Jianfeng Lu, Professor
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:
Teaching (Fall 2022):
 MATH 631.01, MEASURE AND INTEGRATION
Synopsis
 Gross Hall 324, WF 10:15 AM11:30 AM
 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)
 Cai, Z; Lu, J; Yang, S, Fast algorithms of bath calculations in simulations of quantum systembath dynamics,
Computer Physics Communications, vol. 278
(September, 2022) [doi] [abs]
 Bierman, J; Li, Y; Lu, J, Quantum Orbital Minimization Method for Excited States Calculation on a Quantum Computer.,
Journal of Chemical Theory and Computation, vol. 18 no. 8
(August, 2022),
pp. 46744689 [doi] [abs]
 Lu, J; Steinerberger, S, Neural collapse under crossentropy loss,
Applied and Computational Harmonic Analysis, vol. 59
(July, 2022),
pp. 224241 [doi] [abs]
 Lu, J; Wang, L, Complexity of zigzag sampling algorithm for strongly logconcave distributions,
Statistics and Computing, vol. 32 no. 3
(June, 2022) [doi] [abs]
 Pescia, G; Han, J; Lovato, A; Lu, J; Carleo, G, Neuralnetwork quantum states for periodic systems in continuous space,
Physical Review Research, vol. 4 no. 2
(June, 2022) [doi] [abs]
 Recent Grant Support
 RTG: Training Tomorrow's Workforce in Analysis and Applications, National Science Foundation, 2021/072026/06.
 NRTHDR: Harnessing AI for Autonomous Material Design, National Science Foundation, 2020/092025/08.
 EAGERQACQSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets, National Science Foundation, 2020/092023/08.
 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.
 Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/092022/09.
 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.
 CAREER: Research and training in advanced computational methods for quantum and statistical mechanics, National Science Foundation, DMS1454939, 2015/092020/08.
