3. 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. 1-10, Institute of Mathematical Statistics [doi]  [abs]
  4. Talkington, A; Dantoin, C; Durrett, R, Ordinary Differential Equation Models for Adoptive Immunotherapy., Bulletin of Mathematical Biology, vol. 80 no. 5 (May, 2018), pp. 1059-1083 [doi]  [abs]
  5. Huo, R; Durrett, R, Latent voter model on locally tree-like random graphs, Stochastic Processes and Their Applications, vol. 128 no. 5 (May, 2018), pp. 1590-1614, Elsevier BV [doi]  [abs]
  6. Ma, R; Durrett, R, A simple evolutionary game arising from the study of the role of igf-II in pancreatic cancer, The Annals of Applied Probability, vol. 28 no. 5 (October, 2018), pp. 2896-2921, Institute of Mathematical Statistics [doi]  [abs]
  7. Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice, Stochastic Processes and Their Applications (January, 2019) [doi]  [abs]
  8. Wang, Z; Durrett, R, Extrapolating weak selection in evolutionary games., Journal of Mathematical Biology, vol. 78 no. 1-2 (January, 2019), pp. 135-154 [doi]  [abs]

Dym, Nadav

  1. 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. 1-10, Association for Computing Machinery (ACM) [doi]  [abs]
  2. Dym, N; Slutsky, R; Lipman, Y, Linear variational principle for Riemann mappings and discrete conformality., Proceedings of the National Academy of Sciences of the United States of America, vol. 116 no. 3 (January, 2019), pp. 732-737 [doi]  [abs]

Freeman, Daniel

  1. 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. 6933-6953, American Mathematical Society (AMS) [doi]

Getz, Jayce R.

  1. Getz, JR, Nonabelian fourier transforms for spherical representations, Pacific Journal of Mathematics, vol. 294 no. 2 (January, 2018), pp. 351-373, Mathematical Sciences Publishers [doi]  [abs]
  2. 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. 275-305, WILEY [doi]  [abs]
  3. Getz, JR; Liu, B, A summation formula for triples of quadratic spaces, Advances in Mathematics, vol. 347 (April, 2019), pp. 150-191 [doi]  [abs]

Gu, Miao (Pam)

  1. Gu, M; Martin, G, Factorization Tests and Algorithms Arising from Counting Modular Forms and Automorphic Representations, Canadian Mathematical Bulletin, vol. 62 no. 1 (March, 2019), pp. 81-97, Canadian Mathematical Society [doi]  [abs]

Hahn, Heekyoung

  1. Hahn, H; Huh, J; Lim, E; Sohn, J, From partition identities to a combinatorial approach to explicit Satake inversions, Annals of Combinatorics, vol. 22 (June, 2018), pp. 543-562, Springer Verlag [doi]

Hain, Richard   (search)

  1. 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. 723-750 [doi]  [abs]

Harer, John

  1. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric cross-modal comparison of heterogeneous sensor data, Ieee Aerospace Conference Proceedings, vol. 2018-March (June, 2018), pp. 1-10, IEEE [doi]  [abs]
  2. 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. 2018-March (June, 2018), pp. 1-8, IEEE, ISBN 9781538620144 [doi]  [abs]

He, Siming

  1. Bedrossian, J; He, S, Erratum: Suppression of blow-up in patlak-keller-segel via shear flows (SIAM Journal on Mathematical Analysis (2017) 49 (4722-4766) DOI: 10.1137/16M1093380), Siam Journal on Mathematical Analysis, vol. 50 no. 6 (January, 2018), pp. 6365-6372 [doi]  [abs]
  2. He, S, Suppression of blow-up in parabolic-parabolic Patlak-Keller-Segel via strictly monotone shear flows, Nonlinearity, vol. 31 no. 8 (July, 2018), pp. 3651-3688, IOP Publishing [doi]  [abs]
  3. He, S; Tadmor, E, Suppressing Chemotactic Blow-Up Through a Fast Splitting Scenario on the Plane, Archive for Rational Mechanics and Analysis, vol. 232 no. 2 (May, 2019), pp. 951-986, Springer Nature America, Inc [doi]  [abs]

Herschlag, Gregory J.

