Jianfeng Lu

Professor of Mathematics

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.

Appointments and Affiliations

• Professor of Mathematics
• Associate Professor of Chemistry
• Professor of Physics

Contact Information

• Office Location: 242 Physics Bldg, 120 Science Drive, Durham, NC 27708
• Office Phone: (919) 660-2875
• Email Address: jianfeng@math.duke.edu
• Websites:
  • Personal website

Education

• Ph.D. Princeton University, 2009

Awards, Honors, and Distinctions

• IMA Prize in Mathematics and its Applications. Institute of Mathematics and its Applications. 2017
• CAREER Award. National Science Foundation. 2015
• Sloan Research Fellowship. Alfred P. Sloan Foundation. 2013
• Porter Ogden Jacobus Fellowship. Princeton University. 2008

Courses Taught

• MATH 375: Introduction to Linear Programming and Game Theory
• MATH 493: Research Independent Study
• MATH 494: Research Independent Study
• MATH 631: Measure and Integration
• MATH 660: Numerical Partial Differential Equations
• MATH 690-70: Topics in Applied Mathematics
• MATH 757: Introduction to Linear Programming and Game Theory
• PHYSICS 590: Selected Topics in Theoretical Physics

In the News

• Pratt Researchers to Harness Computational Power to Solve Time-Intensive Calculations (Jul 10, 2015)
• Sloan Foundation Names Charbonneau, Lu as 2013 Research Fellows (Feb 15, 2013)

Representative Publications

• Lu, J; Lu, Y; Zhou, Z, Continuum limit and preconditioned Langevin sampling of the path integral molecular dynamics, Journal of Computational Physics, vol 423 (2020) [10.1016/j.jcp.2020.109788] [abs].
• Han, J; Lu, J; Zhou, M, Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach, Journal of Computational Physics, vol 423 (2020) [10.1016/j.jcp.2020.109792] [abs].
• Sen, D; Sachs, M; Lu, J; Dunson, DB, Efficient posterior sampling for high-dimensional imbalanced logistic regression, Biometrika, vol 107 no. 4 (2020), pp. 1005-1012 [10.1093/biomet/asaa035] [abs].
• Cai, Z; Lu, J; Yang, S, Inchworm Monte Carlo Method for Open Quantum Systems, Communications on Pure and Applied Mathematics, vol 73 no. 11 (2020), pp. 2430-2472 [10.1002/cpa.21888] [abs].
• Li, Y; Cheng, X; Lu, J, Butterfly-net: Optimal function representation based on convolutional neural networks, Communications in Computational Physics, vol 28 no. 5 (2020), pp. 1838-1885 [10.4208/CICP.OA-2020-0214] [abs].