Jonathan Weare
Warren Weaver Hall, Room 1103 251 Mercer Street New York, NY 10012-1185 weare [AT] cims [DOT] nyu [DOT] edu ![]() |
Our group is primarily focused on
the design, analysis, and application of stochastic algorithms
and models. Our work draws on tools from probability theory, the
theory of partial differential equations, and numerical
analysis. A significant portion of research in the group is
organized around long term collaborations with applications
experts in areas such as biophysics, computational chemistry,
and weather and climate science.
I am currently an associate
professor of mathematics in the Courant Institute of
Mathematical Sciences at New York University. Previously I was
an associate professor in the statistics department and in the
James Franck Institute at the University of Chicago and, before
that, an assistant professor in the mathematics department
there. Before moving to Chicago I was a Courant Instructor of
mathematics at NYU and a PhD student in mathematics at the
University of California at Berkeley.
Current
research areas
Monte Carlo sampling methods
multiscale analysis and simulation randomized numerical linear algebra rare event analysis and simulation statistical and machine learning for differential equations People
High school students
Anna Zhang,
Stuyvesant High School 2020-
Undergraduate
students
Charlie Marshall,
mathematics, University of Chicago 2019-2020
Douglas Dow, mathematics, University of Chicago 2019-2020 Bradley Stadie, mathematics, University of Chicago 2013-2014 Masters students Bixing Qiao,
mathematics, Courant Institute 2019-2020
Eileen Li, statistics, University of Chicago 2016-2017 Doctoral students Anya Katsevich,
mathematics, Courant Institute 2019-
Chatipat Lorpaiboon, chemistry, University of Chicago (co-mentored with A. Dinner) 2018- John Strahan, chemistry, University of Chicago (co-mentored with A. Dinner) 2018- Adam Antoszewski, chemistry, University of Chicago (co-mentored with A. Dinner) 2017- Justin Finkel, applied math, University of Chicago (co-mentored with D. Abbot) 2017- Sam Greene, chemistry, Columbia (co-mentored with T. Berkelbach) 2017- Robert Webber, mathematics, Courant Institute 2015- Bodhi Vani, chemistry, University of Chicago (co-mentored with A. Dinner) 2015- 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 Michael
Lindsey, mathematics, Courant Institute 2019-
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
Stochastics and Partial Differential Equations: Analysis and Computations SIAM/ASA Journal on Uncertainty Quantification Publications
(sorted roughly by area)
A nearly complete list of publications and preprints can be found on arXiv Monte Carlo, Markov chains, and related A metric on directed graphs and
Markov chains based on hitting probabilities
with Z.M. Boyd, N. Fraiman, J.L. Marzuola, P.J. Mucha, and B. Osting, SIAM Journal on Mathematics of Data Science, accepted Stratification as a general variance reduction method for Monte Carlo with A.R. Dinner, E.H. Thiede, and B. Van Koten, SIAM/ASA Journal on Uncertainty Quantification (JUQ), 8(3) [2020], 1139-1188 Langevin Markov chain Monte Carlo with stochastic gradients with C. Matthews [2018] Umbrella sampling: a powerful method to sample tails of distributions with C. Matthews, A. Kravstov, and E. Jennings, Monthly Notices of the Royal Astronomical Society, 480(3) [2018], 4069-4079 Ensemble preconditioning for Markov chain Monte Carlo simulation with B. Leimkuhler and C. Matthews, Statistics and Computing, 28(2) [2017], 277-290 Eigenvector method for umbrella sampling enables error analysis with E. Thiede, B. Van Koten, and A. Dinner, Journal of Chemical Physics, 145(8) [2016], 084115 Sharp entrywise perturbation bounds for Markov chains with E. Thiede and B. Van Koten, SIAM Journal on Matrix Analysis and Applications, 36(3) [2015], 917-941 On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method with B. Roux, Journal of Chemical Physics, 138(8) [2013], 084107 An affine-invariant sampler for exoplanet fitting and discovery in radial velocity data with F. Hou, J. Goodman, D. Hogg, and C. Schwab, The Astrophysical Journal, 75 [2012], 198 Ensemble samplers with affine invariance with J. Goodman, Communications in Applied Mathematics and Computational Science, 5 [2010], 65-80 Efficient Monte Carlo sampling by parallel marginalization Proceedings of the National Academy of Science, 104(31) [2007], 12657-12662 Multiscale analysis and simulation A Kinetic Monte Carlo
Approach for Simulating Cascading Transmission Line Failure
with J. Roth, D.A. Barajas-Solano, P. Stinis, and Mihai Anitescu, SIAM Multiscale Modeling and Simulation, accepted Multiple time-step dual-Hamiltonian hybrid molecular dynamics Monte Carlo canonical propagation algorithm with Y. Chen, S. Kale, A. Dinner, and B. Roux, Journal of Chemical Theory and Computation, 12(4) [2016], 1449-1458 Finding chemical reaction paths with a multilevel preconditioning protocol with S. Kale, S. Olaseni, and A. Dinner, Journal of Chemical Theory and Simulation, 10(12) [2014], 5467-5475 Using multiscale preconditioning to accelerate the convergence of iterative molecular calculations with J. Tempkin, B. Qui, M. Saunders, B. Roux, and A. Dinner, Journal of Chemical Physics, 140(18) [2014], 184114 Nucleotide regulation of the structure and dynamics of G-actin with M. Saunders, J. Tempkin, A. Dinner, B. Roux, and