Jianfeng Lu, James B. Duke Distinguished Professor

Jianfeng Lu

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.

Office Location:  242 Physics Bldg, 120 Science Drive, Durham, NC 27708
Email Address: send me a message
Web Page:  http://www.math.duke.edu/~jianfeng/

Teaching (Fall 2024):

Office Hours:

By email appointments
Education:

Ph.D.Princeton University2009
BSPeking University2005
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

Current Ph.D. Students  

Postdocs Mentored

Undergraduate Research Supervised

Recent Publications

  1. Lu, J; Stubbs, KD, Algebraic Localization of Wannier Functions Implies Chern Triviality in Non-periodic Insulators, Annales Henri Poincare, vol. 25 no. 8 (August, 2024), pp. 3911-3926 [doi]  [abs]
  2. Bierman, J; Li, Y; Lu, J, Qubit Count Reduction by Orthogonally Constrained Orbital Optimization for Variational Quantum Excited-State Solvers., Journal of chemical theory and computation, vol. 20 no. 8 (April, 2024), pp. 3131-3143 [doi]  [abs]
  3. Loring, TA; Lu, J; Watson, AB, Locality of the windowed local density of states, Numerische Mathematik, vol. 156 no. 2 (April, 2024), pp. 741-775, Springer Science and Business Media LLC [doi]  [abs]
  4. Li, X; Pura, J; Allen, A; Owzar, K; Lu, J; Harms, M; Xie, J, DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree., Genet Epidemiol, vol. 48 no. 1 (February, 2024), pp. 42-55 [doi]  [abs]
  5. Chen, Z; Lu, J; Zhang, A, ONE-DIMENSIONAL TENSOR NETWORK RECOVERY, SIAM Journal on Matrix Analysis and Applications, vol. 45 no. 3 (January, 2024), pp. 1217-1244 [doi]  [abs]
Recent Grant Support