Selected Publications can be found here.

Invited Reviewer, Area Chair (AC), and PC Member for Conference:
• International Conference on Learning Representations: ICLR 2024, ICLR 2022, ICLR 2021
• International Conference on Machine Learning: ICML 2022, ICML 2021, and ICML 2020
• Conference on Neural Information Processing Systems: NeurIPS 2022, NeurIPS 2021, NeurIPS 2020, and NeurIPS 2019
• Conference on Learning Theory: COLT 2024, COLT 2022, COLT 2021
• AAAI Conference on Artificial Intelligence: AAAI 2020 and AAAI 2019
• Conference on Uncertainty in Artificial Intelligence: UAI 2019

Invited Reviewer for Journal:
• Journal of Machine Learning Research (JMLR)
• Annals of Statistics (Ann. Stat.)
• Neural Computation (MIT press)
• Neural Networks (Elsevier)
• IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS)
• Constructive Approximations

Invited research visits:
• Microsoft Research (MSR), Redmond, USA, Summer 2018.
• TTIC, Chicago, USA, Fall 2019.
• University of Cambridge, England, Fall 2021.

Seminar, university, and research lab:
• Harvard University / Interdisciplinary Science Seminar, 2022.
• Harvard University / Special talk on Deep Learning, invited by Professor Jun Liu, 2021.
• Brown University / CRUNCH seminar, invited by Professor George Em Karniadakis, 2021.
• Max Planck Institute + UCLA / Math Machine Learning seminar, 2020.
• University of Michigan, Ann Arbor / Seminar, 2020.
• Brown University / Seminar, 2020.
• National University of Singapore / Seminar, 2020.
• University of British Columbia / Seminar, 2020.
• Stanford University / CS theory lunch, 2019.
• Harvard University / Professor Horng-Tzer Yau lab, 2019.
• Carnegie Mellon University (CMU) / AI Seminar Series, 2019.
• Carnegie Mellon University (CMU) / Professor Eric P. Xing lab, 2019.
• Toyota Technological Institute at Chicago (TTIC) / Young Researcher Seminar Series, 2019.
• Purdue University / Seminar at School of Industrial Engineering, 2019.
• PhILMs center / invited by Professor George Em Karniadakis at Brown University, 2019.
• Google Research (at Cambridge) / invited by Dr. Dilip Krishnan (Research Scientist at Google), 2017.
• MIT / Professor David Sontag lab, 2017.
• MIT / Professor Tomaso Poggio lab, 2016.
• MIT / Machine Learning Tea, 2016.

Invited talk at International Conference:
• Workshop on Machine Learning Theory and Foundations, Microsoft Research Asia, 2023, China.
• NeurIPS 2022 workshop on Interpolation Regularizers and Beyond, NeurIPS 2022, Canada.
• Minisymposium on Theoretical Foundations of Deep Learning, ICIAM 2019, Spain.

Harvard University                                         2020-2021
Postdoctoral fellow
Mentor: Horng-Tzer Yau, Department of Mathematics

Massachusetts Institute of Technology              2020
Ph.D., Computer Science
Advisor: Leslie Pack Kaelbling
Thesis committee: Yoshua Bengio and Suvrit Sra

Massachusetts Institute of Technology               2016
S.M., Electrical Engineering and Computer Science
Advisors: Leslie Pack Kaelbling and Tomás Lozano-Pérez

Everyone is free to use, modify, distribute, and redistribute all of my following codes for any purpose, including commercial purposes, without contacting me.

• Bayesian optimization with exponential convergence
• Generalization in Deep Learning
• Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization