People

High school students
Anna Zhang, Stuyvesant High School 2020–2021
Undergraduate students
Krisha Garg, mathematics, New York University 2025–
David Huang, mathematics, New York University 2024–2025
Horace Sun, mathematics, New York University 2024–2025
Ibrohim Nosirov, mathematics, New York University (co-mentored with C. Musco) Summer 2023
James Butler, mathematics, University of Chicago (co-mentored with D. Abbot) 2020–2021
Runxin Ni, mathematics, New York University (co-mentored with D. Abbot) Summer 2020
Charlie Marshall, mathematics, University of Chicago (co-mentored with D. Abbot) 2019–2020
Douglas Dow, mathematics, University of Chicago 2019–2020
Bradley Stadie, mathematics, University of Chicago 2013–2014
Master's students
Bixing Qiao, mathematics, New York University 2019–2020
Eileen Li, statistics, University of Chicago (co-mentored with A. Dinner) 2016–2017
Doctoral students
Weizhou Wang, chemistry, University of Chicago (co-mentored with A. Dinner) 2024–
Kaiwen Zhang, mathematics, New York University 2024–
Ellen Persson, mathematics, New York University (co-mentored with G. Stadler) 2024–
Xuanxi Zhang, mathematics, New York University 2023–
Natalia Hajłasz, mathematics, New York University 2022–
Xiaoou Cheng, mathematics, New York University 2020–
Huan Zhang, mathematics, New York University 2019–2025
Chatipat Lorpaiboon, chemistry, University of Chicago (co-mentored with A. Dinner) 2018–2025
John Strahan, chemistry, University of Chicago (co-mentored with A. Dinner) 2018–2024
Anya Katsevich, mathematics, New York University 2017–2022
Justin Finkel, applied math, University of Chicago (co-mentored with D. Abbot) 2017–2022
Adam Antoszewski, chemistry, University of Chicago (co-mentored with A. Dinner) 2017–2022
Sam Greene, chemistry, Columbia (co-mentored with T. Berkelbach) 2017–2022
Robert Webber, mathematics, New York University 2015–2021
Bodhi Vani, chemistry, University of Chicago (co-mentored with A. Dinner) 2015–2021
Erik Thiede, chemistry, University of Chicago (co-mentored with A. Dinner) 2013–2019
David Plotkin, geoscience, University of Chicago (co-mentored with D. Abbot) 2012–2018
Jeremy Tempkin, chemistry, University of Chicago (co-mentored with A. Dinner) 2012–2017
Instructors and postdoctoral scholars
Yifan Chen, mathematics, New York University 2023–
Michael Lindsey, mathematics, New York University 2019–2022
Brian Van Koten, applied math, University of Chicago 2014–2018
Charles Matthews, applied math, University of Chicago 2014–2018
Seyit Kale, chemistry, University of Chicago 2012–2015

Editorial work

Current
Communications on Pure and Applied Mathematics Communications on Pure and Applied Mathematics
Annals of Applied Probability Annals of Applied Probability
Past
SIAM/ASA Journal on Uncertainty Quantification SIAM/ASA Journal on Uncertainty Quantification
Stochastics and Partial Differential Equations Stochastics and PDEs: Analysis and Computations

Publications

Sorted roughly by area. Preprints can be found on arXiv.

