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Mathematics Faculty: Publications since January 2017

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

Agarwal, Pankaj K.

  1. 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) [doi]  [abs]
  2. 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]
  3. 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 [doi]  [abs]
  4. 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]
  5. 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]
  6. 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, ISBN 9781450347068 [doi]  [abs]
  7. Agarwal, PK; Fox, K; Nath, A; Sidiropoulos, A; Wang, Y, Computing the Gromov-Hausdorff Distance for Metric Trees, Acm Transactions on Algorithms, vol. 14 no. 2 (April, 2018), pp. 1-20 [doi]
  8. 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]
  9. Rav, M; Lowe, A; Agarwal, PK, Flood Risk Analysis on Terrains, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, vol. 2017-November (November, 2017), ISBN 9781450354905 [doi]  [abs]
  10. Agarwal, PK; Har-Peled, S; Suri, S; Yıldız, H; Zhang, W, Convex Hulls Under Uncertainty, Algorithmica, vol. 79 no. 2 (October, 2017), pp. 340-367 [doi]
  11. Agarwal, PK; Rubin, N; Sharir, M, Approximate nearest neighbor search amid higher-dimensional flats, Leibniz International Proceedings in Informatics, Lipics, vol. 87 (September, 2017), ISBN 9783959770491 [doi]  [abs]
  12. Agarwal, PK; Kumar, N; Sintos, S; Suri, S, Efficient algorithms for k-regret minimizing sets, Leibniz International Proceedings in Informatics, Lipics, vol. 75 (August, 2017), ISBN 9783959770361 [doi]  [abs]
  13. Agarwal, PK; Fox, K; Panigrahi, D; Varadarajan, KR; Xiao, A, Faster algorithms for the geometric transportation problem, Leibniz International Proceedings in Informatics, Lipics, vol. 77 (June, 2017), pp. 71-716, ISBN 9783959770385 [doi]  [abs]
  14. Wu, Y; Agarwal, PK; Li, C; Yang, J; Yu, C, Computational Fact Checking through Query Perturbations, Acm Transactions on Database Systems, vol. 42 no. 1 (January, 2017), pp. 1-41 [doi]
  15. Wu, Y; Gao, J; Agarwal, PK; Yang, J, Finding diverse, high-value representatives on a surface of answers, Proceedings of the Vldb Endowment, vol. 10 no. 7 (January, 2017), pp. 793-804  [abs]
  16. Garg, N; Sadiq, M; Agarwal, P, GOASREP: Goal oriented approach for software requirements elicitation and prioritization using analytic hierarchy process, Advances in Intelligent Systems and Computing, vol. 516 (January, 2017), pp. 281-287, ISBN 9789811031557 [doi]  [abs]

Arlotto, Alessandro

  1. 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]
  2. Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem., Corr, vol. abs/1809.02016 (2018)
  3. Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks, Management Science (2018), INFORMS
  4. 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]
  5. Arlotto, A; Gurvich, I, Uniformly bounded regret in the multi-secretary problem (October, 2017)  [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]
  2. Autry, EA; Bayliss, A; Volpert, VA, Traveling waves in a nonlocal, piecewise linear reaction–diffusion population model, Nonlinearity, vol. 30 no. 8 (August, 2017), pp. 3304-3331 [doi]

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, 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, Proceedings of the 39th Ieee Aerospace Conference (March, 2018)  [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

Bryant, Robert   (search)

  1. Bryant, R; Huang, L; Mo, X, On Finsler surfaces of constant flag curvature with a Killing field, Journal of Geometry and Physics, vol. 116 (June, 2017), pp. 345-357 [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, ISBN 9781509059904 [doi]  [abs]
  2. Mappouras, G; Vahid, A; Calderbank, R; Hower, DR; Sorin, DJ, Jenga: Efficient fault tolerance for stacked DRAM, Proceedings - 35th IEEE International Conference on Computer Design, ICCD 2017 (November, 2017), pp. 361-368, ISBN 9781538622544 [doi]  [abs]
  3. Kadhe, S; Calderbank, R, Rate optimal binary linear locally repairable codes with small availability, Ieee International Symposium on Information Theory Proceedings (August, 2017), pp. 166-170, ISBN 9781509040964 [doi]  [abs]
  4. Michelusi, N; Nokleby, M; Mitra, U; Calderbank, R, Multi-scale spectrum sensing in small-cell mm-wave cognitive wireless networks, Ieee International Conference on Communications (July, 2017), ISBN 9781467389990 [doi]  [abs]
  5. Cnaan-On, I; Harms, A; Krolik, JL; Calderbank, AR, Run-length limited codes for backscatter communication, 2015 Ieee International Conference on Acoustics, Speech, and Signal Processing (Icassp) (June, 2017), pp. 6110-6114, ISBN 9781509041176 [doi]  [abs]
  6. Wang, L; Chen, M; Rodrigues, M; Wilcox, D; Calderbank, R; Carin, L, Information-Theoretic Compressive Measurement Design., Ieee Transactions on Pattern Analysis and Machine Intelligence, vol. 39 no. 6 (June, 2017), pp. 1150-1164 [doi]  [abs]
  7. Campbell, K; Carpenter, KLH; Espinosa, S; Hashemi, J; Qiu, Q; Tepper, M; Calderbank, R; Sapiro, G; Egger, HL; Baker, JP; Dawson, G, Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism., The Journal of Pediatrics, vol. 183 (April, 2017), pp. 133-139.e1 [doi]  [abs]
  8. Hadani, R; Rakib, S; Tsatsanis, M; Monk, A; Goldsmith, AJ; Molisch, AF; Calderbank, R, Orthogonal Time Frequency Space Modulation, 2017 Ieee Wireless Communications and Networking Conference (Wcnc) (March, 2017), IEEE, ISBN 9781509041831 [doi]  [abs]

Cheng, Cheng

  1. Cheng, C; Jiang, Y; Sun, Q, Spatially distributed sampling and reconstruction, Applied and Computational Harmonic Analysis (August, 2017) [doi]
  2. Li, L; Cheng, C; Han, D; Sun, Q; Shi, G, Phase Retrieval From Multiple-Window Short-Time Fourier Measurements, Ieee Signal Processing Letters, vol. 24 no. 4 (April, 2017), pp. 372-376 [doi]

Cheng, Xiuyuan

  1. Cheng, X; Rachh, M; Steinerberger, S, On the diffusion geometry of graph laplacians and applications, Applied and Computational Harmonic Analysis (April, 2018) [doi]
  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 [doi]
  3. 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
  4. Lu, J; Lu, Y; Wang, X; Li, X; Linderman, GC; Wu, C; Cheng, X; Mu, L; Zhang, H; Liu, J; Su, M; Zhao, H; Spatz, ES; Spertus, JA; Masoudi, FA; Krumholz, HM; Jiang, L, Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project), Lancet (London, England), vol. 390 no. 10112 (December, 2017), pp. 2549-2558 [doi]

Dasgupta, Samit

  1. Dasgupta, S; Kakde, M; Ventullo, K, On the Gross-Stark Conjecture, Annals of Mathematics, vol. 188 no. 3 (November, 2018), pp. 833-870 [doi]  [abs]
  2. Dasgupta, S; Voight, J, Sylvester’s problem and mock Heegner points, Proceedings of the American Mathematical Society, vol. 146 no. 8 (March, 2018), pp. 3257-3273 [doi]
  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 [doi]  [abs]

Daubechies, Ingrid

  1. 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]
  2. 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 [doi]  [abs]
  3. Alaifari, R; Daubechies, I; Grohs, P; Yin, R, Stable Phase Retrieval in Infinite Dimensions, Foundations of Computational Mathematics (January, 2018) [doi]  [abs]
  4. Alaifari, R; Daubechies, I; Grohs, P; Thakur, G, Reconstructing Real-Valued Functions from Unsigned Coefficients with Respect to Wavelet and Other Frames, Journal of Fourier Analysis and Applications, vol. 23 no. 6 (December, 2017), pp. 1480-1494 [doi]
  5. Deligiannis, N; Mota, JFC; Cornelis, B; Rodrigues, MRD; Daubechies, I, Multi-Modal Dictionary Learning for Image Separation With Application in Art Investigation., Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society, vol. 26 no. 2 (February, 2017), pp. 751-764 [doi]  [abs]
  6. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society, vol. 26 no. 1 (January, 2017), pp. 160-171 [doi]  [abs]
  7. Voronin, S; Daubechies, I, An iteratively reweighted least squares algorithm for sparse regularization, in Contemporary Mathematics, vol. 693 (January, 2017), pp. 391-411 [doi]  [abs]
  8. Yin, R; Gao, T; Lu, YM; Daubechies, I, A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets, Siam Journal on Imaging Sciences, vol. 10 no. 2 (January, 2017), pp. 711-750 [doi]
  9. Fodor, G; Cornelis, B; Yin, R; Dooms, A; Daubechies, I, Cradle Removal in X-Ray Images of Panel Paintings, Image Processing On Line, vol. 7 (2017), pp. 23-42 [doi]

Dolbow, John E.

