Jianfeng Lu, James B. Duke Distinguished Professor
Jianfeng Lu is an applied mathematician interested in mathematical analysis and algorithm development for problems from computational physics, theoretical chemistry, materials science, machine learning, and other related fields.
More specifically, his current research focuses include: High dimensional PDEs; generative models and sampling methods; control and reinforcement learning; electronic structure and many body problems; quantum molecular dynamics; multiscale modeling and analysis.  Contact Info:
Teaching (Fall 2024):
 MATH 375.01, LINEAR PROGRAMMING
Synopsis
 Gross Hall 324, TuTh 10:05 AM11:20 AM
 MATH 757.01, LINEAR PROGRAMMING
Synopsis
 Gross Hall 324, TuTh 10:05 AM11:20 AM
Teaching (Spring 2025):
 MATH 660.01, NUMERICAL PARTIAL DIFF EQNS
Synopsis
 SEE INSTRU , TuTh 01:25 PM02:40 PM
 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; Stubbs, KD, Algebraic Localization of Wannier Functions Implies Chern Triviality in Nonperiodic Insulators,
Annales Henri Poincare, vol. 25 no. 8
(August, 2024),
pp. 39113926 [doi] [abs]
 Bierman, J; Li, Y; Lu, J, Qubit Count Reduction by Orthogonally Constrained Orbital Optimization for Variational Quantum ExcitedState Solvers.,
Journal of chemical theory and computation, vol. 20 no. 8
(April, 2024),
pp. 31313143 [doi] [abs]
 Loring, TA; Lu, J; Watson, AB, Locality of the windowed local density of states,
Numerische Mathematik, vol. 156 no. 2
(April, 2024),
pp. 741775, Springer Science and Business Media LLC [doi] [abs]
 Li, X; Pura, J; Allen, A; Owzar, K; Lu, J; Harms, M; Xie, J, DYNATE: Localizing rarevariant association regions via multiple testing embedded in an aggregation tree.,
Genet Epidemiol, vol. 48 no. 1
(February, 2024),
pp. 4255 [doi] [abs]
 Chen, Z; Lu, J; Zhang, A, ONEDIMENSIONAL TENSOR NETWORK RECOVERY,
SIAM Journal on Matrix Analysis and Applications, vol. 45 no. 3
(January, 2024),
pp. 12171244 [doi] [abs]
 Recent Grant Support
 Collaborative Research: RI: Medium: Machine learning for PDEs, and with PDEs, National Science Foundation, 2024/082028/07.
 Innovation of Numerical Methods for HighDimensional Partial Differential Equations, National Science Foundation, 2023/082026/07.
 New computational methods to dynamically pinpointing the subregions carrying diseaseassociated rare variants, National Human Genome Research Institute, 2022/092026/07.
 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.
 Innovation of Numerical Methods for HighDimensional Problems, National Science Foundation, 2020/072024/06.
 HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/102023/09.
 FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis, National Science Foundation, 2019/102023/09.
 EAGERQACQSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets, National Science Foundation, 2020/092023/08.
 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.