Monte Carlo, Markov chains, and related
Convergence of unadjusted Langevin in high dimensions: delocalization of bias Y. Chen, X. Cheng, J. Niles-Weed, and J. Weare · Communications in Pure and Applied Mathematics, 2026, e70032
Sampling parameters of ordinary differential equations with constrained Langevin dynamics C. Chi, J. Weare, and A.R. Dinner · SIAM Journal on Uncertainty Quantification, 13(3) 2025, 1374–1405
Ensemble Markov chain Monte Carlo with teleporting walkers M. Lindsey, J. Weare, and A. Zhang · SIAM Journal on Uncertainty Quantification, 10(3) 2022, 860–885
A metric on directed graphs and Markov chains based on hitting probabilities Z.M. Boyd, N. Fraiman, J. Marzuola, P.J. Mucha, B. Osting, and J. Weare · SIAM Journal on Mathematics of Data Science, 3(2) 2021, 467–493
Stratification as a general variance reduction method for Markov chain Monte Carlo A.R. Dinner, E.H. Thiede, B. Van Koten, and J. Weare · SIAM/ASA Journal on Uncertainty Quantification, 8(3) 2020, 1139–1188
Langevin Markov chain Monte Carlo with stochastic gradients C. Matthews and J. Weare · 2018
Umbrella sampling: a powerful method to sample tails of distributions C. Matthews, J. Weare, A. Kravtsov, and E. Jennings · Monthly Notices of the Royal Astronomical Society, 480(3) 2018, 4069–4079
Ensemble preconditioning for Markov chain Monte Carlo simulation B. Leimkuhler, C. Matthews, and J. Weare · Statistics and Computing, 28(2) 2017, 277–290
Eigenvector method for umbrella sampling enables error analysis E.H. Thiede, B. Van Koten, J. Weare, and A.R. Dinner · Journal of Chemical Physics, 145(8) 2016, 084115
Sharp entrywise perturbation bounds for Markov chains E.H. Thiede, B. Van Koten, and J. Weare · SIAM Journal on Matrix Analysis and Applications, 36(3) 2015, 917–941
The Brownian fan M. Hairer and J. Weare · Communications in Pure and Applied Mathematics, 68(1) 2015, 1–60
Improved diffusion Monte Carlo M. Hairer and J. Weare · Communications in Pure and Applied Mathematics, 67(12) 2014, 1995–2021
On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method B. Roux and J. Weare · Journal of Chemical Physics, 138(8) 2013, 084107
An affine-invariant sampler for exoplanet fitting and discovery in radial velocity data F. Hou, J. Goodman, D.W. Hogg, J. Weare, and C. Schwab · The Astrophysical Journal, 745 2012, 198
Ensemble samplers with affine invariance J. Goodman and J. Weare · Communications in Applied Mathematics and Computational Science, 5 2010, 65–80
Efficient Monte Carlo sampling by parallel marginalization J. Weare · Proceedings of the National Academy of Sciences, 104(31) 2007, 12657–12662
Multiscale analysis and simulation
Mercury's chaotic secular evolution as a subdiffusive process D.S. Abbot, R.J. Webber, D.M. Hernandez, S. Hadden, and J. Weare · The Astrophysical Journal, 967(2) 2024, 121
A Kinetic Monte Carlo approach for simulating cascading transmission line failure J. Roth, D.A. Barajas-Solano, P. Stinis, J. Weare, and M. Anitescu · SIAM Multiscale Modeling and Simulation, 19(1) 2021, 208–241
Multiple time-step dual-Hamiltonian hybrid molecular dynamics Monte Carlo canonical propagation algorithm Y. Chen, S. Kale, J. Weare, A.R. Dinner, and B. Roux · Journal of Chemical Theory and Computation, 12(4) 2016, 1449–1458
Finding chemical reaction paths with a multilevel preconditioning protocol S. Kale, O. Sode, J. Weare, and A.R. Dinner · Journal of Chemical Theory and Computation, 10(12) 2014, 5467–5475
Using multiscale preconditioning to accelerate the convergence of iterative molecular calculations J. Tempkin, B. Qi, M. Saunders, B. Roux, A.R. Dinner, and J. Weare · Journal of Chemical Physics, 140(18) 2014, 184114
Nucleotide regulation of the structure and dynamics of G-actin M. Saunders, J. Tempkin, J. Weare, A.R. Dinner, B. Roux, and G. Voth · Biophysical Journal, 106(8) 2014, 1710–1720
The relaxation of a family of broken bond crystal surface models J. Marzuola and J. Weare · Physical Review E, 88 2013, 032403
The theory of ultra-coarse-graining. 1. General principles J. Dama, A. Sinitskiy, M. McCullagh, J. Weare, B. Roux, A.R. Dinner, and G. Voth · Journal of Chemical Theory and Computation, 9(5) 2013, 2466–2480
Minimizing memory as an objective for coarse-graining N. Guttenberg, J. Dama, M. Saunders, G. Voth, J. Weare, and A.R. Dinner · Journal of Chemical Physics, 138(9) 2013, 094111
