Jonathan Weare
Warren Weaver Hall, Room 1103 251 Mercer Street New York, NY 10012-1185 [my last name]@nyu.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.
Research
areas Monte Carlo
sampling methods
multiscale analysis and simulation randomized numerical linear algebra rare event analysis and simulation statistical and machine learning for scientific computing People
High school students
Anna Zhang,
Stuyvesant High School 2020-2021
Undergraduate
students
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 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 Xiaoou Cheng, New
York University 2020-
Huan Zhang, mathematics, New York University 2019- Zhengqu Wan, mathematics, New York University 2018- Chatipat Lorpaiboon, chemistry, University of Chicago (co-mentored with A. Dinner) 2018- John Strahan, chemistry, University of Chicago (co-mentored with A. Dinner) 2018- 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 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
Ensemble Markov chain Monte Carlo with teleporting walkers
with M. Lindsey and A. Zhang, SIAM Journal on Uncertainty
Quantification, acceptedA 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,
3(2) [2021], 467-493Stratification as a general variance reduction method for Markov chain 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], 277-290Eigenvector 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], 65-80Efficient 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,
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-actin with 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 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], 1771-1784Variance 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
Approximating matrix eigenvalues by subspace iteration with
repeated random sparsification
with S.M. Greene, R.J. Webber, and T.C. Berkelbach, SIAM Journal on Scientific Computing, acceptedImproved 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],
5572–5585Beyond 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], 547-587Rare event analysis and simulation
Rare event sampling improves Mercury instability statistics
Statistical and machine learning for
scientific computing
with D.S. Abott, R.J. Webber, and S. Hadden, Astrophysical
Journal, 923(2) [2021], 236 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], 2327-2347Practical 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], 863-891Trajectory 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], 1-60Improved 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 Kuroshio with E. Vanden-Eijnden, Monthly Weather Review, 141
[2013], 1822-1841Steered 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
Efficient Conditional Path Sampling of Stochastic
Differential EquationsThesis (Ph.D.) - University of California, Berkeley, ProQuest,
[2007], ISBN: 978-0549-73732-2 Learning
forecasts of rare stratospheric transitions from short
simulationswith J. Finkel, R.J. Webber, D.S. Abbot, and E.P. Gerber, Monthly
Weather Review, 149(11) [2021], 3649-3669Long-timescale predictions from short-trajectory data: A benchmark analysis of the trp-cage miniprotein with 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 estimation with 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 operators with 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 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
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. 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) E.H. Thiede (I am not an author) Python implementation of a stratification approach to MCMC Umbrella Sampling 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 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) J. Dama (I am not an author) Demonstration C++ implementation of an improved diffusion Monte Carlo method. emcee: The MCMC Hammer D. Foreman-Mackey, D. Hogg, D. Lang, and J. Goodman (I am not an author) Python implementation of an affine invariant ensemble MCMC scheme. |