Peng Wang
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Peng Wang |
About Me
Currently, I am an assistant professor at the Department of Computer and Information Science at the University of Macao. Before this, I am a postdoc research fellow advised by Professors Laura Balzano and Qing Qu at the University of Michigan. I got my Ph.D. degree under the supervision of Professor Anthony Man-Cho So at The Chinese University of Hong Kong.
Open Positions (Fall 2026)
I am actively seeking PhD students with a strong background in mathematics or programming. Research assistants and postdoctoral positions are also available. If you are interested, please feel free to contact me directly! Please visit here for more information!
Research Interests
Broadly speaking, my research interest lies in the intersects of optimization, machine learning, and artificial intelligence. Currently, I am devoted to understanding mathematical foundations of deep learning and generative AI models, including supervised learning models, diffusion models, and large language models. I mainly study how low-complexity structures (e.g., low-rankness, sparsity, over-parameterization) in practical problems lead to favorable optimization properties and use them to mitigate the challenges caused by worst-case scenarios, enable efficient optimization, and improve our understanding of learning phenomena.
Feel free to email me if you are interested in my research. Remote collaboration is also welcome!
What's New
[December 2025] I will chair a session at the IEEE International Conference on Big Data in Macao!
[November 2025] I will give a talk at the Conference on Mathematics and AI Science in Dongguan, Guangdong!
[October 2025] I will give a talk at the HKUST(GZ) & UM Data Science Workshop at the University of Macau!
[October 2025] I will give a presentation at the seminar hosted by the Department of Statistics and Data Science at the Chinese University of Hong Kong!
[September 2025] One paper on understanding feature learning of diffusion models is accepted by NeurIPS 2025!
[August 2025] Our paper on understanding hierarchical representation learning of deep neural networks is accepted by JMLR!
[August 2025] Our new book Learning Deep Representations of Data Distributions is posted online! Thanks for my wonderful collaborators Dr. Sam Buchanan, Druv Pai, and Prof. Yi Ma!
[June 2025] One paper on the global loss landscape analyais of deep matrix factorization is posted!
[May 2025] One paper on understanding the mechenism of transformers has been accepted by ICML 2025!
[Apr 2025] Our recent works on studying the generalization of diffusion models appears on SIAM News Blog!
[Mar 2025] A tutorial paper on understanding the role of low-rank structures in the training and adaptation of deep learning models is posted!
[Mar 2025] I will attend the Conference on Parsimony and Learning at Stanford University from March 24-27!
[Feb 2025] One paper on the local error bound of deep linear networks is posted!
[Jan 2025] One paper on the representation capabilities of diffusion models is posted!
[Jan 2025] One paper is accepted by INFORMS Journal on Computing!
[Jan 2025] One paper on the separation capabilities of shallow nonlinear networks is posted!
[Jan 2025] I will give a presentaion in 1W-MINDS Seminar online at 5PM (Beijing Time) on Jan 9, 2025!
[Jan 2025] I will give a presenation in IMS Young Mathematical Scientists Forum at National University of Singapore!
[May 2024] Five papers [paper1, paper2, paper3, paper4, paper5] are accepted by ICML 2024!
Teaching Courses
CISC7402 Mathematics for Artificial Intelligence (Fall 2025)
CISC3023 Machine Learning (Spring 2026, Syllabus)
Service
Area Chair of CPAL (2024-Present)
Reviewers of SIAM Journal on Optimization, Operation Research, Journal of Machine Learning Research, IEEE Transactions on Information Theory, IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Global Optimization
Reviewers of ICML (2021-Present), NeurIPS (2022-Present), ICLR (2023-Present)