Extending molecular simulation time scales: Parallel-in-time integration for high-level quantum chemistry and complex force representations E. Bylaska, J. Weare, and J.H. Weare · Journal of Chemical Physics, 139 2013, 074114
The evolution of a crystal surface: analysis of a 1D step train connecting two facets in the ADL regime H. Al Hajj Shehadeh, R.V. Kohn, and J. Weare · Physica D, 240 2011, 1771–1784
Variance reduction for particle filters of systems with time-scale separation D. Givon, P. Stinis, and J. Weare · IEEE Transactions on Signal Processing, 57(2) 2009, 424–435
Randomized numerical linear algebra
Randomly sparsified Richardson iteration: A dimension-independent sparse linear solver J. Weare and R.J. Webber · Communications in Pure and Applied Mathematics, 79 2026, 89–122
Full configuration interaction excited-state energies in large active spaces from subspace iteration with repeated random sparsification S.M. Greene, R.J. Webber, J.E.T. Smith, J. Weare, and T.C. Berkelbach · Journal of Chemical Theory and Computation, 18(12) 2022, 7218–7232
Approximating matrix eigenvalues by subspace iteration with repeated random sparsification S.M. Greene, R.J. Webber, T.C. Berkelbach, and J. Weare · SIAM Journal on Scientific Computing, 44(5) 2022, A3067–A3097
Improved fast randomized iteration approach to full configuration interaction S.M. Greene, R.J. Webber, J. Weare, and T.C. Berkelbach · Journal of Chemical Theory and Computation, 16(9) 2020, 5572–5585
Beyond walkers in stochastic quantum chemistry: reducing error using Fast Randomized Iteration S.M. Greene, R.J. Webber, J. Weare, and T.C. Berkelbach · Journal of Chemical Theory and Computation, 15(9) 2019, 4834–4850
Fast randomized iteration: diffusion Monte Carlo through the lens of numerical linear algebra L.H. Lim and J. Weare · SIAM Review: Research Spotlight, 59(3) 2017, 547–587
Rare event analysis and simulation
BAD-NEUS: Rapidly converging trajectory stratification J. Strahan, C. Lorpaiboon, J. Weare, and A.R. Dinner · Journal of Chemical Physics, 161 2024, 084109
Revealing the statistics of extreme events hidden in short weather forecast data J. Finkel, E.P. Gerber, D.S. Abbot, and J. Weare · AGU Advances, 4 2023, e2023AV000881 Featured as an Editor's Highlight
Simple physics and integrators accurately reproduce Mercury instability statistics D.S. Abbot, D.M. Hernandez, S. Hadden, R.J. Webber, G.P. Afentakis, and J. Weare · The Astrophysical Journal, 944(2) 2023, 190
Data-driven transition path analysis yields a statistical understanding of sudden stratospheric warming events in an idealized model J. Finkel, R.J. Webber, E.P. Gerber, D.S. Abbot, and J. Weare · Journal of the Atmospheric Sciences, 80 2023, 519–534
Augmented transition path theory for sequences of events C. Lorpaiboon, J. Weare, and A.R. Dinner · Journal of Chemical Physics, 157(9) 2022, 094115
Computing transition path theory quantities with trajectory stratification B.P. Vani, J. Weare, and A.R. Dinner · Journal of Chemical Physics, 157(3) 2022, 034106
Rare event sampling improves Mercury instability statistics D.S. Abbot, R.J. Webber, S. Hadden, D. Seligman, and J. Weare · The Astrophysical Journal, 923(2) 2021, 236
Insulin dissociates by diverse mechanisms of coupled unfolding and unbinding A. Antoszewski, C.-J. Feng, B.P. Vani, E.H. Thiede, L. Hong, J. Weare, A. Tokmakoff, and A.R. Dinner · Journal of Physical Chemistry B, 124(27) 2020, 5571–5587
Path properties of atmospheric transitions: illustration with a low-order sudden stratospheric warming model J. Finkel, D.S. Abbot, and J. Weare · Journal of Atmospheric Sciences, 77(7) 2020, 2327–2347
Practical rare event simulation for extreme mesoscale weather R.J. Webber, D.A. Plotkin, M.E. O'Neill, D.S. Abbot, and J. Weare · Chaos, 29 2019, 053109 Featured in SIAM News
Maximizing simulated tropical cyclone intensity with action minimization D.A. Plotkin, R.J. Webber, M.E. O'Neill, J. Weare, and D.S. Abbot · Journal of Advances in Modeling Earth Systems, 11(4) 2019, 863–891
Trajectory stratification of stochastic dynamics A.R. Dinner, J.C. Mattingly, J. Tempkin, B. Van Koten, and J. Weare · SIAM Review: Research Spotlight, 60(4) 2018, 909–938
Simulating the stochastic dynamics and cascade failure of power networks C. Matthews, B. Stadie, J. Weare, M. Anitescu, and C. Demarco · 2017
