Guanyang Wang 王冠扬
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Guanyang Wang ContactDepartment of Statistics |
About me
Welcome! I am an assistant professor in the Department of Statistics at Rutgers University, New Brunswick.
My research primarily focuses on Markov Chain Monte Carlo (MCMC) techniques, general Monte Carlo methods, machine learning, and probability. Feel free to contact me if you would like to chat about interests we may share. My research is partially supported by NSF DMS-2210849. Thank you, NSF!
I received my Ph.D. in Mathematics (Ph.D. minor in Statistics) from Stanford University in August 2020, advised by Prof. Persi Diaconis. Prior to that, I received my B.S. with Honors in Mathematics from University of Science and Technology of China (USTC) in Jun 2015.
My Erdős number is 2 with multiplicity 2 :) ( [1] and [2] )
People
I am lucky to work with the following very talented students.
Yasa Syed (Statistics, fourth year Ph.D. student)
Wei Yuan (Statistics, third year Ph.D. student)
Budhaditya Halder (Statistics, third year Ph.D. student)
Tianze Wang (Statistics, second year Ph.D. student)
Yuchen Wei (Mathematics, fifth year Ph.D. student)
Nilava Metya (Mathematics, second year Ph.D. student)
Recent News
Excited to receive the Blackwell-Rosenbluth Award by j-ISBA !
Paper Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network with Changpeng Lu, Joseph H. Lubin, Vidur S. Sarma, Samuel Z. Stentz, Sijian Wang, and Sagar D. Khare accepted in the Proceedings of the National Academy of Sciences (PNAS) (Aug, 2023).
Paper Spectral telescope: Convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure with Qian Qin accepted in the Annals of Applied Probability (AoAP) (Jun, 2023). There is a very nice blog by Sam Power on this project.
Paper Unbiased Multilevel Monte Carlo methods for intractable distributions: MLMC meets MCMC with Tianze Wang accepted in the Journal of Machine Learning Research (JMLR) (Jun, 2023).
Paper Optimal randomized multilevel Monte Carlo for repeatedly nested expectations with Yasa Syed accepted in International Conference on Machine Learning (ICML) (Apr, 2023).
Paper Metropolis–Hastings transition kernel couplings with John O'Leary accepted in Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques (AIHP) (Jan, 2023).
Recent Works
Pattern detection in bipartite networks: a review of terminology, applications and methods
Zachary Neal, Annabel Cadieux, Diego Garlaschelli, Nicholas J. Gotelli, Fabio Saracco, Tiziano Squartini, Shade T. Shutters, Werner Ulrich, Guanyang Wang, Giovanni Strona [arXiv]
Unbiased Multilevel Monte Carlo methods for intractable distributions: MLMC meets MCMC
Tianze Wang **, Guanyang Wang
Journal of Machine Learning Research (JMLR) 2023 [arXiv] [Code] [Slides]
Optimal randomized multilevel Monte Carlo for repeatedly nested expectations
Yasa Syed**, Guanyang Wang
Conference version: International Conference on Machine Learning (ICML) 2023
Journal version to be submitted [arXiv] [Code] [Slides]
Spectral telescope: Convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure
Qian Qin, Guanyang Wang*
Annals of Applied Probability (AoAP) 2023 [Blog by Sam] [Slides by Qian]
Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network
Changpeng Lu, Joseph H. Lubin, Vidur S. Sarma, Samuel Z. Stentz, Guanyang Wang, Sijian Wang, Sagar D. Khare
Proceedings of the National Academy of Sciences (PNAS) 2023 [bioRxiv]
Repeated averages on graphs
Ramis Movassagh, Mario Szegedy, Guanyang Wang*
Minor revision, Annals of Applied Probability (AoAP)
Miscellaneous
Chinese Painting Generation Using Generative Adversarial Networks
Guanyang Wang, Ying Chen, Yuan Chen
CS231n Course Project, 2017. [Chinese Painting Dataset]