  1. 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. 825-834, IEEE, ISBN 9781538643686 [doi]  [abs]

Huo, Ran

  1. 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]
  2. Huo, R; Durrett, R, Latent voter model on locally tree-like random graphs, Stochastic Processes and Their Applications, vol. 128 no. 5 (May, 2018), pp. 1590-1614, Elsevier BV [doi]  [abs]

Junge, Matthew S

  1. 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]
  2. 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]
  3. 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]
  4. 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. 1013-1030, Institute of Mathematical Statistics [doi]  [abs]
  5. Brito, G; Fowler, C; Junge, M; Levy, A, Ewens Sampling and Invariable Generation, Combinatorics, Probability and Computing, vol. 27 no. 6 (November, 2018), pp. 853-891, Cambridge University Press (CUP) [doi]  [abs]
  6. Cristali, I; Junge, M; Durrett, R, Poisson percolation on the oriented square lattice, Stochastic Processes and Their Applications (January, 2019) [doi]  [abs]

Kiselev, Alexander A.

  1. 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. 6208-6229, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  2. Kiselev, A; Tan, C, Finite time blow up in the hyperbolic Boussinesq system, Advances in Mathematics, vol. 325 (February, 2018), pp. 34-55, Elsevier BV [doi]  [abs]
  3. 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. 1-37, Springer Nature [doi]  [abs]
  4. 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. 2127-2152, Springer Nature America, Inc [doi]  [abs]
  5. Kiselev, A, Special Issue Editorial: Small Scales and Singularity Formation in Fluid Dynamics, Journal of Nonlinear Science, vol. 28 no. 6 (December, 2018), pp. 2047-2050, Springer Nature America, Inc [doi]
  6. Kiselev, A; Li, C, Global regularity and fast small-scale formation for Euler patch equation in a smooth domain, Communications in Partial Differential Equations, vol. 44 no. 4 (April, 2019), pp. 279-308 [doi]  [abs]

Kovalsky, Shahar

  1. Shan, S; Kovalsky, SZ; Winchester, JM; Boyer, DM; Daubechies, I, ariaDNE: A robustly implemented algorithm for Dirichlet energy of the normal, Methods in Ecology and Evolution, vol. 10 no. 4 (April, 2019), pp. 541-552 [doi]  [abs]

Layton, Anita T.

  1. 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. 253-264, Elsevier BV [doi]  [abs]
  2. 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. F643-F657 [doi]  [abs]
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Layton, Harold

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Leete, Jessica

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Levine, Adam S.

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Li, Didong

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Li, Yingzhou

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Liu, Jian-Guo

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Lu, Jianfeng

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Maggioni, Mauro

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Malen, Greg

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Miller, Ezra

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Mukherjee, Sayan

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Nagy, Akos

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Ng, Lenhard L.

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Nolen, James H.

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Orizaga, Saulo

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Pfister, Henry

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Pierce, Lillian B.

