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, mathematical 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. Research in the group is informed by our
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 high-dimensional equations People
High school students
Anna Zhang,
Stuyvesant High School 2020-
Undergraduate
students
James Butler,
mathematics, University of Chicago (co-mentored with D. Abbot)
2020-
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 Masters students Bixing Qiao,
mathematics, New York University 2019-2020
Eileen Li, statistics, University of Chicago (co-mentored with A. Dinner) 2016-2017 Doctoral students Zhengqu Wan,
mathematics, New York University 2020-
Anya Katsevich, mathematics, New York University 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- 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, New York University 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, New York University 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) Preprints can be found on arXiv Monte Carlo, Markov chains, and related A
metric on directed graphs and Markov chains based on hitting
probabilitieswith Z.M. Boyd, N. Fraiman, J.L. Marzuola, P.J. Mucha, and B. Osting, SIAM Journal on Mathematics of Data Science,
3(2) [2021], 467-493Stratification
as a general variance reduction method for Markov chain
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 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,
19(1) [2021], 208-241 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 high-dimensional equations Long-timescale
predictions from short-trajectory data: A benchmark
analysis of the trp-cage miniproteinwith J. Strahan, A. Antoszewski, C. Lorpaiboon, B. Vani, and A.R. Dinner, Journal of Chemical Theory and Computation,
17(5) [2021], 2948-2963Error bounds
for dynamical spectral estimationwith R.J. Webber, E.H. Thiede, D. Dow, and A.R. Dinner, SIAM
Journal on Mathematics of Data Science, 3(1) [2021],
225-252Integrated
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
Fast Randomized
Iteration for Electronic Structure (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. pyEDGARE.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)E.H. Thiede (I am not an author) Python implementation of a stratification approach to MCMC EMUS
for Data Science Applications C. Matthews (I am not an author) Lightweight Python implementation of EMUS for data science applications Ensemble
QuasiNewton MCMC (EQN)C. Matthews (I am not an author) Python implementation of an emsemble preconditioning approach to MCMC Fast
Randomized Iteration (FRI)with J. Dama Demonstration C++ implementation of randomized power iteration. Enhanced
Sampling ToolkitJ. 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)J. Dama (I am not an author) Demonstration C++ implementation of an improved diffusion Monte Carlo method. emcee: The MCMC
HammerD. Foreman-Mackey, D. Hogg, D. Lang, and J. Goodman (I am not an author) Python implementation of an affine invariant ensemble MCMC scheme. |