  1. 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 [doi]  [abs]
  2. 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 [doi]  [abs]
  3. Peco, C; Chen, W; Liu, Y; Bandi, MM; Dolbow, JE; Fried, E, Influence of surface tension in the surfactant-driven fracture of closely-packed particulate monolayers., Soft Matter, vol. 13 no. 35 (September, 2017), pp. 5832-5841 [doi]  [abs]
  4. Zhang, Z; Dolbow, JE, Remeshing strategies for large deformation problems with frictional contact and nearly incompressible materials, International Journal for Numerical Methods in Engineering, vol. 109 no. 9 (March, 2017), pp. 1289-1314 [doi]
  5. Stershic, AJ; Dolbow, JE; Moës, N, The Thick Level-Set model for dynamic fragmentation, Engineering Fracture Mechanics, vol. 172 (March, 2017), pp. 39-60 [doi]

Dunson, David B.   (search)

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. Miller, JW; Dunson, DB, Robust Bayesian inference via coarsening, Journal of the American Statistical Association (May, 2018), pp. 1-31 [doi]
  6. 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]
  7. 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]
  8. 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 (January, 2018), pp. 1-13 [doi]  [abs]
  9. 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 (January, 2018) [doi]
  10. Dunson, DB, Statistics in the big data era: Failures of the machine, Statistics & Probability Letters (January, 2018) [doi]  [abs]
  11. 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]
  12. Minsker, S; Srivastava, S; Lin, L; Dunson, DB, Robust and scalable bayes via a median of subset posterior measures, Journal of Machine Learning Research, vol. 18 (December, 2017), pp. 1-40  [abs]
  13. Wheeler, MW; Dunson, DB; Herring, AH, Bayesian Local Extremum Splines., Biometrika, vol. 104 no. 4 (December, 2017), pp. 939-952, Oxford University Press (OUP)  [abs]
  14. Shang, Y; Dunson, D; Song, J-S, Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics, Operations Research, vol. 65 no. 6 (December, 2017), pp. 1574-1588 [doi]
  15. Durante, D; Dunson, DB; Vogelstein, JT, Rejoinder: Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association, vol. 112 no. 520 (October, 2017), pp. 1547-1552 [doi]
  16. Durante, D; Dunson, DB; Vogelstein, JT, Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association, vol. 112 no. 520 (October, 2017), pp. 1516-1530 [doi]  [abs]
  17. Reddy, A; Zhang, J; Davis, NS; Moffitt, AB; Love, CL; Waldrop, A; Leppa, S; Pasanen, A; Meriranta, L; Karjalainen-Lindsberg, M-L; Nørgaard, P; Pedersen, M; Gang, AO; Høgdall, E; Heavican, TB; Lone, W; Iqbal, J; Qin, Q; Li, G; Kim, SY; Healy, J; Richards, KL; Fedoriw, Y; Bernal-Mizrachi, L; Koff, JL; Staton, AD; Flowers, CR; Paltiel, O; Goldschmidt, N; Calaminici, M; Clear, A; Gribben, J; Nguyen, E; Czader, MB; Ondrejka, SL; Collie, A; Hsi, ED; Tse, E; Au-Yeung, RKH; Kwong, Y-L; Srivastava, G; Choi, WWL; Evens, AM; Pilichowska, M; Sengar, M; Reddy, N; Li, S; Chadburn, A; Gordon, LI; Jaffe, ES; Levy, S; Rempel, R; Tzeng, T; Happ, LE; Dave, T; Rajagopalan, D; Datta, J; Dunson, DB; Dave, SS, Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma., Cell, vol. 171 no. 2 (October, 2017), pp. 481-494.e15 [doi]  [abs]
  18. Li, C; Srivastava, S; Dunson, DB, Simple, scalable and accurate posterior interval estimation, Biometrika, vol. 104 no. 3 (September, 2017), pp. 665-680 [doi]  [abs]
  19. Lock, EF; Dunson, DB, Bayesian genome- and epigenome-wide association studies with gene level dependence., Biometrics, vol. 73 no. 3 (September, 2017), pp. 1018-1028 [doi]  [abs]
  20. Srivastava, S; Engelhardt, BE; Dunson, DB, Expandable factor analysis., Biometrika, vol. 104 no. 3 (September, 2017), pp. 649-663 [doi]  [abs]
  21. Guhaniyogi, R; Qamar, S; Dunson, DB, Bayesian tensor regression, Journal of Machine Learning Research, vol. 18 (August, 2017), pp. 1-31  [abs]
  22. Schaich Borg, J; Srivastava, S; Lin, L; Heffner, J; Dunson, D; Dzirasa, K; de Lecea, L, Rat intersubjective decisions are encoded by frequency-specific oscillatory contexts., Brain and Behavior, vol. 7 no. 6 (June, 2017), pp. e00710 [doi]  [abs]
  23. Zhu, B; Dunson, DB, Bayesian Functional Data Modeling for Heterogeneous Volatility, Bayesian Analysis, vol. 12 no. 2 (June, 2017), pp. 335-350 [doi]
  24. Wang, L; Durante, D; Jung, RE; Dunson, DB, Bayesian network-response regression., Bioinformatics (Oxford, England), vol. 33 no. 12 (June, 2017), pp. 1859-1866 [doi]  [abs]
  25. Ovaskainen, O; Tikhonov, G; Norberg, A; Guillaume Blanchet, F; Duan, L; Dunson, D; Roslin, T; Abrego, N, How to make more out of community data? A conceptual framework and its implementation as models and software., Ecology Letters, vol. 20 no. 5 (May, 2017), pp. 561-576 [doi]  [abs]
  26. Ovaskainen, O; Tikhonov, G; Dunson, D; Grøtan, V; Engen, S; Sæther, B-E; Abrego, N, How are species interactions structured in species-rich communities? A new method for analysing time-series data., Proceedings of the Royal Society B: Biological Sciences, vol. 284 no. 1855 (May, 2017), pp. 20170768-20170768 [doi]  [abs]
  27. Moffitt, AB; Ondrejka, SL; McKinney, M; Rempel, RE; Goodlad, JR; Teh, CH; Leppa, S; Mannisto, S; Kovanen, PE; Tse, E; Au-Yeung, RKH; Kwong, Y-L; Srivastava, G; Iqbal, J; Yu, J; Naresh, K; Villa, D; Gascoyne, RD; Said, J; Czader, MB; Chadburn, A; Richards, KL; Rajagopalan, D; Davis, NS; Smith, EC; Palus, BC; Tzeng, TJ; Healy, JA; Lugar, PL; Datta, J; Love, C; Levy, S; Dunson, DB; Zhuang, Y; Hsi, ED; Dave, SS, Enteropathy-associated T cell lymphoma subtypes are characterized by loss of function of SETD2., The Journal of Experimental Medicine, vol. 214 no. 5 (May, 2017), pp. 1371-1386 [doi]  [abs]
  28. Durante, D; Paganin, S; Scarpa, B; Dunson, DB, Bayesian modelling of networks in complex business intelligence problems, Journal of the Royal Statistical Society. Series C, Applied Statistics, vol. 66 no. 3 (April, 2017), pp. 555-580 [doi]
  29. McKinney, M; Moffitt, AB; Gaulard, P; Travert, M; De Leval, L; Nicolae, A; Raffeld, M; Jaffe, ES; Pittaluga, S; Xi, L; Heavican, T; Iqbal, J; Belhadj, K; Delfau-Larue, MH; Fataccioli, V; Czader, MB; Lossos, IS; Chapman-Fredricks, JR; Richards, KL; Fedoriw, Y; Ondrejka, SL; Hsi, ED; Low, L; Weisenburger, D; Chan, WC; Mehta-Shah, N; Horwitz, S; Bernal-Mizrachi, L; Flowers, CR; Beaven, AW; Parihar, M; Baseggio, L; Parrens, M; Moreau, A; Sujobert, P; Pilichowska, M; Evens, AM; Chadburn, A; Au-Yeung, RKH; Srivastava, G; Choi, WWL; Goodlad, JR; Aurer, I; Basic-Kinda, S; Gascoyne, RD; Davis, NS; Li, G; Zhang, J; Rajagopalan, D; Reddy, A; Love, C; Levy, S; Zhuang, Y; Datta, J; Dunson, DB; Davé, SS, The Genetic Basis of Hepatosplenic T-cell Lymphoma., Cancer Discovery, vol. 7 no. 4 (April, 2017), pp. 369-379 [doi]  [abs]
  30. Tikhonov, G; Abrego, N; Dunson, D; Ovaskainen, O, Using joint species distribution models for evaluating how species-to-species associations depend on the environmental context, edited by Warton, D, Methods in Ecology and Evolution, vol. 8 no. 4 (April, 2017), pp. 443-452 [doi]
  31. Dunson, DB, Toward Automated Prior Choice, Statistical Science, vol. 32 no. 1 (February, 2017), pp. 41-43 [doi]
  32. Abrego, N; Dunson, D; Halme, P; Salcedo, I; Ovaskainen, O, Wood-inhabiting fungi with tight associations with other species have declined as a response to forest management, Oikos, vol. 126 no. 2 (February, 2017) [doi]
  33. Johndrow, JE; Bhattacharya, A; Dunson, DB, TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS., The Annals of Statistics, vol. 45 no. 1 (January, 2017), pp. 1-38 [doi]  [abs]
  34. Lin, L; St Thomas, B; Zhu, H; Dunson, DB, Extrinsic local regression on manifold-valued data., Journal of the American Statistical Association, vol. 112 no. 519 (January, 2017), pp. 1261-1273 [doi]  [abs]
  35. Durante, D; Dunson, DB; Vogelstein, JT, Nonparametric Bayes Modeling of Populations of Networks, Journal of the American Statistical Association, vol. 112 no. 520 (2017), pp. 1516-1530 [doi]
  36. Lin, L; Rao, V; Dunson, D, Bayesian nonparametric inference on the Stiefel manifold, Statistica Sinica (2017) [doi]

