Research

Research Interests

I work broadly at the interface of machine learning, optimization and statistics. In particular, I am interested in

• Theory and applications of optimization and sampling techniques to generative artificial intelligence (GenAI), e.g., training strategies of large language models (LLMs) and attention-based vision models (e.g., vision transformer), and score-based generative models (SGMs; a.k.a. diffusion models)

• Optimization for machine learning and statistics, particularly stochastic, nonsmooth, nonconvex and/or distributionally robust optimization algorithms

• The interplay between optimization and sampling

• High-dimensional statistical inference

• Estimation of high-dimensional covariance and precision matrices and its applications

Academic Services

Reviewer for

• Journals

  • IEEE Transactions on Signal Processing (2018—1, 2019—1, 2020—2)

  • SIAM Journal on Imaging Sciences (2021)

  • Neural Computation (2021)

  • IEEE Computational Intelligence Magazine (2022)

• Conferences

  • NeurIPS 2020, 2021, 2022, 2023

  • ICML 2021, 2022, 2023, 2024

  • ICLR 2021, 2022, 2024

  • AISTATS 2020, 2021, 2022, 2024