معرفی
Yifan Sun is an Assistant Professor in the Department of Computer Science at Stony Brook University. She is also affiliated with the AI Institute and the Institute of Advanced Computational Science (IACS) at Stony Brook. Dr. Sun received her PhD in Electrical Engineering from UCLA in 2015, with research focusing on convex optimization and semidefinite programming. Prior to joining Stony Brook, she worked at Technicolor Research and Innovation on machine learning applications and completed postdoctoral research at the University of British Columbia in Vancouver and INRIA in Paris.
Her research centers on the design and analysis of optimization algorithms, particularly those arising in large-scale machine learning and scientific computing. Dr. Sun studies how structural properties like sparsity, curvature, and decomposability can be exploited to design faster, more stable, and more interpretable optimization methods. Her work spans both theoretical foundations and practical applications, covering first-order methods, quasi-Newton techniques, and algorithms for convex programming to nonconvex deep models. She also investigates how optimization theory, particularly insights from linear algebra and geometry, can improve understanding of deep learning models, regularization techniques, and representation learning.
Dr. Sun's recent publications demonstrate strong trends in large-scale graph learning, advanced optimization techniques (particularly Frank-Wolfe variants), and applications to natural language processing. Her work often bridges theoretical guarantees with practical implementations, as evidenced by her open-source code repositories. She leads the OptML Research lab at Stony Brook, focusing on optimization methods for machine learning challenges. Her publications appear in top venues including NeurIPS, ICML, CVPR, and various optimization journals, reflecting her interdisciplinary approach that connects computer science, applied mathematics, and engineering disciplines.