G. Voth, Biophysical Journal, 106(8) [2014], 1710-1720 The relaxation of a family of broken bond crystal surface models with J. Marzuola, Physical Review E, 88 [2013], 032403 The theory of ultra coarse graining I, general principles with J. Dama, A. Sinitskiy, M. McCullagh, B. Roux, A. Dinner, and G. Voth, Journal of Chemical Theory and Computation, 9(5) [2013], 2466-2480 Minimizing memory as an objective for coarse-graining with N. Guttenberg, J. Dama, M. Saunders, A. Dinner, and G. Voth, 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 with E. Bylaska 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 with H. Al Hajj Shehadeh and R.V. Kohn, Physica D, 240 [2011], 1771-1784 Variance reduction for particle filters of systems with time-scale separation with D. Givon and P. Stinis, IEEE Transactions on Signal Processing, 57(2) [2009], 424-435 Randomized numerical linear algebra Improved Fast Randomized
Iteration approach to Full Configuration Interaction
with S.M. Greene, R.J. Webber, 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 with S.M. Greene, R.J. Webber, 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 with L.H. Lim, SIAM Reviews: Research Spotlight, 59(3) [2017], 547-587 Rare event analysis and simulation Insulin dissociates by
diverse mechanisms of coupled unfolding and unbinding
with A. Antoszewski, C.-J. Feng, B.P. Vani, E.H. Thiede, L. Hong, 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 with J. Finkel, and D.S. Abbot, Journal of Atmospheric Science, 77(7) [2020], 2327–2347 Practical rare event simulation for extreme mesoscale weather with R.J. Webber, D.A. Plotkin, M.E O'Neill, and D.S. Abbot, Chaos, 29 [2019], 053109 Maximizing simulated tropical cyclone intensity with action minimization with D.A. Plotkin, R.J. Webber, M.E O'Neill, and D.S. Abbot, Journal of Advances in Modeling Earth Systems (JAMES), 11(4) [2019], 863-891 Trajectory stratification of stochastic dynamics with A.R. Dinner, J.C. Mattingly, J. Tempkin, and B. Van Koten, SIAM Reviews: Research Spotlight, 60(4) [2018], 909–938 Simulating the stochastic dynamics and cascade failure of power networks with C. Matthews, B. Stadie, M. Anitescu, and C. Demarco, [2017] The Brownian fan with M. Hairer, Communications in Pure and Applied Mathematics, 68(1) [2015], 1-60 Improved diffusion Monte Carlo with M. Hairer, Communications in Pure and Applied Mathematics, 67(12) [2014], 1995-2021 Data assimilation in the low noise regime with applications to the Kuroshio with E. Vanden-Eijnden, Monthly Weather Review, 141 [2013], 1822-1841 Steered transition path sampling with N. Guttenberg and A. Dinner, Journal of Chemical Physics, 136 [2012], 234103 Rare event simulation for small noise diffusions with E. Vanden-Eijnden, 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 Journal of Computational Physics, 228 [2009], 4312-4331 Statistical and machine learning for differential equations Error bounds for dynamical
spectral estimation
with R.J. Webber, E.H. Thiede, D. Dow, and A.R. Dinner, SIAM Journal on Mathematics of Data Science, to appear Integrated VAC: A robust strategy for identifying eigenfunctions of dynamical operators with C. Lorpaiboon, E.H. Thiede, R.J. Webber, and A.R. Dinner, Journal of Physical Chemistry B, 124(42) [2020], 9354-9364 Galerkin approximation of dynamical quantities using trajectory data with E.H. Thiede, D. Giannakis, and A.R. Dinner, Journal of Chemical Physics, 150 [2019], 24111 Distinguishing meanders of the Kuroshio using machine learning with D. Plotkin and D. Abbot, Journal of Geophysical Research - Oceans, 119(10) [2014], 6593-6604 Software
FRIES https://github.com/sgreene8/FRIES S.M. Greene (I am not an author) C++ implementations of various methods within the Fast Randomized Iteration (FRI) framework for performing Full Configuration Interaction calculations on molecular systems and the Hubbard model. pyEDGAR https://github.com/ehthiede/pyEDGAR E.H. Thiede (I am not an author) Python implementation of Dynamic Galerkin Approximation (DGA) which builds predictions of long-timescale phenomena from short trajectory data. Eigenvector Method for Umbrella Sampling (EMUS) https://github.com/ehthiede/EMUS E.H. Thiede (I am not an author) Python implementation of a stratification approach to MCMC Ensemble QuasiNewton MCMC (EQN) https://bitbucket.org/c_matthews/ensembleqn C. Matthews (I am not an author) Python implementation of an emsemble preconditioning approach to MCMC Fast Randomized Iteration (FRI) https://github.com/jonathanweare/Fast-Randomized-Iteration-FRI- with J. Dama Demonstration C++ implementation of randomized power iteration. Enhanced Sampling Toolkit https://github.com/jtempkin/enhanced_sampling_toolkit J. Tempkin (I am not an author) The Enhanced Sampling Toolkit provides a flexible and extensible toolkit for rapidly prototyping rare event simulation algorithms. The code is written entirely in Python and acts as a wrapper to various well-established molecular dynamics codes. Ticketed Diffusion Monte Carlo (TDMC) http://dx.doi.org/10.5281/zenodo.17001 J. Dama (I am not an author) Demonstration C++ implementation of an improved diffusion Monte Carlo method. emcee: The MCMC Hammer http://dan.iel.fm/emcee/current/ D. Foreman-Mackey, D. Hogg, D. Lang, and J. Goodman (I am not an author) Python implementation of an affine invariant ensemble MCMC scheme. |