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Plesser, M. Ronen

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Pollack, Aaron

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Randles, Amanda

  1. 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. 2792-2802, WILEY [doi]  [abs]
  2. 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. 825-834, IEEE, ISBN 9781538643686 [doi]  [abs]
  3. Hegele, LA; Scagliarini, A; Sbragaglia, M; Mattila, KK; Philippi, PC; Puleri, DF; Gounley, J; Randles, A, High-Reynolds-number turbulent cavity flow using the lattice Boltzmann method, Physical Review. E, vol. 98 no. 4 (October, 2018), American Physical Society (APS) [doi]  [abs]
  4. Dabagh, M; Randles, A, Role of deformable cancer cells on wall shear stress-associated-VEGF secretion by endothelium in microvasculature., Plos One, vol. 14 no. 2 (January, 2019), pp. e0211418 [doi]  [abs]
  5. Gounley, J; Draeger, EW; Oppelstrup, T; Krauss, WD; Gunnels, JA; Chaudhury, R; Nair, P; Frakes, D; Leopold, JA; Randles, A, Computing the ankle-brachial index with parallel computational fluid dynamics., Journal of Biomechanics, vol. 82 (January, 2019), pp. 28-37 [doi]  [abs]
  6. Gounley, J; Vardhan, M; Randles, A, A Framework for Comparing Vascular Hemodynamics at Different Points in Time., Computer Physics Communications, vol. 235 (February, 2019), pp. 1-8 [doi]  [abs]
  7. Vardhan, M; Das, A; Gouruev, J; Randles, A, Computational fluid modeling to understand the role of anatomy in bifurcation lesion disease, Proceedings 25th Ieee International Conference on High Performance Computing Workshops, Hipcw 2018 (February, 2019), pp. 56-64, ISBN 9781728101149 [doi]  [abs]
  8. Feiger, B; Vardhan, M; Gounley, J; Mortensen, M; Nair, P; Chaudhury, R; Frakes, D; Randles, A, Suitability of lattice Boltzmann inlet and outlet boundary conditions for simulating flow in image-derived vasculature., International Journal for Numerical Methods in Biomedical Engineering (March, 2019), pp. e3198 [doi]  [abs]
  9. Lee, S; Gounley, J; Randles, A; Vetter, JS, Performance portability study for massively parallel computational fluid dynamics application on scalable heterogeneous architectures, Journal of Parallel and Distributed Computing, vol. 129 (July, 2019), pp. 1-13 [doi]  [abs]

Reed, Michael C.

  1. Duncan, W; Best, J; Golubitsky, M; Nijhout, HF; Reed, M, Homeostasis despite instability., Mathematical Biosciences, vol. 300 (June, 2018), pp. 130-137 [doi]  [abs]
  2. Sadre-Marandi, F; Dahdoul, T; Reed, MC; Nijhout, HF, Sex differences in hepatic one-carbon metabolism., Bmc Systems Biology, vol. 12 no. 1 (October, 2018), pp. 89 [doi]  [abs]
  3. West, A; Best, J; Abdalla, A; Nijhout, HF; Reed, M; Hashemi, P, Voltammetric evidence for discrete serotonin circuits, linked to specific reuptake domains, in the mouse medial prefrontal cortex., Neurochemistry International, vol. 123 (February, 2019), pp. 50-58 [doi]  [abs]
  4. 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]

Robles, Colleen M

  1. Robles, C, Characterization of Calabi–Yau variations of Hodge structure over tube domains by characteristic forms, Mathematische Annalen, vol. 371 no. 3-4 (August, 2018), pp. 1229-1253, Springer Nature [doi]  [abs]

Rudin, Cynthia D.

  1. Angelino, E; Larus-Stone, 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. 1-78  [abs]
  2. Li, O; Liu, H; Chen, C; Rudin, C, Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions, 32nd Aaai Conference on Artificial Intelligence, Aaai 2018 (January, 2018), pp. 3530-3537, ISBN 9781577358008  [abs]
  3. Vu, M-AT; 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., The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, vol. 38 no. 7 (February, 2018), pp. 1601-1607 [doi]  [abs]
  4. 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. 449-466, Institute for Operations Research and the Management Sciences (INFORMS) [doi]  [abs]
  5. Rudin, C; Ertekin, Ş, Learning customized and optimized lists of rules with mathematical programming, Mathematical Programming Computation, vol. 10 no. 4 (December, 2018), pp. 659-702, Springer Nature America, Inc [doi]  [abs]
  6. 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 single-image super-resolution, Ieee Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2018-June (December, 2018), pp. 987-994, ISBN 9781538661000 [doi]  [abs]
  7. Ban, GY; Rudin, C, The big Data newsvendor: Practical insights from machine learning, Operations Research, vol. 67 no. 1 (January, 2019), pp. 90-108 [doi]  [abs]

Ryser, Marc D.