Durrett, Richard T.

  1. 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 [doi]  [abs]
  2. Wang, Z; Durrett, R, Extrapolating weak selection in evolutionary games., Journal of Mathematical Biology (July, 2018) [doi]  [abs]
  3. 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]
  4. 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 [doi]
  5. 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) [doi]  [abs]
  6. Basak, A; Durrett, R; Foxall, E, Diffusion limit for the partner model at the critical value, Electronic Journal of Probability, vol. 23 (January, 2018) [doi]  [abs]
  7. 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 [doi]  [abs]
  8. Lopatkin, AJ; Meredith, HR; Srimani, JK; Pfeiffer, C; Durrett, R; You, L, Persistence and reversal of plasmid-mediated antibiotic resistance., Nature Communications, vol. 8 no. 1 (November, 2017), pp. 1689 [doi]  [abs]
  9. Gleeson, JP; Durrett, R, Temporal profiles of avalanches on networks., Nature Communications, vol. 8 no. 1 (October, 2017), pp. 1227 [doi]  [abs]
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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 (July, 2018), pp. 1-10, Association for Computing Machinery (ACM) [doi]  [abs]
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Fernandes de Oliveira, Goncalo M.

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Getz, Jayce R.

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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)

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Harer, John

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He, Siming

  1. He, S, Suppression of blow-up in parabolic–parabolic Patlak–Keller–Segel via strictly monotone shear flows, Nonlinearity, vol. 31 no. 8 (August, 2018), pp. 3651-3688 [doi]
  2. He, S; Tadmor, E, Suppressing Chemotactic Blow-Up Through a Fast Splitting Scenario on the Plane, Archive for Rational Mechanics and Analysis (January, 2018), Springer Nature America, Inc [doi]  [abs]
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Herschlag, Gregory J.

  1. Cao, Y; Feng, Y; Ryser, MD; Zhu, K; Herschlag, G; Cao, C; Marusak, K; Zauscher, S; You, L, Programmable assembly of pressure sensors using pattern-forming bacteria., Nature Biotechnology, vol. 35 no. 11 (November, 2017), pp. 1087-1093 [doi]  [abs]
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Junge, Matthew S

  1. 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 [doi]
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Kiselev, Alexander A.

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Kovalsky, Shahar

  1. Aigerman, N; Kovalsky, SZ; Lipman, Y, Spherical orbifold tutte embeddings, Acm Transactions on Graphics, vol. 36 no. 4 (July, 2017), pp. 1-13 [doi]
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Layton, Anita T.

  1. 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. F692-F700 [doi]  [abs]
  2. 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. F665-F676 [doi]  [abs]
  3. 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. 384-389 [doi]  [abs]
  4. Layton, AT, Sweet success? SGLT2 inhibitors and diabetes., American Journal of Physiology. Renal Physiology, vol. 314 no. 6 (June, 2018), pp. F1034-F1035 [doi]
  5. Leete, J; Gurley, S; Layton, AT, Modeling sex differences in the renin angiotensin system and the efficacy of antihypertensive therapies, Computers & Chemical Engineering, vol. 112 (April, 2018), pp. 253-264 [doi]  [abs]
  6. 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]
  7. 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]
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Layton, Harold

  1. 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. F692-F700 [doi]  [abs]

Levine, Adam S.

  1. Baldwin, JA; Levine, AS; Sarkar, S, Khovanov homology and knot Floer homology for pointed links, Journal of Knot Theory and Its Ramifications, vol. 26 no. 02 (February, 2017), pp. 1740004-1740004 [doi]

Li, Lei

  1. Li, L; Liu, J-G; Lu, J, Fractional Stochastic Differential Equations Satisfying Fluctuation-Dissipation Theorem, Journal of Statistical Physics, vol. 169 no. 2 (October, 2017), pp. 316-339 [doi]
  2. Li, L; Xu, X; Spagnolie, SE, A Locally Gradient-Preserving Reinitialization for Level Set Functions, Journal of Scientific Computing, vol. 71 no. 1 (April, 2017), pp. 274-302 [doi]
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Li, Yingzhou

  1. Li, Y; Ying, L, Distributed-memory hierarchical interpolative factorization, Research in the Mathematical Sciences, vol. 4 no. 1 (December, 2017) [doi]
  2. Zhang, L; Sun, L; Guan, Z; Lee, S; Li, Y; Deng, HD; Li, Y; Ahlborg, NL; Boloor, M; Melosh, NA; Chueh, WC, Quantifying and Elucidating Thermally Enhanced Minority Carrier Diffusion Length Using Radius-Controlled Rutile Nanowires., Nano Letters, vol. 17 no. 9 (September, 2017), pp. 5264-5272 [doi]  [abs]
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Liu, Jian-Guo

  1. Feng, Y; Li, L; Liu, J-G; Xu, X, A note on one-dimensional time fractional ODEs, Applied Mathematics Letters, vol. 83 (September, 2018), pp. 87-94 [doi]
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Lu, Jianfeng

