I am a Research Scientist at Meta. I received my Ph.D. in mathematics from the University of Wisconsin-Madison, where I worked with Prof. Hanbaek Lyu. My dissertation, “Block Majorization–Minimization for Nonconvex Optimization and Beyond: Trust-Region, Riemannian, and Stochastic Extensions”, was awarded the John Nohel Prize for Outstanding Dissertation. During my doctoral studies, I also earned a Master’s degree in Computer Science.
My research centers on large-scale optimization, with a focus on the design and theoretical analysis of scalable algorithms for nonconvex and structured problems. In particular, I have developed and studied:
- Block majorization–minimization methods for nonconvex optimization
- Riemannian optimization for multi-block problems
- Stochastic optimization algorithms
- Trust-region frameworks
I am broadly interested in bridging optimization theory and real-world machine learning systems, especially in settings that require scalability, structure awareness, and strong convergence guarantees.
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