  1. Ryser, MD; Horton, JK; Hwang, ES, How Low Can We Go-and Should We? Risk Reduction for Minimal-Volume DCIS., Annals of Surgical Oncology, vol. 25 no. 2 (February, 2018), pp. 354-355 [doi]
  2. Role of Preoperative Variables in Reducing the Rate of Occult Invasive Disease for Women Considering Active Surveillance for Ductal Carcinoma In Situ., Jama Surgery, vol. 153 no. 3 (March, 2018), pp. 290-291 [doi]
  3. Ryser, MD; Min, B-H; Siegmund, KD; Shibata, D, Spatial mutation patterns as markers of early colorectal tumor cell mobility., Proceedings of the National Academy of Sciences of the United States of America, vol. 115 no. 22 (May, 2018), pp. 5774-5779 [doi]  [abs]
  4. 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]
  5. Ryser, MD; Gulati, R; Eisenberg, MC; Shen, Y; Hwang, ES; Etzioni, RB, Identification of the Fraction of Indolent Tumors and Associated Overdiagnosis in Breast Cancer Screening Trials., American Journal of Epidemiology, vol. 188 no. 1 (January, 2019), pp. 197-205 [doi]  [abs]
  6. Ryser, MD; Weaver, DL; Zhao, F; Worni, M; Grimm, LJ; Gulati, R; Etzioni, R; Hyslop, T; Lee, SJ; Hwang, ES, Cancer Outcomes in DCIS Patients Without Locoregional Treatment., Journal of the National Cancer Institute (February, 2019) [doi]  [abs]
  7. Shen, Y; Dong, W; Gulati, R; Ryser, MD; Etzioni, R, Estimating the frequency of indolent breast cancer in screening trials., Statistical Methods in Medical Research, vol. 28 no. 4 (April, 2019), pp. 1261-1271 [doi]  [abs]

