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A true gem in the academic community.
Knowledgeable and truly inspiring educator.
Knowledgeable and truly inspiring educator.
Creates dynamic and thought-provoking lessons.
Your ability to make complex topics understandable and your willingness to collaborate with students made this course unforgettable. Thank you!
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About Lijun
Lijun Ding is an Assistant Professor in the Department of Mathematics at the University of California, San Diego. She earned her Ph.D. in Operations Research and Information Engineering from Cornell University in 2021, advised by Yudong Chen and Madeleine Udell. Following her doctoral studies, Ding served as a postdoctoral scholar at the Institute for Foundations of Data Science (IFDS), jointly at the University of Wisconsin-Madison and the University of Washington, from 2021 to 2023. In 2024, she held an Assistant Professor position in the Department of Industrial and Systems Engineering at Texas A&M University before joining UC San Diego. Her research specializes in continuous optimization, particularly semidefinite programming and its applications in statistics. Ding's work addresses challenges in low-rank matrix recovery, algorithmic regularization, and efficient optimization methods for data science.
Ding has authored several influential publications, including 'Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence' (2021, 140 citations), 'Factor group-sparse regularization for efficient low-rank matrix recovery' (2019, 112 citations), 'Leave-one-out approach for matrix completion: Primal and dual analysis' (2020, 95 citations), 'An optimal-storage approach to semidefinite programming using approximate complementarity' (2021, 54 citations), and 'Flat minima generalize for low-rank matrix recovery' (2024, 45 citations). Her research has garnered over 814 citations on Google Scholar. Ding received the Hsien Wu and Daisy Yen Wu Scholarship from Cornell University in 2021 and a Meritorious Reviewer Award from Mathematics of Operations Research in 2024. She teaches undergraduate and graduate courses in optimization and data science, including MATH 173A at UC San Diego in Fall 2024 and ISEN 311 at Texas A&M in Spring 2024. Her contributions extend to seminars on topics such as optimization for statistical learning and semidefinite programming in data science.
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