Jiaoyang Huang

• 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).
• Amol Aggarwal and Jiaoyang Huang (2024), Edge Rigidity of Dyson Brownian Motion with General Initial Data, Electronic Journal of Probability, 29 (), pp. 1-62.
• Vadim Gorin and Jiaoyang Huang (2024), Dynamical Loop Equation, The Annals of Probability, 52 (5), pp. 1758-1863.
• Jiaoyang Huang (2024), Asymptotics of Generalized Bessel Functions and Weight Multiplicities via Large Deviations of Radial Dunkl Processes, Probability Theory and Related Fields, 190 (), pp. 941-1006.
• Jiaoyang Huang, Fan Yang, Lingfu Zhang (2024), Pearcey universality at cusps of polygonal lozenge tiling, Communications on Pure and Applied Mathematics, 77 (9), pp. 3708-3784.
• Alice Guionnet and Jiaoyang Huang (2023), Large Deviations Asymptotics of Rectangular Spherical Integral, Journal of Functional Analysis, 285 (11), pp. 110-144.
• Jiaoyang Huang (2023), Edge Statistics for Lozenge Tilings of Polygons, I: Concentration of Height Function on Strip Domains, Probability Theory and Related Fields, 188 (), pp. 337-485.
• Jiaoyang Huang and Horng-Tzer Yau (2023), Spectrum of Random d-Regular Graphs up to the Edge, Communications on Pure and Applied Mathematics, 77 (3), pp. 1635-1723.
• Kenji Kawaguchi, Zhun Deng, Xu Ji, Jiaoyang Huang (2023), How Does Information Bottleneck Help Deep Learning?, Proceedings of the 40th International Conference on Machine Learning (ICML), 202 ().
• Jiaoyang Huang (2022), Invertibility of Adjacency Matrices for Random d-Regular Graphs, Duke Mathematical Journal, 170 (18), pp. 3977-4032.
• All Research from Jiaoyang Huang »

Current Courses

• STAT4300 - Probability

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

  STAT4300004 ( Syllabus )

  STAT4300005 ( Syllabus )

Past 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.

• NSF Career Award, 2024
• Bernoulli Society New Researcher Award, 2024
• Sloan Research Fellowship, 2024
• 2022 Blavatnik Regional Awards, 2022
• Simons Junior Fellow, 2020
• Harvard Graduate Society Term-time Research Fellowship, 2018
• Gold medal in the 50th International Mathematical Olympiad, 2009

• Penn Today - 02/20/2024
• Interview by Simons Foundation - 11/29/2021

Knowledge @ Wharton

• The EV Trade War Alone Won’t Save American Manufacturing, Knowledge @ Wharton - 02/11/2025
• AI’s Impact on Productivity and Innovation | Daniel Rock, Knowledge @ Wharton - 02/11/2025
• Jeremy Siegel: How Tariffs Could Impact the U.S. Economy, Knowledge @ Wharton - 02/10/2025
• Why Accountability Matters in AI Development and Governance, Knowledge @ Wharton - 02/07/2025
• Embracing AI: Are You a Doomer, Gloomer, Zoomer, or Bloomer?, Knowledge @ Wharton - 02/04/2025
• Can Pride Make You Look Incompetent at Work?, Knowledge @ Wharton - 02/04/2025
• Transformative Tech: Innovating for the Underserved | Valentina Assenova, Knowledge @ Wharton - 02/04/2025
• As People Still Recover From the LA Wildfires, What Can Business Leaders Do to Help?, Knowledge @ Wharton - 02/04/2025
• The Economic Impact of Trump’s Tariffs, Knowledge @ Wharton - 02/03/2025
• Building a Brand: The Entrepreneurial Lessons Learned on the Road to Cirkul’s Success, Knowledge @ Wharton - 01/30/2025