Saper, Leslie

  1. Saper, L, ℒ-modules and micro-support, to appear in Annals of Mathematics (2018)

Sapiro, Guillermo

  1. Asiedu, MN; Simhal, A; Chaudhary, U; Mueller, JL; Lam, CT; Schmitt, JW; Venegas, G; Sapiro, G; Ramanujam, N, Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope, Ieee Transactions on Bio Medical Engineering (2018), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  2. 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]
  3. Bertrán, MA; Martínez, NL; Wang, Y; Dunson, D; Sapiro, G; Ringach, D, Active learning of cortical connectivity from two-photon imaging data., Plos One, vol. 13 no. 5 (January, 2018), pp. e0196527 [doi]  [abs]
  4. 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 low-cost point-of-care digital colposcope, Progress in Biomedical Optics and Imaging Proceedings of Spie, vol. 10485 (January, 2018), SPIE, ISBN 9781510614550 [doi]  [abs]
  5. Chiew, KS; Hashemi, J; Gans, LK; Lerebours, L; Clement, NJ; Vu, M-AT; Sapiro, G; Heller, NE; Adcock, RA, Motivational valence alters memory formation without altering exploration of a real-life spatial environment., Plos One, vol. 13 no. 3 (January, 2018), pp. e0193506 [doi]  [abs]
  6. Duchin, Y; Shamir, RR; Patriat, R; Kim, J; Vitek, JL; Sapiro, G; Harel, N, Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI., Plos One, vol. 13 no. 8 (January, 2018), pp. e0201469 [doi]  [abs]
  7. 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. 442-459, Springer International Publishing [doi]  [abs]
  8. 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. 6687-6696  [abs]
  9. 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. 2018-January (January, 2018), pp. 2564-2572, IEEE, ISBN 9781538610343 [doi]  [abs]
  10. Sokolić, J; Qiu, Q; Rodrigues, MRD; Sapiro, G, Learning to identify while failing to discriminate, Proceedings 2017 Ieee International Conference on Computer Vision Workshops, Iccvw 2017, vol. 2018-January (January, 2018), pp. 2537-2544, IEEE, ISBN 9781538610343 [doi]  [abs]
  11. Vu, M-AT; 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., The Journal of Neuroscience : the Official Journal of the Society for Neuroscience, vol. 38 no. 7 (February, 2018), pp. 1601-1607 [doi]  [abs]
  12. Pisharady, PK; Sotiropoulos, SN; Duarte-Carvajalino, 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. 488-503 [doi]  [abs]
  13. 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. 1676-1690, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  14. Bartesaghi, A; Aguerrebere, C; Falconieri, V; Banerjee, S; Earl, LA; Zhu, X; Grigorieff, N; Milne, JLS; Sapiro, G; Wu, X; Subramaniam, S, Atomic Resolution Cryo-EM Structure of β-Galactosidase., Structure (London, England : 1993), vol. 26 no. 6 (June, 2018), pp. 848-856.e3 [doi]  [abs]
  15. Hashemi, J; Dawson, G; Carpenter, KLH; Campbell, K; Qiu, Q; Espinosa, S; Marsan, S; Baker, JP; Egger, HL; Sapiro, G, Computer Vision Analysis for Quantification of Autism Risk Behaviors, Ieee Transactions on Affective Computing (August, 2018), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  16. Aguerrebere, C; Delbracio, M; Bartesaghi, A; Sapiro, G, A Practical Guide to Multi-Image Alignment, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018-April (September, 2018), pp. 1927-1931, IEEE [doi]  [abs]
  17. Ahn, HK; Qiu, Q; Bosch, E; Thompson, A; Robles, FE; Sapiro, G; Warren, WS; Calderbank, R, Classifying Pump-Probe Images of Melanocytic Lesions Using the WEYL Transform, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018-April (September, 2018), pp. 4209-4213, IEEE, ISBN 9781538646588 [doi]  [abs]
  18. 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. 2018-April (September, 2018), pp. 6767-6771, IEEE, ISBN 9781538646588 [doi]  [abs]
  19. 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]
  20. Lezama, J; Qiu, Q; Musé, P; Sapiro, G, OLE: Orthogonal Low-rank 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. 8109-8118 [doi]  [abs]
  21. 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. 2743-2751 [doi]  [abs]
  22. Bovery, MDMJ; Dawson, G; Hashemi, J; Sapiro, G, A Scalable Off-the-Shelf Framework for Measuring Patterns of Attention in Young Children and its Application in Autism Spectrum Disorder, Ieee Transactions on Affective Computing (2019), pp. 1-1, Institute of Electrical and Electronics Engineers (IEEE) [doi]
  23. Kim, J; Duchin, Y; Shamir, RR; Patriat, R; Vitek, J; Harel, N; Sapiro, G, Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation., Human Brain Mapping, vol. 40 no. 2 (February, 2019), pp. 679-698 [doi]  [abs]
  24. Dawson, G; Sapiro, G, Potential for Digital Behavioral Measurement Tools to Transform the Detection and Diagnosis of Autism Spectrum Disorder., Jama Pediatrics (February, 2019) [doi]
  25. Shamir, RR; Duchin, Y; Kim, J; Patriat, R; Marmor, O; Bergman, H; Vitek, JL; Sapiro, G; Bick, A; Eliahou, R; Eitan, R; Israel, Z; Harel, N, Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery., Neurosurgery, vol. 84 no. 3 (March, 2019), pp. 749-757 [doi]  [abs]
  26. Campbell, K; Carpenter, KL; Hashemi, J; Espinosa, S; Marsan, S; Borg, JS; Chang, Z; Qiu, Q; Vermeer, S; Adler, E; Tepper, M; Egger, HL; Baker, JP; Sapiro, G; Dawson, G, Computer vision analysis captures atypical attention in toddlers with autism., Autism, vol. 23 no. 3 (April, 2019), pp. 619-628 [doi]  [abs]

Sober, Barak

  1. Shaus, A; Sober, B; Tzang, O; Ioffe, Z; Cheshnovsky, O; Finkelstein, I; Piasetzky, E, Raman Binary Mapping of Iron Age Ostracon in an Unknown Material Composition and High-Fluorescence Setting—A Proof of Concept, Archaeometry, vol. 61 no. 2 (April, 2019), pp. 459-469, WILEY [doi]  [abs]

Stern, Mark A.

