Jianfeng Lu, Associate Professor of Mathematics and Chemistry and Physics

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.

Contact Info:

Office Location:	242 Physics Bldg, 120 Science Drive, Durham, NC 27708
Office Phone:	(919) 660-2875
Email Address:
Web Page:	http://www.math.duke.edu/~jianfeng/

Teaching (Spring 2019):

• MATH 375.01, LINEAR PROGRAMMING Synopsis

  Gross Hall 304B, TuTh 10:05 AM-11:20 AM

  (also cross-listed as MATH 757.01)

• MATH 660.01, NUMERICAL PARTIAL DIFF EQNS Synopsis

  Gross Hall 304B, 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 S. 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; Vanden-Eijnden, E, Methodological and Computational Aspects of Parallel Tempering Methods in the Infinite Swapping Limit, Journal of Statistical Physics, vol. 174 no. 3 (February, 2019), pp. 715-733 [doi]  [abs]
2. Li, Y; Lu, J, Bold diagrammatic Monte Carlo in the lens of stochastic iterative methods, Transactions of Mathematics and Its Applications, vol. 3 no. 1 (February, 2019), pp. 1-17, Oxford University Press (OUP) [doi]
3. Liu, JG; Lu, J; Margetis, D; Marzuola, JL, Asymmetry in crystal facet dynamics of homoepitaxy by a continuum model, Physica D: Nonlinear Phenomena (January, 2019) [doi]  [abs]
4. Martinsson, A; Lu, J; Leimkuhler, B; Vanden-Eijnden, E, The simulated tempering method in the infinite switch limit with adaptive weight learning, Journal of Statistical Mechanics: Theory and Experiment, vol. 2019 no. 1 (January, 2019), pp. 013207-013207, IOP Publishing [doi]
5. Huang, H; Liu, JG; Lu, J, Learning interacting particle systems: Diffusion parameter estimation for aggregation equations, Mathematical Models and Methods in Applied Sciences, vol. 29 no. 1 (January, 2019), pp. 1-29 [doi]  [abs]

Recent Grant Support

• Quantum Computing in Chemical and Material Sciences, Department of Energy, 2018/09-2021/09.      
• CAREER: Research and training in advanced computational methods for quantum and statistical mechanics, National Science Foundation, DMS-1454939, 2015/09-2020/08.