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

Teaching (Fall 2024):

• MATH 375.01, LINEAR PROGRAMMING Synopsis

  Gross Hall 324, TuTh 10:05 AM-11:20 AM

• MATH 757.01, LINEAR PROGRAMMING Synopsis

  Gross Hall 324, TuTh 10:05 AM-11:20 AM

• MATH 660.01, NUMERICAL PARTIAL DIFF EQNS Synopsis

  SEE INSTRU , TuTh 01:25 PM-02:40 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  

• 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. 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

• Collaborative Research: RI: Medium: Machine learning for PDEs, and with PDEs, National Science Foundation, 2024/08-2028/07.      
• 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.      
• NRT-HDR: Harnessing AI for Autonomous Material Design, National Science Foundation, 2020/09-2025/08.      
• 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.      
• Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/09-2022/09.      
• Collaborative Research: SI2-SSI: ELSI-Infrastructure for Scalable Electronic Structure Theory, National Science Foundation, 1450280, 2015/06-2022/05.