Data assimilation in the low noise regime with applications to the Kuroshio E. Vanden-Eijnden and J. Weare · Monthly Weather Review, 141 2013, 1822–1841
Steered transition path sampling N. Guttenberg, A.R. Dinner, and J. Weare · Journal of Chemical Physics, 136 2012, 234103
Rare event simulation for small noise diffusions E. Vanden-Eijnden and J. Weare · Communications in Pure and Applied Mathematics, 65(12) 2012, 1770–1803
Particle filtering with path sampling and an application to a bimodal ocean current model J. Weare · Journal of Computational Physics, 228 2009, 4312–4331
Efficient Conditional Path Sampling of Stochastic Differential Equations J. Weare · Thesis (Ph.D.) — University of California, Berkeley · ProQuest, 2007, ISBN: 978-0-549-73732-2
Statistical and machine learning for science
Improved energies and wavefunction accuracy with Weighted Variational Monte Carlo H. Zhang, R.J. Webber, M. Lindsey, T.C. Berkelbach, and J. Weare · Physical Review Research, accepted
An exact multiple-time-step variational formulation for the committor and the transition rate C. Lorpaiboon, J. Weare, and A.R. Dinner · Journal of Physical Chemistry B, 130(1) 2026, 155–170
Can AI weather models predict out-of-distribution gray swan tropical cyclones? Y.Q. Sun, P. Hassanzadeh, M. Zand, A. Chattopadhyay, J. Weare, and D.S. Abbot · Proceedings of the National Academy of Sciences, 122(21) 2025, e2420914122
Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics Z. Pengmei, C. Lorpaiboon, S.C. Guo, J. Weare, and A.R. Dinner · Journal of Chemical Physics, 162 2025, 044107
The surprising efficiency of temporal difference learning for rare event prediction X. Cheng and J. Weare · NeurIPS 2024
Using explainable AI and transfer learning to understand and predict the maintenance of Atlantic blocking with limited observational data H. Zhang, J. Finkel, D.S. Abbot, E.P. Gerber, and J. Weare · Journal of Geophysical Research: Machine Learning and Computation, 1(4) 2024, e2024JH000243
Improved active learning via dependent leverage score sampling A. Shimizu, X. Cheng, C. Musco, and J. Weare · ICLR 2024 Oral presentation, top 1.2% of submissions
Accurate estimates of dynamical statistics using memory C. Lorpaiboon, S.C. Guo, J. Strahan, J. Weare, and A.R. Dinner · Journal of Chemical Physics, 160 2024, 084108 Featured as an Editor's Pick
AI can identify Solar System instability billions of years in advance D.S. Abbot, J.D. Laurence-Chasen, R.J. Webber, D.M. Hernandez, and J. Weare · Research Notes of the AAS, 8(1) 2024
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction J. Strahan, S.C. Guo, C. Lorpaiboon, A.R. Dinner, and J. Weare · Journal of Chemical Physics, 159 2023, 014110
Predicting rare events using neural networks and short-trajectory data J. Strahan, J. Finkel, A.R. Dinner, and J. Weare · Journal of Computational Physics, 488 2023, 112152
Understanding and eliminating spurious modes in variational Monte Carlo using collective variables H. Zhang, R.J. Webber, M. Lindsey, T.C. Berkelbach, and J. Weare · Physical Review Research, 5 2023, 023101
Learning forecasts of rare stratospheric transitions from short simulations J. Finkel, R.J. Webber, E.P. Gerber, D.S. Abbot, and J. Weare · Monthly Weather Review, 149(11) 2021, 3649–3669
Long-timescale predictions from short-trajectory data: A benchmark analysis of the trp-cage miniprotein J. Strahan, A. Antoszewski, C. Lorpaiboon, B.P. Vani, J. Weare, and A.R. Dinner · Journal of Chemical Theory and Computation, 17(5) 2021, 2948–2963
Error bounds for dynamical spectral estimation R.J. Webber, E.H. Thiede, D. Dow, A.R. Dinner, and J. Weare · SIAM Journal on Mathematics of Data Science, 3(1) 2021, 225–252
Integrated VAC: A robust strategy for identifying eigenfunctions of dynamical operators C. Lorpaiboon, E.H. Thiede, R.J. Webber, J. Weare, and A.R. Dinner · Journal of Physical Chemistry B, 124(42) 2020, 9354–9364
Galerkin approximation of dynamical quantities using trajectory data E.H. Thiede, D. Giannakis, A.R. Dinner, and J. Weare · Journal of Chemical Physics, 150 2019, 241111
Distinguishing meanders of the Kuroshio using machine learning D.A. Plotkin, J. Weare, and D.S. Abbot · Journal of Geophysical Research – Oceans, 119(10) 2014, 6593–6604