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:

Office Location:	331 Gross Hall, 140 Science Drive, Durham, NC 27708
Email Address:
Web Page:	http://www.math.duke.edu/~jianfeng/

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  

• Kyle Thicke  
• Chao Xu  

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)

1. Zhu, G; Bierman, J; Lu, J; Li, Y, Quantum circuit for non-unitary linear transformation of basis sets, Npj Quantum Information, vol. 11 no. 1 (December, 2025) [doi]  [abs]
2. Chen, Z; Lu, J; Lu, Y; Zhang, X, FULLY DISCRETIZED SOBOLEV GRADIENT FLOW FOR THE GROSS-PITAEVSKII EIGENVALUE PROBLEM, Mathematics of Computation, vol. 94 no. 356 (November, 2025), pp. 2723-2760 [doi]  [abs]
3. He, Y; Balasubramanian, K; Sriperumbudur, BK; Lu, J, Regularized Stein Variational Gradient Flow, Foundations of Computational Mathematics, vol. 25 no. 4 (August, 2025), pp. 1199-1257 [doi]  [abs]
4. Triplett, L; Lu, J, Diffusion methods for generating transition paths, Journal of Computational Physics, vol. 522 (February, 2025) [doi]  [abs]
5. Rohatgi, D; Marwah, T; Lipton, ZC; Lu, J; Moitra, A; Risteski, A, Towards characterizing the value of edge embeddings in Graph Neural Networks, Proceedings of Machine Learning Research, vol. 267 (January, 2025), pp. 51905-51923  [abs]

Recent Grant Support

• Collaborative Research: RI: Medium: Machine learning for PDEs, and with PDEs, National Science Foundation, 2024/08-2028/07.      
• 2026 Gene Golub SIAM Summer School, Society for Industrial and Applied Mathematics , 2025/10-2027/01.      
• NRT-HDR: Harnessing AI for Autonomous Material Design, National Science Foundation, 2020/09-2026/08.      
• Innovation of Numerical Methods for High-Dimensional Partial Differential Equations, National Science Foundation, 2023/08-2026/07.      
• New computational methods to dynamically pinpointing the subregions carrying disease-associated rare variants, National Human Genome Research Institute, 2022/09-2026/07.      
• RTG: Training Tomorrow's Workforce in Analysis and Applications, National Science Foundation, 2021/07-2026/06.      
• Innovation of Numerical Methods for High-Dimensional Problems, National Science Foundation, 2020/07-2024/06.      
• HDR TRIPODS: Innovations in Data Science: Integrating Stochastic Modeling, Data Representation, and Algorithms, National Science Foundation, 2019/10-2023/09.      
• FET: Small: Efficient Inference Tools for Quantum Systems: Algorithms, Applications, and Analysis, National Science Foundation, 2019/10-2023/09.      
• EAGER-QAC-QSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets, National Science Foundation, 2020/09-2023/08.