  1. Lipnowski, M; Stern, M, Geometry of the Smallest 1-form Laplacian Eigenvalue on Hyperbolic Manifolds, Geometrical and Functional Analysis Gafa, vol. 28 no. 6 (December, 2018), pp. 1717-1755, Springer Nature [doi]  [abs]

Stubbs, Kevin

  1. Czaja, W; Manning, B; Murphy, JM; Stubbs, K, Discrete directional Gabor frames, Applied and Computational Harmonic Analysis, vol. 45 no. 1 (July, 2018), pp. 1-21, Elsevier BV [doi]

Tarokh, Vahid

  1. Magnússon, S; Enyioha, C; Li, N; Fischione, C; Tarokh, V, Convergence of Limited Communication Gradient Methods., Ieee Trans. Automat. Contr., vol. 63 (2018), pp. 1356-1371
  2. Banerjee, T; Choi, JS; Pesaran, B; Ba, D; Tarokh, V, Classification of Local Field Potentials using Gaussian Sequence Model., Ssp (2018), pp. 683-687, IEEE, ISBN 978-1-5386-1571-3
  3. Banerjee, T; Whipps, GT; Gurram, P; Tarokh, V, Sequential Event Detection Using Multimodal Data in Nonstationary Environments., Fusion (2018), pp. 1940-1947, IEEE, ISBN 978-0-9964527-6-2
  4. 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. 717-732
  5. Shahrampour, S; Beirami, A; Tarokh, V, On Data-Dependent Random Features for Improved Generalization in Supervised Learning., edited by McIlraith, SA; Weinberger, KQ, Aaai (2018), pp. 4026-4033, AAAI Press
  6. Soloveychik, I; Tarokh, V, Stationary Geometric Graphical Model Selection., Corr, vol. abs/1806.03571 (2018)
  7. Ding, J; Tarokh, V; Yang, J-Y, Bridging AIC and BIC: A New Criterion for Autoregression., Ieee Trans. Information Theory, vol. 64 (2018), pp. 4024-4043
  8. 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. 65-II (2018), pp. 1259-1263
  9. 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. 2018-December (January, 2018), pp. 4690-4701, NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)  [abs]
  10. 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. 451-462, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  11. Shahrampour, S; Beirami, A; Tarokh, V, On data-dependent random features for improved generalization in supervised learning, 32nd Aaai Conference on Artificial Intelligence, Aaai 2018, vol. abs/1712.07102 (January, 2018), pp. 4026-4033  [abs]
  12. 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. 2018-January (January, 2018), pp. 228-235, IEEE [doi]  [abs]
  13. Soloveychik, I; Tarokh, V; Paulson, JA, On the spectral norms of pseudo-wigner and related matrices, 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol. 2018-January (January, 2018), pp. 61-66, IEEE [doi]  [abs]
  14. Soloveychik, I; Xiang, Y; Tarokh, V, Explicit symmetric pseudo-random matrices, Ieee International Symposium on Information Theory Proceedings, vol. 2018-January (January, 2018), pp. 424-428, IEEE, ISBN 9781509030972 [doi]  [abs]
  15. 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. 2018-January (March, 2018), pp. 928-932, IEEE, ISBN 9781509059904 [doi]  [abs]
  16. 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. 2018-January (March, 2018), pp. 750-754, IEEE, ISBN 9781509059904 [doi]  [abs]
  17. Han, Q; Ding, J; Airoldi, E; Tarokh, V, Modeling nonlinearity in multi-dimensional dependent data, 2017 Ieee Global Conference on Signal and Information Processing, Globalsip 2017 Proceedings, vol. 2018-January (March, 2018), pp. 206-210, IEEE, ISBN 9781509059904 [doi]  [abs]
  18. Soloveychik, I; Xiang, Y; Tarokh, V, Pseudo-Wigner Matrices, Ieee Transactions on Information Theory, vol. 64 no. 4 (April, 2018), pp. 3170-3178, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  19. Soloveychik, I; Xiang, Y; Tarokh, V, Symmetric Pseudo-Random Matrices, Ieee Transactions on Information Theory, vol. 64 no. 4 (April, 2018), pp. 3179-3196, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  20. 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. 2429-2440, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  21. 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. 1356-1371, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  22. 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. 717-732, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  23. 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. 218-222, IEEE [doi]  [abs]
  24. 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. 1259-1263, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  25. 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. 1940-1947, IEEE [doi]  [abs]
  26. 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. 2018-April (September, 2018), pp. 4414-4418, IEEE, ISBN 9781538646588 [doi]  [abs]
  27. Banerjee, T; Choi, J; Pesaran, B; Ba, D; Tarokh, V, Wavelet Shrinkage and Thresholding Based Robust Classification for Brain-Computer Interface, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp), vol. 2018-April (September, 2018), pp. 836-840, IEEE [doi]  [abs]
  28. 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. 2018-April (September, 2018), pp. 3994-3998, IEEE [doi]  [abs]
  29. Ding, J; Tarokh, V; Yang, Y, Model Selection Techniques: An Overview, Ieee Signal Processing Magazine, vol. 35 no. 6 (November, 2018), pp. 16-34, Institute of Electrical and Electronics Engineers (IEEE) [doi]  [abs]
  30. 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]
  31. Shahrampour, S; Beirami, A; Tarokh, V, Supervised Learning Using Data-dependent Random Features with Application to Seizure Detection, Proceedings of the Ieee Conference on Decision and Control, vol. 2018-December (January, 2019), pp. 1168-1173, ISBN 9781538613955 [doi]  [abs]
  32. Banerjee, T; Whipps, G; Gurram, P; Tarokh, V, Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data, 2018 Ieee Global Conference on Signal and Information Processing, Globalsip 2018 Proceedings, vol. abs/1807.06945 (February, 2019), pp. 126-130 [doi]  [abs]
  33. Xiang, Y; Ding, J; Tarokh, V, Estimation of the evolutionary spectra with application to stationarity test, Ieee Transactions on Signal Processing, vol. 67 no. 5 (March, 2019), pp. 1353-1365, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC [doi]  [abs]
  34. Angjelichinoski, M; Banerjee, T; Choi, J; Pesaran, B; Tarokh, V, Minimax-optimal decoding of movement goals from local field potentials using complex spectral features., Journal of Neural Engineering, vol. 16 no. 4 (April, 2019), pp. 046001 [doi]  [abs]
  35. Ding, J; Zhou, J; Tarokh, V, Asymptotically Optimal Prediction for Time-Varying Data Generating Processes, Ieee Transactions on Information Theory, vol. 65 no. 5 (May, 2019), pp. 3034-3067 [doi]  [abs]