  1. Cao, Y; Lu, J, Stochastic dynamical low-rank approximation method, Journal of Computational Physics, vol. 372 (November, 2018), pp. 564-586 [doi]  [abs]
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  3. Barthel, T; Lu, J, Fundamental Limitations for Measurements in Quantum Many-Body Systems., Physical Review Letters, vol. 121 no. 8 (August, 2018), pp. 080406 [doi]  [abs]
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  7. Cai, Z; Lu, J, A Quantum Kinetic Monte Carlo Method for Quantum Many-Body Spin Dynamics, Siam Journal on Scientific Computing, vol. 40 no. 3 (January, 2018), pp. B706-B722 [doi]
  8. Lu, J; Yang, H, Phase-space sketching for crystal image analysis based on synchrosqueezed transforms, Siam Journal on Imaging Sciences, vol. 11 no. 3 (January, 2018), pp. 1954-1978, SIAM PUBLICATIONS [doi]  [abs]
  9. Delgadillo, R; Lu, J; Yang, X, Frozen Gaussian approximation for high frequency wave propagation in periodic media, Asymptotic Analysis, vol. 110 no. 3-4 (January, 2018), pp. 113-135 [doi]  [abs]
  10. Yu, VW-Z; Corsetti, F; García, A; Huhn, WP; Jacquelin, M; Jia, W; Lange, B; Lin, L; Lu, J; Mi, W; Seifitokaldani, A; Vázquez-Mayagoitia, 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. 267-285 [doi]
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  13. Cai, Z; Lu, J, A surface hopping Gaussian beam method for high-dimensional transport systems, Siam Journal on Scientific Computing, vol. 40 no. 5 (January, 2018), pp. B1277-B1301, SIAM PUBLICATIONS [doi]  [abs]
  14. Zhu, W; Qiu, Q; Wang, B; Lu, J; Sapiro, G; Daubechies, I, Stop memorizing: A data-dependent regularization framework for intrinsic pattern learning., Corr, vol. abs/1805.07291 (2018)
  15. Lu, J; Thicke, K, Cubic scaling algorithms for RPA correlation using interpolative separable density fitting, Journal of Computational Physics, vol. 351 (December, 2017), pp. 187-202 [doi]
  16. Cao, Y; Lu, J, Lindblad equation and its semiclassical limit of the Anderson-Holstein model, Journal of Mathematical Physics, vol. 58 no. 12 (December, 2017), pp. 122105-122105 [doi]  [abs]
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  27. Lu, J; Zhou, Z, Path integral molecular dynamics with surface hopping for thermal equilibrium sampling of nonadiabatic systems., The Journal of Chemical Physics, vol. 146 no. 15 (April, 2017), pp. 154110 [doi]  [abs]
  28. Watson, AB; Lu, J; Weinstein, MI, Wavepackets in inhomogeneous periodic media: Effective particle-field dynamics and Berry curvature, Journal of Mathematical Physics, vol. 58 no. 2 (February, 2017), pp. 021503-021503 [doi]
  29. Niu, X; Luo, T; Lu, J; Xiang, Y, Dislocation climb models from atomistic scheme to dislocation dynamics, Journal of the Mechanics and Physics of Solids, vol. 99 (February, 2017), pp. 242-258 [doi]
  30. Li, XH; Lu, J, Quasi-nonlocal Coupling of Nonlocal Diffusions, Siam Journal on Numerical Analysis, vol. 55 no. 5 (January, 2017), pp. 2394-2415 [doi]
  31. Lu, J; Yang, H, Preconditioning Orbital Minimization Method for Planewave Discretization, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 254-273 [doi]
  32. Lin, L; Lu, J; Vanden-Eijnden, E, A Mathematical Theory of Optimal Milestoning (with a Detour via Exact Milestoning), Communications on Pure and Applied Mathematics (January, 2017) [doi]  [abs]
  33. Li, Q; Lu, J; Sun, W, Validity and Regularization of Classical Half-Space Equations, Journal of Statistical Physics, vol. 166 no. 2 (January, 2017), pp. 398-433 [doi]
  34. Li, Q; Lu, J, An asymptotic preserving method for transport equations with oscillatory scattering coefficients, Multiscale Modeling & Simulation, vol. 15 no. 4 (January, 2017), pp. 1694-1718 [doi]  [abs]
  35. Gao, Y; Liu, J-G; Lu, J, Weak Solution of a Continuum Model For Vicinal Surface in The Attachment-Detachment-Limited Regime, Siam Journal on Mathematical Analysis, vol. 49 no. 3 (January, 2017), pp. 1705-1731 [doi]  [abs]
  36. Cornelis, B; Yang, H; Goodfriend, A; Ocon, N; Lu, J; Daubechies, I, Removal of Canvas Patterns in Digital Acquisitions of Paintings., Ieee Transactions on Image Processing : a Publication of the Ieee Signal Processing Society, vol. 26 no. 1 (January, 2017), pp. 160-171 [doi]  [abs]

Lu, Yulong

  1. Lu, Y; Stuart, A; Weber, H, Gaussian Approximations for Transition Paths in Brownian Dynamics, Siam Journal on Mathematical Analysis, vol. 49 no. 4 (January, 2017), pp. 3005-3047 [doi]

Ma, Ding

  1. D. Ma, Period polynomial relations of binomial coefficients and binomial realization of formal double zeta space, International Journal of Number Theory, vol. 13 no. 03 (April, 2017), pp. 761-774 [doi]  [abs]

Maggioni, Mauro

  1. Escande, P; Debarnot, V; Maggioni, M; Mangeat, T; Weiss, P, Learning and exploiting physics of degradations, Optics Infobase Conference Papers, vol. Part F105-MATH 2018 (January, 2018), ISBN 9781557528209 [doi]  [abs]
  2. 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), ISBN 9781510617995 [doi]  [abs]
  3. Murphy, JM; Maggioni, M, Unsupervised Clustering and Active Learning of Hyperspectral Images With Nonlinear Diffusion, Ieee Transactions on Geoscience and Remote Sensing (January, 2018) [doi]  [abs]
  4. Little, AV; Maggioni, M; Rosasco, L, Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature, Applied and Computational Harmonic Analysis, vol. 43 no. 3 (November, 2017), pp. 504-567 [doi]
  5. Wang, YG; Maggioni, M; Chen, G, Enhanced detection of chemical plumes in hyperspectral images and movies throughimproved backgroundmodeling, Proceedings of Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, vol. 2015-June (October, 2017), ISBN 9781467390156 [doi]  [abs]
  6. Gerber, S; Maggioni, M, Multiscale strategies for computing optimal transport, Journal of Machine Learning Research, vol. 18 (August, 2017), pp. 1-32  [abs]
  7. Bongini, M; Fornasier, M; Hansen, M; Maggioni, M, Inferring interaction rules from observations of evolutive systems I: The variational approach, Mathematical Models & Methods in Applied Sciences, vol. 27 no. 05 (May, 2017), pp. 909-951 [doi]
  8. Tomita, TM; Maggioni, M; Vogelstein, JT, ROFLMAO: Robust oblique forests with linear MAtrix operations, Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (January, 2017), pp. 498-506, ISBN 9781611974874  [abs]
  9. Crosskey, M; Maggioni, M, ATLAS: A Geometric Approach to Learning High-Dimensional Stochastic Systems Near Manifolds, Multiscale Modeling & Simulation, vol. 15 no. 1 (January, 2017), pp. 110-156 [doi]
  10. E Causevic and R~R Coifman and R Isenhart and A Jacquin and E~R John and M Maggioni and L~S Prichep and F~J Warner, QEEG-based classification with wavelet packets and microstate features for triage applications in the ER (2005)
  11. GL Davis and Mauro Maggioni and FJ Warner and FB Geshwind and AC Coppi and RA DeVerse and RR Coifman, Hyper-spectral Analysis of normal and malignant colon tissue microarray sections using a novel DMD system (2004) (Poster, Optical Imaging NIH workshop, to app. in proc..)
  12. Ronald R Coifman and Mauro Maggioni, Multiresolution Analysis associated to diffusion semigroups: construction and fast algorithms no. YALE/DCS/TR-1289 (2004)

Malen, Greg

  1. Malen, G, Homomorphism complexes and -cores, Discrete Mathematics, vol. 341 no. 9 (September, 2018), pp. 2567-2574 [doi]

Mattingly, Jonathan C.   (search)

  1. Herschlag, G; Ravier, R; Mattingly, JC, Evaluating Partisan Gerrymandering in Wisconsin (September, 2017)  [abs]
  2. Bakhtin, Y; Hurth, T; Lawley, SD; Mattingly, JC, Smooth invariant densities for random switching on the torus, vol. 31 no. 4 (August, 2017), pp. 1331-1350 [doi]  [abs]
  3. Johndrow, JE; Mattingly, JC, Coupling and Decoupling to bound an approximating Markov Chain (July, 2017)  [abs]
  4. Glatt-Holtz, NE; Herzog, DP; Mattingly, JC, Scaling and Saturation in Infinite-Dimensional Control Problems with Applications to Stochastic Partial Differential Equations, Annals of Pde (June, 2017)  [abs]
  5. Glatt-Holtz, N; Mattingly, JC; Richards, G, On Unique Ergodicity in Nonlinear Stochastic Partial Differential Equations, Journal of Statistical Physics, vol. 166 no. 3-4 (February, 2017), pp. 618-649 [1512.04126v1], [doi]  [abs]

Miller, Ezra

  1. 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]

Motta, Francis C.

  1. Motta, FC, Topological Data Analysis: Developments and Applications, in Advances in Nonlinear Geosciences, edited by Tsonis, A (November, 2017), pp. 369-391, Springer, ISBN 3319588958  [abs]
  2. Cho, C-Y; Motta, FC; Kelliher, CM; Deckard, A; Haase, SB, Reconciling conflicting models for global control of cell-cycle transcription., Cell Cycle, vol. 16 no. 20 (October, 2017), pp. 1965-1978 [doi]  [abs]
  3. Burris, CS; Motta, FC; Shipman, PD, An Unoriented Variation on de Bruijn Sequences, Graphs and Combinatorics, vol. 33 no. 4 (July, 2017), pp. 845-858 [doi]

