Jianfeng Lu, Professor of Mathematics and Physics and Associate Professor of Chemistry
Jianfeng Lu is an applied mathematician interested in mathematical analysis and algorithm development for problems from computational physics, theoretical chemistry, materials science and other related fields.
More specifically, his current research focuses include: Electronic structure and many body problems; quantum molecular dynamics; multiscale modeling and analysis; rare events and sampling techniques.  Contact Info:
 Office Hours:
 By email appointments
 Education:
Ph.D.  Princeton University  2009 
BS  Peking University  2005 
 Specialties:

Applied Math
 Research Interests:
Mathematical analysis and algorithm development for problems from
computational physics, theoretical chemistry, materials science and others.
More specifically:
Electronic structure and many body problems;
Multiscale modeling and analysis; and
Rare events and sampling techniques.
 Areas of Interest:
Applied Mathematics Partial Differential Equations Probability Numerical Analysis Scientific Computing
 Curriculum Vitae
 Current Ph.D. Students
 Postdocs Mentored
 Haizhao Yang (July, 2015  present)
 Zhennan Zhou (August, 2014  present)
 Undergraduate Research Supervised
 Jeremy Tay (September, 2015  December, 2015)
 Fuchsia Chen (January, 2015  September, 2015)
 Leslie Lei (May, 2013  May, 2014)
 Recent Publications
(More Publications)
 Lu, J; Steinerberger, S, Optimal Trapping for Brownian Motion: a Nonlinear Analogue of the Torsion Function,
Potential Analysis, vol. 54 no. 4
(April, 2021),
pp. 687698 [doi] [abs]
 Thicke, K; Watson, AB; Lu, J, Computing edge states without hard truncation,
Siam Journal on Scientific Computing, vol. 43 no. 2
(March, 2021),
pp. B323B353 [doi] [abs]
 Stubbs, KD; Watson, AB; Lu, J, Iterated projected position algorithm for constructing exponentially localized generalized Wannier functions for periodic and nonperiodic insulators in two dimensions and higher,
Physical Review B, vol. 103 no. 7
(February, 2021) [doi] [abs]
 Lu, J; Otto, F, Optimal Artificial Boundary Condition for Random Elliptic Media,
Foundations of Computational Mathematics
(January, 2021) [doi] [abs]
 Han, J; Lu, J; Zhou, M, Solving highdimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach,
Journal of Computational Physics, vol. 423
(December, 2020) [doi] [abs]
 Recent Grant Support
 NRTHDR: Harnessing AI for Autonomous Material Design, National Science Foundation, 2020/092025/08.
 Innovation of Numerical Methods for HighDimensional Problems, National Science Foundation, 2020/072023/06.
 FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis, National Science Foundation, 2019/102022/09.
 HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/102022/09.
 EAGERQACQSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets, National Science Foundation, 2020/092022/08.
 Collaborative Research: SI2SSI: ELSIInfrastructure for Scalable Electronic Structure Theory, National Science Foundation, 1450280, 2015/062022/05.
 Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/092021/09.
 Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/092021/09.
 CAREER: Research and training in advanced computational methods for quantum and statistical mechanics, National Science Foundation, DMS1454939, 2015/092020/08.
