معرفی
Yuxin Chen is a Professor at the University of Pennsylvania, holding joint appointments in the Department of Statistics and Data Science and the Department of Electrical and Systems Engineering. Prior to UPenn, he was an Assistant Professor at Princeton University (2017-2021) and a Postdoctoral Researcher at Stanford University (2015-2017). His research spans statistics, optimization, reinforcement learning theory, diffusion models, and information theory, with a focus on theoretical foundations and practical algorithms for machine learning.
- Education: Ph.D. in Electrical Engineering (Stanford, 2015), M.S. in Statistics (Stanford, 2013), M.S. in Electrical and Computer Engineering (UT Austin, 2010), B.E. in Electrical/Microelectronics (Tsinghua, 2008).
Research Interests encompass theoretical and applied aspects of machine learning, including nonconvex optimization, sample complexity analysis, low-dimensional adaptation, and generative modeling. His work bridges mathematical rigor with real-world applications, particularly in scientific imaging and high-dimensional data analysis.
Scientific Awards include the
- SIAM Activity Group on Imaging Science Best Paper Prize (2024)
- Alfred P. Sloan Fellowship (2022)
- NSF Career Award (2022)
- Google Research Scholar Award (2022)
- IEEE Transactions on Power Electronics Prize Paper Award (2024)
Advising and Grants: He has mentored numerous students who have transitioned to academic roles at institutions like UIUC and UW-Madison. His research is supported by grants from the NSF, Amazon, and Google, with recent projects focusing on controllable diffusion models and efficient reinforcement learning algorithms.