Tralie, Christopher

  1. Tralie, CJ; Perea, JA, (Quasi)Periodicity Quantification in Video Data, Using Topology, Siam Journal on Imaging Sciences, vol. 11 no. 2 (January, 2018), pp. 1049-1077, Society for Industrial & Applied Mathematics (SIAM) [doi]  [abs]
  2. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric cross-modal comparison of heterogeneous sensor data, 2018 Ieee Aerospace Conference (March, 2018), IEEE [doi]  [abs]

Turnage-Butterbaugh, Caroline

  1. Conrey, JB; Turnage-Butterbaugh, CL, On r-gaps between zeros of the Riemann zeta-function, Bulletin of the London Mathematical Society (January, 2018) [doi]  [abs]

Vafaee, Faramarz

  1. Greene, JE; Lewallen, S; Vafaee, F, (1, 1) L-space knots, Compositio Mathematica, vol. 154 no. 5 (May, 2018), pp. 918-933, Oxford University Press (OUP) [doi]  [abs]

Venakides, Stephanos

  1. Perez-Arancibia, 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
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Viel, Shira

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Wang, Jiuya

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Wang, Lihan

  1. Lei Li, Jianfeng Lu, Jonathan Mattingly and Lihan Wang, Numerical methods for stochastic differential equations based on Gaussian mixture (December, 2018) [1812.11932]

