Selected Publications can be found here.

Invited Conference reviewer:
• International Conference on Learning Representations: 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

Program Committee Member:
• Conference on Learning Theory: COLT 2022, COLT 2021
• AAAI Conference on Artificial Intelligence: AAAI 2020 and AAAI 2019
• Conference on Uncertainty in Artificial Intelligence: UAI 2019

Invited Journal reviewer:
• 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, Summer 2018.
• TTIC, Chicago, Fall 2019.
• University of Cambridge: Invited to be in the participant list of the program on "Mathematics of Deep Learning" by the organization team of Prof. Peter Bartlett, Prof. Arnulf Jentzen, Prof. Anders Hansen, Prof. Gitta Kutyniok, Prof. Stephane Mallat, and Prof. Carola Schönlieb.

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