- About Me
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Research Interests
- Probability Theory: Extreme Value Theory, Statistical Mechanics.
- Machine Learning: Federated learning, Statistical learning theory.
Publications and Preprints
- M. Biskup and H. Huang, A limit law for the maximum of subcritical DG-model on a hierarchical lattice (2023)
[arXiv] [Slides]
- J. Zhang et al, How a few Davids improve one Goliath: Federated learning in a resource-skew edge computing environments.
[Paper] [Code]
- Phase Transition and Critical Behavior in Hierarchical Integer-valued Gaussian and Coulomb Gas Model. Joint work with M. Biskup. (In preparation).
- Asynchronous Federated Learning with Orthogonal Weight Calibration. Joint work with J. Zhang (In Preparation).
Talks and Expository Paper
Teaching
- Fall 2024: Math 118 (Mathematical Methods of Data Theory)
- Spring 2024: Math 170E (Probability Theory)
- Winter 2024: Math 170S (Statistics)
- Fall 2023: Math 156 (Machine Learning) and Math 275A (Graduate-level Probability Theory)
- Spring 2023: Math 131BH (Analysis Honors II)
- Winter 2023: Math 131AH (Analysis Honors I)
- Fall 2022: Math 275A (Graduate-level Probability Theory)
- Spring 2022: Math 131BH (Analysis Honors II)
- Winter 2022: Math 131AH (Analysis Honors I)
- Fall 2021: Math 131AH (Analysis Honors I)
- Spring 2021: Math 131BH (Analysis Honors) and Math 171 (Stochastic Processes)
- Winter 2021: Math 132 (Complex Analysis) and Math 115A (Linear Algebra)
- Fall 2020: Math 131AH (Analysis Honors I)
- Summer 2020: Math 170E (Probability Theory)
- Spring 2020: Math 135 (ODE)
- Winter 2020: Math 32A (Multivariable Calculus)
- Fall 2019: Math 131A (Analysis)
Math Friends
