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 dynamical systems 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 Postdoctoral scholars 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 Stratification as a general
variance reduction method for Monte Carlowith 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 gradientswith C. Matthews [2018] Umbrella sampling: a powerful method to sample tails of
distributionswith 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
simulationwith B. Leimkuhler and C. Matthews, Statistics and Computing,
28(2) [2017], 277-290Eigenvector method for umbrella sampling enables error analysiswith E. Thiede, B. Van Koten, and A. Dinner, Journal of
Chemical Physics, 145(8) [2016], 084115 Sharp entrywise perturbation bounds for Markov chainswith 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 methodwith B. Roux, Journal of Chemical Physics, 138(8) [2013],
084107 An affine-invariant sampler for exoplanet fitting and discovery
in radial velocity datawith F. Hou, J. Goodman, D. Hogg, and C. Schwab, The
Astrophysical Journal, 75 [2012], 198 Ensemble samplers with affine invariancewith J. Goodman, Communications in Applied Mathematics and
Computational Science, 5 [2010], 65-80Efficient Monte Carlo sampling by parallel marginalizationProceedings of the National Academy of Science, 104(31)
[2007], 12657-12662 Multiscale analysis and simulation 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], 184114Nucleotide regulation of the structure and dynamics of
G-actinwith M. Saunders, J. Tempkin, A. Dinner, B. Roux, and G. Voth, Biophysical
Journal, 106(8) [2014], 1710-1720The relaxation of a family of broken bond crystal surface
modelswith J. Marzuola, Physical Review E, 88 [2013], 032403 The theory of ultra coarse graining I, general principleswith 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-grainingwith 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
representationswith 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 regimewith H. Al Hajj Shehadeh and R.V. Kohn, Physica D, 240
[2011], 1771-1784Variance reduction for particle filters of systems with
time-scale separationwith 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 Interactionwith S.M. Greene, R.J. Webber, and T.C. Berkelbach, Journal
of Chemical Theory and Computation, 16(9) [2020],
5572–5585Beyond walkers in stochastic quantum chemistry: reducing
error using Fast Randomized Iterationwith 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 algebrawith L.H. Lim, SIAM Reviews: Research Spotlight, 59(3)
[2017], 547-587Rare 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 modelwith J. Finkel, and D.S. Abbot, Journal of Atmospheric
Science, 77(7) [2020], 2327–2347Practical rare event simulation for extreme mesoscale weatherwith 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
minimizationwith 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], 863-891Trajectory stratification of stochastic dynamicswith 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 networkswith 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], 1-60Improved 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 Kuroshiowith E. Vanden-Eijnden, Monthly Weather Review, 141
[2013], 1822-1841Steered transition path samplingwith N. Guttenberg and A. Dinner, Journal of Chemical
Physics, 136 [2012], 234103 Rare event simulation for small noise diffusionswith 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 modelJournal of Computational Physics, 228 [2009], 4312-4331 Statistical and machine learning for dynamical systems Integrated VAC: A robust
strategy for identifying eigenfunctions of dynamical operatorswith C. Lorpaiboon, E.H. Thiede, R.J. Webber, and A.R. Dinner, Journal
of Physical Chemistry B, 124(42) [2020], 9354-9364Galerkin approximation of dynamical quantities using
trajectory datawith 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
pyEDGARhttps://github.com/ehthiede/pyEDGAR E. 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. 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 Toolkithttps://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 Hammerhttp://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. |