I am an applied mathematician, and a researcher at Vicarious AI. I obtained my Ph.D. in applied math at Brown University, advised by Prof. Stuart Geman, and my bachelor degrees from Peking University.
My current research interests are in building compositional probabilistic generative models, and the associated inference and learning problems.
Most recent publications on Google Scholar.
‡ indicates equal contribution.
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
Miguel Lázaro-Gredilla, Wolfgang Lehrach, Nishad Gothoskar, Guangyao Zhou,
Antoine Dedieu, Dileep George
AAAI Conference on Artificial Ingelligence (AAAI) 2021
Mixed Hamiltonian Monte Carlo for mixed discrete and continuous variables
Advances in Neural Information Processing Systems (NeurIPS) 2020
Extended abstract accepted as talk at PROBPROG 2020
A detailed mathematical theory of thalamic and cortical microcircuits based on inference
in a generative vision model
Dileep George, Miguel Lázaro-Gredilla, Wolfgang Lehrach, Antoine Dedieu, Guangyao Zhou
Capacities and efficient computation of first passage probabilities
Jackson Loper‡, Guangyao Zhou‡, Stuart Geman
Phys. Rev. E 102, 023304, 2020
Base-pair ambiguity and the kinetics of RNA folding
Guangyao Zhou, Jackson Loper, Stuart Geman
BMC Bioinformatics, 20(1):666, 2019
Sparse feature selection by information theory
Guangyao Zhou, Stuart Geman, Joachim M Buhmann
IEEE International Symposium on Information Theory (ISIT), 2014
L1-graph construction using structured sparsity
Guangyao Zhou, Zhiwu Lu, Yuxin Peng
Neurocomputing, 120:441-452, 2013
Full Resume in PDF.
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