Mukherjee, Sayan

  1. 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]
  2. 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]
  3. 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]
  4. Singleton, KR; Crawford, L; Tsui, E; Manchester, HE; Maertens, O; Liu, X; Liberti, MV; Magpusao, AN; Stein, EM; Tingley, JP; Frederick, DT; Boland, GM; Flaherty, KT; McCall, SJ; Krepler, C; Sproesser, K; Herlyn, M; Adams, DJ; Locasale, JW; Cichowski, K; Mukherjee, S; Wood, KC, Melanoma Therapeutic Strategies that Select against Resistance by Exploiting MYC-Driven Evolutionary Convergence., Cell Reports, vol. 21 no. 10 (December, 2017), pp. 2796-2812 [doi]  [abs]
  5. Darnell, G; Georgiev, S; Mukherjee, S; Engelhardt, BE, Adaptive randomized dimension reduction on massive data, Journal of machine learning research : JMLR, vol. 18 (November, 2017)  [abs]
  6. 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., The Anatomical Record : Advances in Integrative Anatomy and Evolutionary Biology (October, 2017) [doi]  [abs]
  7. Crawford, L; Wood, KC; Zhou, X; Mukherjee, S, Bayesian Approximate Kernel Regression With Variable Selection, Journal of the American Statistical Association (August, 2017), pp. 1-12 [doi]
  8. Bobrowski, O; Mukherjee, S; Taylor, JE, Topological consistency via kernel estimation, Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability, vol. 23 no. 1 (February, 2017), pp. 288-328 [doi]
  9. Tan, Z; Mukherjee, S, Partitioned tensor factorizations for learning mixed membership models, 34th International Conference on Machine Learning, Icml 2017, vol. 7 (January, 2017), pp. 5156-5165, ISBN 9781510855144  [abs]

Nagy, Akos

  1. Nagy, Á, The Berry Connection of the Ginzburg–Landau Vortices, Communications in Mathematical Physics, vol. 350 no. 1 (February, 2017), pp. 105-128 [doi]

Ng, Lenhard L.

  1. Ekholm, T; Ng, L; Shende, V, A complete knot invariant from contact homology, Inventiones Mathematicae, vol. 211 no. 3 (March, 2018), pp. 1149-1200 [doi]  [abs]
  2. Cieliebak, K; Ekholm, T; Latschev, J; Ng, L, Knot contact homology, string topology, and the cord algebra, Journal De L'Ecole Polytechnique Mathematiques, vol. 4 (January, 2017), pp. 661-780 [doi]  [abs]
  3. Ng, L; Rutherford, D; Shende, V; Sivek, S, The cardinality of the augmentation category of a Legendrian link, Mathematical Research Letters, vol. 24 no. 6 (2017), pp. 1845-1874

Nolen, James H.

  1. 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 [doi]  [abs]
  2. Nolen, J; Roquejoffre, J-M; Ryzhik, L, Refined long-time asymptotics for Fisher–KPP fronts, Communications in Contemporary Mathematics (January, 2018), pp. 1850072-1850072, World Scientific Pub Co Pte Lt [doi]  [abs]
  3. Mourrat, J-C; Nolen, J, Scaling limit of the corrector in stochastic homogenization, The Annals of Applied Probability, vol. 27 no. 2 (April, 2017), pp. 944-959, Institute of Mathematical Statistics (IMS), ISSN 1050-5164 [arXiv:1502.07440], [1502.07440], [doi]  [abs]
  4. Nolen, J; Roquejoffre, J-M; Ryzhik, L, Convergence to a single wave in the Fisher-KPP equation, Chinese Annals of Mathematics, Series B, vol. 38 no. 2 (March, 2017), pp. 629-646 [1604.02994], [doi]

Orizaga, Saulo

  1. Glasner, K; Orizaga, S, Multidimensional equilibria and their stability in copolymer–solvent mixtures, Physica D: Nonlinear Phenomena, vol. 373 (June, 2018), pp. 1-12 [doi]  [abs]
  2. Orizaga, S; Riahi, DN, Triad resonant wave interactions in electrically charged jets, Applied Mathematics and Mechanics, vol. 38 no. 8 (August, 2017), pp. 1127-1148 [doi]

Petters, Arlie O.

  1. A. O. Petters and M. C. Werner, Gravitational Lensing and Black Holes (Spring, 2017), Springer, in preparation

Pfister, Henry

  1. Rengaswamy, N; Calderbank, R; Pfister, HD; Kadhe, S, Synthesis of Logical Clifford Operators via Symplectic Geometry, Ieee International Symposium on Information Theory Proceedings, vol. 2018-June (August, 2018), pp. 791-795 [doi]  [abs]
  2. Hager, C; Pfister, HD, Deep Learning of the Nonlinear Schrödinger Equation in Fiber-Optic Communications, Ieee International Symposium on Information Theory Proceedings, vol. 2018-June (August, 2018), pp. 1590-1594 [doi]  [abs]
  3. Santi, E; Hager, C; Pfister, HD, Decoding Reed-Muller Codes Using Minimum- Weight Parity Checks, Ieee International Symposium on Information Theory Proceedings, vol. 2018-June (August, 2018), pp. 1296-1300 [doi]  [abs]
  4. 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. 2018-June (August, 2018), pp. 1754-1758, ISBN 9781538647806 [doi]  [abs]
  5. Hager, C; Pfister, HD, Approaching Miscorrection-Free Performance of Product Codes With Anchor Decoding, Ieee Transactions on Communications, vol. 66 no. 7 (July, 2018), pp. 2797-2808 [doi]
  6. Hager, C; Pfister, HD, Nonlinear interference mitigation via deep neural networks, 2018 Optical Fiber Communications Conference and Exposition, Ofc 2018 Proceedings (June, 2018), pp. 1-3, ISBN 9781943580385  [abs]
  7. Yoo, I; Imani, MF; Sleasman, T; Pfister, HD; Smith, DR, Enhancing Capacity of Spatial Multiplexing Systems Using Reconfigurable Cavity-backed Metasurface Antennas in Clustered MIMO Channels, Ieee Transactions on Communications (January, 2018) [doi]  [abs]
  8. Häger, C; Pfister, HD, Nonlinear Interference Mitigation via Deep Neural Networks, Optical Fiber Communication Conference, vol. Part F84-OFC 2018 (2018), OSA [doi]  [abs]
  9. Charbonneau, P; Li, YC; Pfister, HD; Yaida, S, Cycle-expansion method for the Lyapunov exponent, susceptibility, and higher moments., Physical Review. E, vol. 96 no. 3-1 (September, 2017), pp. 032129 [doi]  [abs]
  10. Hager, C; Pfister, HD, Miscorrection-free Decoding of Staircase Codes, 2017 European Conference on Optical Communication (Ecoc), vol. 2017-September (September, 2017), pp. 1-3, IEEE [doi]  [abs]
  11. Kudekar, S; Kumar, S; Mondelli, M; Pfister, HD; Sasoglu, E; Urbanke, RL, Reed–Muller Codes Achieve Capacity on Erasure Channels, Ieee Transactions on Information Theory, vol. 63 no. 7 (July, 2017), pp. 4298-4316 [doi]
  12. Hager, C; Pfister, HD; Graell i Amat, A; Brannstrom, F, Density Evolution for Deterministic Generalized Product Codes on the Binary Erasure Channel at High Rates, Ieee Transactions on Information Theory (July, 2017) [doi]
  13. Sabag, O; Permuter, HH; Pfister, HD, A Single-Letter Upper Bound on the Feedback Capacity of Unifilar Finite-State Channels, Ieee Transactions on Information Theory, vol. 63 no. 3 (March, 2017), pp. 1392-1409 [doi]  [abs]
  14. Sabag, O; Permuter, HH; Pfister, HD, Single-letter bounds on the feedback capacity of unifilar finite-state channels, 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 (January, 2017), ISBN 9781509021529 [doi]  [abs]
  15. Jian, Y-Y; Pfister, HD; Narayanan, KR, Approaching Capacity at High Rates with Iterative Hard-Decision Decoding, Ieee Transactions on Information Theory (2017), pp. 1-1 [doi]

Pierce, Lillian B.