Wang, Zhe

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Watson, Alexander

  1. Watson, A; Weinstein, MI, Wavepackets in Inhomogeneous Periodic Media: Propagation Through a One-Dimensional Band Crossing, Communications in Mathematical Physics, vol. 363 no. 2 (October, 2018), pp. 655-698, Springer Nature America, Inc [doi]  [abs]

Witelski, Thomas P.   (search)

  1. 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]
  2. BOWEN, M; WITELSKI, TP, Pressure-dipole solutions of the thin-film equation, European Journal of Applied Mathematics (April, 2018), pp. 1-42 [doi]  [abs]
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Wong, Jeffrey T

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Wu, Hau-Tieng

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  3. 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]
  4. 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. 89-122, Elsevier BV [doi]  [abs]
  5. Shen, C; Frasch, MG; Wu, HT; Herry, CL; Cao, M; Desrochers, A; Fecteau, G; Burns, P, Non-invasive 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]
  6. Lin, CY; Su, L; Wu, HT, Wave-Shape Function Analysis: When Cepstrum Meets Time–Frequency Analysis, Journal of Fourier Analysis and Applications, vol. 24 no. 2 (April, 2018), pp. 451-505, Springer Nature [doi]  [abs]
  7. Wu, JC; Wang, CW; Huang, YH; Wu, HT; Huang, PC; Lo, YL, A Portable Monitoring System with Automatic Event Detection for Sleep Apnea Level-IV Evaluation, Proceedings Ieee International Symposium on Circuits and Systems, vol. 2018-May (April, 2018), ISBN 9781538648810 [doi]  [abs]
  8. Wu, H-T; Soliman, EZ, A new approach for analysis of heart rate variability and QT variability in long-term ECG recording., Biomedical Engineering Online, vol. 17 no. 1 (May, 2018), pp. 54 [doi]  [abs]
  9. Liu, TC; Wu, HT; Chen, YH; Fang, TY; Wang, PC; Liu, YW, Analysis of click-evoked 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]
  10. Wu, HT; Liu, YW, Analyzing transient-evoked otoacoustic emissions by concentration of frequency and time, The Journal of the Acoustical Society of America, vol. 144 no. 1 (July, 2018), pp. 448-466, Acoustical Society of America (ASA) [doi]  [abs]
  11. Wu, HT; Wu, JC; Huang, PC; Lin, TY; Wang, TY; Huang, YH; Lo, YL, Phenotype-based and self-learning inter-individual sleep apnea screening with a level IV-like monitoring system, Frontiers in Physiology, vol. 9 no. JUL (July, 2018), FRONTIERS MEDIA SA [doi]  [abs]
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  13. Escalona-Vargas, D; Wu, H-T; 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. 483-487, Springer Nature [doi]  [abs]
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  16. Katz, O; Talmon, R; Lo, YL; Wu, HT, Alternating diffusion maps for multimodal data fusion, Information Fusion, vol. 45 (January, 2019), pp. 346-360, Elsevier BV [doi]  [abs]
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  19. Chen, H-Y; Pan, H-C; Chen, Y-C; Chen, Y-C; Lin, Y-H; Yang, S-H; Chen, J-L; Wu, H-T, Traditional Chinese medicine use is associated with lower end-stage renal disease and mortality rates among patients with diabetic nephropathy: a population-based cohort study., Bmc Complementary and Alternative Medicine, vol. 19 no. 1 (April, 2019), pp. 81 [doi]  [abs]
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Wu, Nan

  1. 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. 3805-3837, Institute of Mathematical Statistics [doi]  [abs]

Yao, Dong

  1. van der Hoorn, P, Average nearest neighbor degrees in scale-free networks, Internet Mathematics (January, 2018), Internet Mathematics [doi]

Young, Alex

  1. Lega, J; Sethuraman, S; Young, AL, On Collisions Times of ‘Self-Sorting’ Interacting Particles in One-Dimension with Random Initial Positions and Velocities, Journal of Statistical Physics, vol. 170 no. 6 (March, 2018), pp. 1088-1122, Springer Nature [doi]

Zhu, Wei

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