Jiaoyang Huang

Jiaoyang Huang
  • Associate Professor of Statistics and Data Science
  • Associate Professor of Mathematics (secondary appointment)

Contact Information

  • office Address:

    309 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: Random matrix theory, random graphs, interacting particle systems, optimization of deep neural networks, posterior sampling, and uncertainty quantification of large scale inverse problems.

Links: Personal Website, Google Scholar

Overview

Education

Ph.D. in Mathematics, Harvard University, 2019
Advisors: Horng-Tzer Yau

B.S. in Mathematics, Massachusetts Institute of Technology, 2014

B.S. in Computer Science and Technology, Tsinghua University, 2011

Academic Positions Held

Simons Junior Fellow (Postdoc Associate), Courant Institute of Mathematical Sciences, New York University, 2020-2022

Member, Institute for Advanced Study, 2019-2020

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Research

Teaching

All Courses

  • AMCS5999 - Independent Study

    Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.

  • AMCS6481 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.

  • MATH4990 - Supervised Study

    Study under the direction of a faculty member. Intended for a limited number ofmathematics majors.

  • MATH6480 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

  • STAT4300 - Probability

    Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.

  • STAT9300 - Probability Theory

    Measure theoretic foundations, laws of large numbers, large deviations, distributional limit theorems, Poisson processes, random walks, stopping times.

  • STAT9910 - Sem in Adv Appl of Stat

    This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.

Awards and Honors

In the News

Activity

Latest Research

Yifan Chen, Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M Stuart (2024), Efficient, Multimodal, and Derivative-Free Bayesian Inference With Fisher-Rao Gradient Flows, , 40 (12).
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In the News

Penn Today
- 02/20/2024
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