  1. Pierce, LB; Yung, PL, A polynomial Carleson operator along the paraboloid, Revista Matemática Iberoamericana (2018), European Mathematical Society
  2. Carneiro, E; Madrid, J; Pierce, LB, Endpoint Sobolev and BV continuity for maximal operators, Journal of Functional Analysis, vol. 273 no. 10 (November, 2017), pp. 3262-3294 [doi]
  3. Heath-Brown, DR; Pierce, LB, Averages and moments associated to class numbers of imaginary quadratic fields, Compositio Mathematica, vol. 153 no. 11 (November, 2017), pp. 2287-2309 [doi]
  4. Pierce, LB, The Vinogradov Mean Value Theorem [after Wooley, and Bourgain, Demeter and Guth], Astérisque (July, 2017), Centre National de la Recherche Scientifique  [abs]
  5. Heath-Brown, DR; Pierce, LB, Simultaneous integer values of pairs of quadratic forms, Journal Fur Die Reine Und Angewandte Mathematik, vol. 2017 no. 727 (June, 2017), pp. 85-143 [doi]  [abs]
  6. Pierce, LB; Turnage-Butterbaugh, CL; Wood, MM, An effective Chebotarev density theorem for families of number fields, with an application to $\ell$-torsion in class groups, (Submitted) (2017)  [abs]
  7. Guo, S; Pierce, LB; Roos, J; Yung, P, Polynomial Carleson operators along monomial curves in the plane, Journal of Geometric Analysis (2017), Springer Verlag  [abs]
  8. Ellenberg, J; Pierce, LB; Wood, MM, On ℓ-torsion in class groups of number fields, Algebra & Number Theory, vol. 11 no. 8 (2017), pp. 1739-1778 [doi]  [abs]

Plesser, M. Ronen

  1. Bertolini, M; Plesser, MR, (0,2) hybrid models, Journal of High Energy Physics, vol. 2018 no. 9 (September, 2018) [doi]  [abs]
  2. Jockers, H; Katz, S; Morrison, DR; Plesser, MR, SU(N) Transitions in M-Theory on Calabi–Yau Fourfolds and Background Fluxes, Communications in Mathematical Physics, vol. 351 no. 2 (April, 2017), pp. 837-871 [doi]

Pollack, Aaron

  1. Pollack, A, The spin -function on for Siegel modular forms, Compositio Mathematica, vol. 153 no. 07 (July, 2017), pp. 1391-1432 [doi]
  2. Pollack, A; Shah, S, On the Rankin–Selberg integral of Kohnen and Skoruppa, Mathematical Research Letters, vol. 24 no. 1 (2017), pp. 173-222 [doi]

Randles, Amanda

  1. 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]
  2. 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 (October, 2018) [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) [doi]  [abs]
  4. 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, ISBN 9781538643686 [doi]  [abs]
  5. 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 [doi]  [abs]
  6. Randles, A; Frakes, DH; Leopold, JA, Computational Fluid Dynamics and Additive Manufacturing to Diagnose and Treat Cardiovascular Disease., Trends in Biotechnology, vol. 35 no. 11 (November, 2017), pp. 1049-1061 [doi]  [abs]
  7. Gounley, J; Vardhan, M; Randles, A, A computational framework to assess the influence of changes in vascular geometry on blood flow, PASC 2017 - Proceedings of the Platform for Advanced Scientific Computing Conference (June, 2017), ISBN 9781450350624 [doi]  [abs]
  8. Dabagh, M; Jalali, P; Butler, PJ; Randles, A; Tarbell, JM, Mechanotransmission in endothelial cells subjected to oscillatory and multi-directional shear flow., Journal of the Royal Society, Interface, vol. 14 no. 130 (May, 2017) [doi]  [abs]
  9. Laurence, TA; Ly, S; Fong, E; Shusteff, M; Randles, A; Gounley, J; Draeger, E, Using stroboscopic flow imaging to validate large-scale computational fluid dynamics simulations, High Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management Ii, vol. 10076 (February, 2017), SPIE, ISBN 9781510605930 [doi]  [abs]
  10. Gounley, J; Draeger, EW; Randles, A, Numerical simulation of a compound capsule in a constricted microchannel., Procedia Computer Science, vol. 108 (January, 2017), pp. 175-184 [doi]  [abs]

Reed, Michael C.

  1. Nijhout, HF; Best, JA; Reed, MC, Systems biology of robustness and homeostatic mechanisms., Wiley Interdisciplinary Reviews. Systems Biology and Medicine (October, 2018), pp. e1440 [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, F; Reed, M; Hashemi, P, Voltammetric evidence for discrete serotonin circuits, linked to specific reuptake domains, in the mouse medial prefrontal cortex., Neurochemistry International (July, 2018) [doi]  [abs]
  4. Duncan, W; Best, J; Golubitsky, M; Nijhout, HF; Reed, M, Homeostasis despite instability., Mathematical Biosciences, vol. 300 (March, 2018), pp. 130-137 [doi]  [abs]
  5. Suppiramaniam, V; Bloemer, J; Reed, M; Bhattacharya, S, Neurotransmitter Receptors, in Comprehensive Toxicology: Third Edition, vol. 6-15 (December, 2017), pp. 174-201, ISBN 9780081006122 [doi]  [abs]
  6. Best, J; Nijhout, HF; Samaranayake, S; Hashemi, P; Reed, M, A mathematical model for histamine synthesis, release, and control in varicosities., Theoretical Biology & Medical Modelling, vol. 14 no. 1 (December, 2017), pp. 24 [doi]  [abs]
  7. Reed, M; Best, J; Golubitsky, M; Stewart, I; Nijhout, HF, Analysis of Homeostatic Mechanisms in Biochemical Networks., Bulletin of Mathematical Biology, vol. 79 no. 11 (November, 2017), pp. 2534-2557 [doi]  [abs]
  8. Nijhout, HF; Sadre-Marandi, F; Best, J; Reed, MC, Systems Biology of Phenotypic Robustness and Plasticity., Integrative and Comparative Biology, vol. 57 no. 2 (August, 2017), pp. 171-184 [doi]  [abs]
  9. Reed, MC; Best, J; Nijhout, HF, Mathematical models of neuromodulation and implications for neurology and psychiatry, edited by Erdi, P; Battacharya, B; Cochran, A (2017)
  10. Reed, MC; Lawley, S; Nijhout, HF, Spiracular fluttering increases oxygen uptake (2017)
  11. Reed, MC; Best, J; Nijhout, HF, Mathematical models of neuromodulation and implications for neurology and psychiatry, in Computational Neurology and Psychiatry, edited by Erdi, P; Bhattacharya, B; Cochran, A (2017), SPRINGER
  12. Thanacoody, HKR; Nijhout, HF; Reed, MC; Thomas, S, Mathematical modeling of the effect of different intravenous acetylcysteine regimens on hepatic glutathione regeneration and hepatocyte death following simulated acetaminophen overdose, Clinical Toxicology (Philadelphia, Pa.), vol. 55 no. 7 (2017), pp. 753-753
  13. Thanacoody, HKR; Nijhout, HF; Reed, MC; Thomas, S, Mathematical modeling of the effect of late administration of a novel acetylcysteine regimen based on the SNAP trial on hepatic glutathione regeneration and hepatocyte death following simulated acetaminophen overdose, Clinical Toxicology (Philadelphia, Pa.), vol. 55 no. 7 (2017), pp. 753-754

Robles, Colleen M

  1. Robles, C, Characterization of Calabi–Yau variations of Hodge structure over tube domains by characteristic forms, Mathematische Annalen (September, 2017), pp. 1-25 [doi]  [abs]
  2. Kerr, M; Robles, C, Variations of Hodge structure and orbits in flag varieties, Advances in Mathematics, vol. 315 (July, 2017), pp. 27-87 [doi]  [abs]
  3. Kerr, M; Robles, C, Classification of smooth horizontal Schubert varieties, European Journal of Mathematics, vol. 3 no. 2 (June, 2017), pp. 289-310 [doi]

Rudin, Cynthia D.

  1. 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 [doi]  [abs]
  2. 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 [doi]  [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. 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]
  5. Struck, AF; Ustun, B; Ruiz, AR; Lee, JW; LaRoche, SM; Hirsch, LJ; Gilmore, EJ; Vlachy, J; Haider, HA; Rudin, C; Westover, MB, Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients., Jama Neurology, vol. 74 no. 12 (December, 2017), pp. 1419-1424 [doi]  [abs]
  6. Ustun, B; Rudin, C, Optimized risk scores, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F129685 (August, 2017), pp. 1125-1134, ISBN 9781450348874 [doi]  [abs]
  7. Angelino, E; Larus-Stone, N; Alabi, D; Seltzer, M; Rudin, C, Learning certifiably optimal rule lists, Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, vol. Part F129685 (August, 2017), pp. 35-44, ISBN 9781450348874 [doi]  [abs]
  8. Wang, T; Rudin, C; Doshi-Velez, F; Liu, Y; Klampfl, E; MacNeille, P, A Bayesian framework for learning rule sets for interpretable classification, Journal of Machine Learning Research, vol. 18 (August, 2017), pp. 1-37  [abs]
  9. Letham, B; Letham, PA; Rudin, C; Browne, EP, Erratum: "Prediction uncertainty and optimal experimental design for learning dynamical systems" [Chaos 26, 063110 (2016)]., Chaos (Woodbury, N.Y.), vol. 27 no. 6 (June, 2017), pp. 069901 [doi]
  10. Zeng, J; Ustun, B; Rudin, C, Interpretable classification models for recidivism prediction, Journal of the Royal Statistical Society: Series a (Statistics in Society), vol. 180 no. 3 (June, 2017), pp. 689-722 [doi]
  11. Ustun, B; Adler, LA; Rudin, C; Faraone, SV; Spencer, TJ; Berglund, P; Gruber, MJ; Kessler, RC, The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5., Jama Psychiatry, vol. 74 no. 5 (May, 2017), pp. 520-526 [doi]  [abs]
  12. Wang, T; Rudin, C; Velez-Doshi, F; Liu, Y; Klampfl, E; Macneille, P, Bayesian rule sets for interpretable classification, Proceedings Ieee International Conference on Data Mining, Icdm (January, 2017), pp. 1269-1274, ISBN 9781509054725 [doi]  [abs]
  13. Yang, H; Rudin, C; Seltzer, M, Scalable Bayesian rule lists, 34th International Conference on Machine Learning, Icml 2017, vol. 8 (January, 2017), pp. 5971-5980, ISBN 9781510855144  [abs]

Ryser, Marc D.

