CPAL Rising Star Award
Conference on Parsimony and Learning, 2024.
Yiping Lu is a tenure-track Assistant Professor at the Beijing International Center for Mathematical Research, Peking University. His research spans machine learning, numerical algorithms, applied probability, PDEs, and control.
The long term goal of my research is to develop a scientific discipline that combines domain knowledge, machine learning, numerical computation, and randomized experiments. I work across probability and statistics, numerical analysis, control, optimization, inverse problems, and operations research.
A recurring theme is that structure determines scalability. Differential equations, stochastic processes, operator geometry, and algebraic invariants tell us which representations and algorithms can remain stable as problems become larger or more complex.
Training in applied mathematics, scientific computing, probability, and machine learning.
Peking University
Northwestern University, with a courtesy appointment in Engineering Sciences and Applied Mathematics
New York University
Stanford University · advised by Lexing Ying and Jose Blanchet
School of Mathematical Sciences, Peking University
Selected recognition and professional service across machine learning and applied mathematics.
Conference on Parsimony and Learning, 2024.
University of Chicago, 2022.
Support for interdisciplinary doctoral research, 2021 to 2024.
Mathematics of Operations Research.
Service for major machine learning conferences including ICML, NeurIPS, ICLR, and AISTATS.
Tutorials, seminars, workshops, and interdisciplinary community building.
I am actively recruiting undergraduate students, graduate students, and postdocs to join my research group. Interested candidates are encouraged to email yipinglu [at] bicmr.pku.edu.cn.
In your message, briefly describe your background, the questions you want to work on, and one research direction from this site that genuinely interests you.
Email Yiping