  1. 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]
  2. 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 (October, 2018) [doi]  [abs]
  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. 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]
  5. 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]
  6. Ryser, MD; Weaver, DL; Marks, JR; Hyslop, T; Hwang, ES, Quantifying the natural history and overtreatment rate of ductal carcinoma in situ, Cancer Research, vol. 78 no. 4 (February, 2018)
  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 (January, 2018), pp. 962280217754232 [doi]  [abs]
  8. Grimm, LJ; Ryser, MD; Partridge, AH; Thompson, AM; Thomas, JS; Wesseling, J; Hwang, ES, Surgical Upstaging Rates for Vacuum Assisted Biopsy Proven DCIS: Implications for Active Surveillance Trials., Annals of Surgical Oncology, vol. 24 no. 12 (November, 2017), pp. 3534-3540 [doi]  [abs]
  9. Cao, Y; Feng, Y; Ryser, MD; Zhu, K; Herschlag, G; Cao, C; Marusak, K; Zauscher, S; You, L, Programmable assembly of pressure sensors using pattern-forming bacteria., Nature Biotechnology, vol. 35 no. 11 (November, 2017), pp. 1087-1093 [doi]  [abs]
  10. Ryser, MD; Rositch, A; Gravitt, PE, Modeling of US Human Papillomavirus (HPV) Seroprevalence by Age and Sexual Behavior Indicates an Increasing Trend of HPV Infection Following the Sexual Revolution., The Journal of Infectious Diseases, vol. 216 no. 5 (September, 2017), pp. 604-611 [doi]  [abs]
  11. Ryser, MD; Gravitt, PE; Myers, ER, Mechanistic mathematical models: An underused platform for HPV research., Papillomavirus research, vol. 3 (June, 2017), pp. 46-49 [doi]  [abs]
  12. Storey, K; Ryser, MD; Leder, K; Foo, J, Spatial Measures of Genetic Heterogeneity During Carcinogenesis., Bulletin of Mathematical Biology, vol. 79 no. 2 (February, 2017), pp. 237-276 [doi]  [abs]

Saper, Leslie

  1. Saper, L, ℒ-modules and micro-support, to appear in Annals of Mathematics (2018)
  2. Saper, L, Perverse sheaves and the reductive Borel-Serre compactification, in Hodge Theory and L²-analysis, edited by Ji, L, vol. 39 (2017), pp. 555-581, International Press  [abs]

Sapiro, Guillermo

  1. 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]
  2. 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 (October, 2018) [doi]  [abs]
  3. 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 [doi]  [abs]
  4. 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, ISBN 9781538646588 [doi]  [abs]
  5. 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, ISBN 9781538646588 [doi]  [abs]
  6. 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) [doi]  [abs]
  7. 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]
  8. 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 [doi]  [abs]
  9. 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 (March, 2018), pp. 1362361318766247 [doi]  [abs]
  10. 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]
  11. 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]
  12. 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, ISBN 9781538610343 [doi]  [abs]
  13. 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, ISBN 9781538610343 [doi]  [abs]
  14. 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]
  15. 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]
  16. 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), ISBN 9781510614550 [doi]  [abs]
  17. 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]
  18. 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]
  19. 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 [doi]  [abs]
  20. 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]
  21. Lezama, J; Qiu, Q; Sapiro, G, Not afraid of the dark: NIR-VIS face recognition via cross-spectral hallucination and low-rank embedding, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 6807-6816 [doi]  [abs]
  22. Ye, Q; Zhang, T; Ke, W; Qiu, Q; Chen, J; Sapiro, G; Zhang, B, Self-learning scene-specific pedestrian detectors using a progressive latent model, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 2057-2066 [doi]  [abs]
  23. Su, S; Delbracio, M; Wang, J; Sapiro, G; Heidrich, W; Wang, O, Deep video deblurring for hand-held cameras, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 237-246, ISBN 9781538604571 [doi]  [abs]
  24. Tepper, M; Sapiro, G, Nonnegative matrix underapproximation for robust multiple model fitting, Proceedings 30th Ieee Conference on Computer Vision and Pattern Recognition, Cvpr 2017, vol. 2017-January (November, 2017), pp. 655-663, ISBN 9781538604571 [doi]  [abs]
  25. Pisharady, PK; Sotiropoulos, SN; Sapiro, G; Lenglet, C, A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI., Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 10433 (September, 2017), pp. 602-610, ISBN 9783319661810 [doi]  [abs]
  26. Sokolić, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Generalization error of deep neural networks: Role of classification margin and data structure, 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017 (September, 2017), pp. 147-151, ISBN 9781538615652 [doi]  [abs]
  27. Sokolic, J; Giryes, R; Sapiro, G; Rodrigues, MRD, Robust Large Margin Deep Neural Networks, Ieee Transactions on Signal Processing, vol. 65 no. 16 (August, 2017), pp. 4265-4280 [doi]
  28. Simhal, AK; Aguerrebere, C; Collman, F; Vogelstein, JT; Micheva, KD; Weinberg, RJ; Smith, SJ; Sapiro, G, Probabilistic fluorescence-based synapse detection., Plos Computational Biology, vol. 13 no. 4 (April, 2017), pp. e1005493 [doi]  [abs]
  29. Campbell, K; Carpenter, KLH; Espinosa, S; Hashemi, J; Qiu, Q; Tepper, M; Calderbank, R; Sapiro, G; Egger, HL; Baker, JP; Dawson, G, Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism., The Journal of Pediatrics, vol. 183 (April, 2017), pp. 133-139.e1 [doi]  [abs]
  30. Gunalan, K; Chaturvedi, A; Howell, B; Duchin, Y; Lempka, SF; Patriat, R; Sapiro, G; Harel, N; McIntyre, CC, Creating and parameterizing patient-specific deep brain stimulation pathway-activation models using the hyperdirect pathway as an example., Plos One, vol. 12 no. 4 (January, 2017), pp. e0176132 [doi]  [abs]

Smith, David A.

  1. Fey, JT; Smith, DA, Algebra as Part of an Integrated High School Curriculum, in And the Rest is Just Algebra, edited by Stewart, S (October 6, 2016), pp. 119-129, Springer International Publishing, ISBN 9783319450537 [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 (January, 2018), WILEY [doi]  [abs]
  2. Sober, B; Levin, D, Computer aided restoration of handwritten character strokes, Computer Aided Design, vol. 89 (August, 2017), pp. 12-24 [doi]
  3. Shaus, A; Faigenbaum-Golovin, S; Sober, B; Turkel, E, Potential Contrast – A New Image Quality Measure, Electronic Imaging, vol. 2017 no. 12 (January, 2017), pp. 52-58 [doi]
  4. Shaus, A; Sober, B; Turkel, E; Piasetzky, E, Beyond the ground truth: Alternative quality measures of document binarizations, Proceedings of International Conference on Frontiers in Handwriting Recognition, Icfhr (January, 2017), pp. 495-500, ISBN 9781509009817 [doi]  [abs]
  5. Faigenbaum-Golovin, S; Mendel-Geberovich, A; Shaus, A; Sober, B; Cordonsky, M; Levin, D; Moinester, M; Sass, B; Turkel, E; Piasetzky, E; Finkelstein, I, Multispectral imaging reveals biblical-period inscription unnoticed for half a century., Plos One, vol. 12 no. 6 (January, 2017), pp. e0178400 [doi]  [abs]
  6. Anat Mendel-Geberovich, ; Arie Shaus, ; Shira Faigenbaum-Golovin, ; Barak Sober, ; Michael Cordonsky, ; Eli Piasetzky, ; Israel Finkelstein,, A Brand New Old Inscription: Arad Ostracon 16 Rediscovered via Multispectral Imaging, Bulletin of the American Schools of Oriental Research no. 378 (2017), pp. 113-113 [doi]

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]
  2. "Nonlinear Harmonic Forms and Indefinite Bochner Formulas " in Hodge Theory and L^2-Analysis, vol. 39 (2017), Higher Education Press

Tarokh, Vahid

  1. 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]
  2. 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, ISBN 9781538646588 [doi]  [abs]
  3. 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 [doi]  [abs]
  4. 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 [doi]  [abs]
  5. 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 [doi]  [abs]
  6. 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 [doi]  [abs]
  7. 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 [doi]
  8. 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 [doi]
  9. Soloveychik, I; Xiang, Y; Tarokh, V, Pseudo-Wigner Matrices, Ieee Transactions on Information Theory, vol. 64 no. 4 (April, 2018), pp. 3170-3178 [doi]
  10. Soloveychik, I; Xiang, Y; Tarokh, V, Symmetric Pseudo-Random Matrices, Ieee Transactions on Information Theory, vol. 64 no. 4 (April, 2018), pp. 3179-3196 [doi]
  11. 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, ISBN 9781509059904 [doi]  [abs]
  12. 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, ISBN 9781509059904 [doi]  [abs]
  13. 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, ISBN 9781509059904 [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, ISBN 9781509030972 [doi]  [abs]
  15. 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 [doi]  [abs]
  16. 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 [doi]  [abs]
  17. Enyioha, C; Magnusson, 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 [doi]
  18. Ding, J; Zhou, J; Tarokh, V, Asymptotically Optimal Prediction for Time-Varying Data Generating Processes, Ieee Transactions on Information Theory (January, 2018) [doi]  [abs]
  19. Magnusson, S; Enyioha, C; Li, N; Fichione, C; Tarokh, V, Communication Complexity of Dual Decomposition Methods for Distributed Resource Allocation Optimization, Ieee Journal of Selected Topics in Signal Processing (2018), pp. 1-1 [doi]
  20. 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
  21. Banerjee, T; Whipps, GT; Gurram, P; Tarokh, V, Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data., Corr, vol. abs/1807.06945 (2018)
  22. 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
  23. 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
  24. 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
  25. 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
  26. Soloveychik, I; Tarokh, V, Stationary Geometric Graphical Model Selection., Corr, vol. abs/1806.03571 (2018)
  27. 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
  28. Han, Q; Ding, J; Airoldi, EM; Tarokh, V, SLANTS: Sequential Adaptive Nonlinear Modeling of Time Series, Ieee Transactions on Signal Processing, vol. 65 no. 19 (October, 2017), pp. 4994-5005 [doi]
  29. Boyer, R; Babadi, B; Kalouptsidis, N; Tarokh, V, Asymptotic Achievability of the Cramer-Rao Bound for Noisy Compressive Sampling (vol 57, pg 1233, 2009), Ieee Transactions on Signal Processing, vol. 65 no. 18 (September, 2017), pp. 4973-4974, IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC [doi]
  30. Ding, J; Xiang, Y; Shen, L; Tarokh, V, Multiple Change Point Analysis: Fast Implementation and Strong Consistency, Ieee Transactions on Signal Processing, vol. 65 no. 17 (September, 2017), pp. 4495-4510 [doi]
  31. Shahrampour, S; Noshad, M; Tarokh, V, On Sequential Elimination Algorithms for Best-Arm Identification in Multi-Armed Bandits, Ieee Transactions on Signal Processing, vol. 65 no. 16 (August, 2017), pp. 4281-4292 [doi]
  32. Deng, Z; Ding, J; Heal, K; Tarokh, V, The number of independent sets in hexagonal graphs, Ieee International Symposium on Information Theory Proceedings (August, 2017), pp. 2910-2914, ISBN 9781509040964 [doi]  [abs]
  33. Soloveychik, I; Xiang, Y; Tarokh, V, Pseudo-wigner matrices from dual BCH codes, Ieee International Symposium on Information Theory Proceedings (August, 2017), pp. 1381-1385, ISBN 9781509040964 [doi]  [abs]
  34. Jeong, S; Kang, J; Pahlavan, K; Tarokh, V, Fundamental Limits of TOA/DOA and Inertial Measurement Unit-Based Wireless Capsule Endoscopy Hybrid Localization, International Journal of Wireless Information Networks, vol. 24 no. 2 (June, 2017), pp. 169-179 [doi]
  35. Farhadi, H; Xiang, Y; Jeong, S; Li, X; Guo, N; Sepulcre, J; Tarokh, V; Li, Q, Inferring the causality network of Abeta and Tau accumulation in the aging brain: a statistical inference approach, Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine, vol. 58 (May, 2017), pp. 2 pages, SOC NUCLEAR MEDICINE INC
  36. Wei, L; Sarwate, AD; Corander, J; Hero, A; Tarokh, V, Analysis of a privacy-preserving PCA algorithm using random matrix theory, 2016 Ieee Global Conference on Signal and Information Processing, Globalsip 2016 Proceedings (April, 2017), pp. 1335-1339, ISBN 9781509045457 [doi]  [abs]
  37. Enyioha, C; Magnússon, S; Heal, K; Li, N; Fischione, C; Tarokh, V, Robustness analysis for an online decentralized descent power allocation algorithm, 2016 Information Theory and Applications Workshop, Ita 2016 (March, 2017), ISBN 9781509025299 [doi]  [abs]
  38. Kuiper, PK; Kolitz, SE; Tarokh, V, Base camp quality of life standardization and improvement, Proceedings International Carnahan Conference on Security Technology (January, 2017), ISBN 9781509010707 [doi]  [abs]
  39. Kurien, BG; Ashcom, JB; Shah, VN; Rachlin, Y; Tarokh, V, Robust interferometric imaging via prior-less phase recovery: redundant spacing calibration with generalized-closure phases, Monthly Notices of the Royal Astronomical Society, vol. 464 no. 2 (January, 2017), pp. 2356-2376 [doi]
  40. Jeong, S; Li, X; Yang, J; Li, Q; Tarokh, V, Dictionary learning and sparse coding-based denoising for high-resolution task functional connectivity MRI analysis, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10541 LNCS (January, 2017), pp. 45-52, ISBN 9783319673882 [doi]  [abs]
  41. Beirami, A; Razaviyayn, M; Shahrampour, S; Tarokh, V, On optimal generalizability in parametric learning, Advances in Neural Information Processing Systems, vol. 2017-December (January, 2017), pp. 3456-3466  [abs]
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Tralie, Christopher

  1. Tralie, CJ; Smith, A; Borggren, N; Hineman, J; Bendich, P; Zulch, P; Harer, J, Geometric Cross-Modal Comparison of Heterogeneous Sensor Data, Proceedings of the 39th Ieee Aerospace Conference (March, 2018)  [abs]
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Turnage-Butterbaugh, Caroline

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Vafaee, Faramarz

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Venakides, Stephanos

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Viel, Shira

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Wang, Jiuya

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Watson, Alexander

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Witelski, Thomas P.   (search)

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Wong, Jeffrey T

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Wu, Hau-Tieng

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  22. Sheu, Y-L; Hsu, L-Y; Chou, P-T; Wu, H-T, Entropy-based time-varying window width selection for nonlinear-type time–frequency analysis, International Journal of Data Science and Analytics, vol. 3 no. 4 (June, 2017), pp. 231-245 [doi]
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  25. Coifman, RR; Steinerberger, S; Wu, HT, Carrier frequencies, holomorphy. And unwinding, Siam Journal on Mathematical Analysis, vol. 49 no. 6 (January, 2017), pp. 4838-4864 [doi]  [abs]
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  27. Frasch, MG; Boylan, GB; Wu, H-T; Devane, D, Commentary: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial., Frontiers in Physiology, vol. 8 (January, 2017), pp. 721 [doi]
  28. Cicone, A; Wu, H-T, How Nonlinear-Type Time-Frequency Analysis Can Help in Sensing Instantaneous Heart Rate and Instantaneous Respiratory Rate from Photoplethysmography in a Reliable Way., Frontiers in Physiology, vol. 8 (January, 2017), pp. 701 [doi]  [abs]
  29. Liu, W-T; Wu, H-T; Juang, J-N; Wisniewski, A; Lee, H-C; Wu, D; Lo, Y-L, Prediction of the severity of obstructive sleep apnea by anthropometric features via support vector machine., Plos One, vol. 12 no. 5 (January, 2017), pp. e0176991 [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 [doi]  [abs]

Yang, Haizhao   (search)

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Zhou, Zhennan

  1. Liu, J-G; Ma, Z; Zhou, Z, Explicit and Implicit TVD Schemes for Conservation Laws with Caputo Derivatives, Journal of Scientific Computing, vol. 72 no. 1 (July, 2017), pp. 291-313 [doi]  [abs]
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Zhu, Wei

  1. Zhu, W; Qiu, Q; Huang, J; Calderbank, AR; Sapiro, G; Daubechies, I, LDMNet: Low Dimensional Manifold Regularized Neural Networks., CoRR, vol. abs/1711.06246 (2017)

 

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