Yatong Bai
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Ph.D. Candidate University of California, Berkeley Advisor: Somayeh Sojoudi Email: yatong_bai (at) berkeley.edu |
About
I am a fourth-year Ph.D. candidate at the mechanical engineering department of UC Berkeley, advised by Professor Somayeh Sojoudi.
Prior to joining Berkeley, I obtained two B.Sc. degrees in computer engineering and mechanical engineering from Georgia Institute of Technology, where I researched with Professors Julien Meaud and Thomas Conte.
I have interned at Microsoft, Scale AI, Honda Aircraft Company, and Tesla, Inc.
Research interests
My interests include generative models (particulaly audio), robust deep learning, (convex) optimization, and controls.
Specifically, I enjoy working on ensuring the adversarial robustness of neural networks, addressing the "accuracy-robustness trade-off", and accelerating their training via theoretically disciplined methods, while still maintaining their impressive performances.
I also strive to accelerate audio generation models while preserving their generation quality and diversity.
News
March 2024: Our paper Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing has been accepted to the SIAM Journal on Mathematics of Data Science (SIMODS).
March 2024: I served as a reviewer for the ICML 2024 conference.
February 2024: We have open-sourced the training and evaluation code of the paper “Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation” at github.com/Bai-YT/ConsistencyTTA. The model checkpoints are shared on Huggingface.
February 2024: New preprint paper “MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers” by Yatong Bai, Mo Zhou, Vishal M. Patel, and Somayeh Sojoudi. Project code github.com/Bai-YT/MixedNUTS.
February 2024: I prepared summarization notes about Nonlinear Optimization for Professor Javad Lavaei's IEOR 160 course and made it publicly available on this website.
October 2023: I served as a reviewer for the ICLR 2024 conference.
September 2023: New preprint paper “Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation” by Yatong Bai, Trung Dang, Dung Tran, Kazuhito Koishida, and Somayeh Sojoudi. This work accelerates text-to-audio generation by up to 400x. Project website consistency-tta.github.io.
August 2023: I served as a Graduate Student Instructor (GSI) for IEOR 160: Nonlinear and Discrete Optimization in the Fall 2023 semester.
July 2023: Our paper Initial State Interventions for Deconfounded Imitation Learning has been accepted to the IEEE Conference on Decision and Control (CDC).
June 2023: I served as a reviewer for the NeurIPS 2023 conference.
April 2023: I served as a reviewer for the 2023 IEEE Conference on Decision and Control (CDC).
February 2023: I served as a reviewer for the ICML 2023 and IEEE CCTA 2023 conferences.
January 2023: New preprint paper “Improving the Accuracy-Robustness Trade-Off of Classifiers via Local Adaptive Smoothing” by Yatong Bai, Brendon G. Anderson, Aerin Kim, and Somayeh Sojoudi.
January 2023: New preprint paper “Let's Go Shopping (LGS) — Web-scale Image-text Dataset for Visual Concept Understanding” by Yatong Bai, Utsav Garg, Erhan Bas, Isidora Filipovic, Apaar Shanker, Haoming Zhang, Samyak Parajuli, Amelia N. Chu, Eugenia D Fomitcheva, Elliot Branson, Aerin Kim, Somayeh Sojoudi, and Kyunghyun Cho.
December 2022: I will join Microsoft Applied Science as a Research Intern in May 2023. I will be working with Kazuhito Koishida.
December 2022: I presented our poster on convex neural network training at the year-end TBSI Symposium.
October 2022: Our paper “Efficient Global Optimization of Two-layer ReLU Networks: Adversarial Training and Quadratic-time Algorithms” has been accepted to the SIAM Journal on Mathematics of Data Science (SIMODS).
August 2022: I am serving as a Graduate Student Instructor (GSI) for IEOR 160: Nonlinear and Discrete Optimization of the Fall 2022 semester.
May 2022: I have joined Scale AI as a Machine Learning Research Intern. I will be working on a bi-modal dataset project with Aerin Kim.
May 2022: I have passed the Qualifying Examination and advanced to candidacy as a Ph.D. candidate.
April 2022: I served as a reviewer for the 2022 IEEE Conference on Decision and Control (CDC).
January 2022: Our paper “Practical Convex Formulation of Robust Two-layer Neural Network Training” has been accepted to the American Control Conference of 2022.
January 2022: I am serving as a Graduate Student Instructor (GSI) for IEOR 160: Nonlinear and Discrete Optimization of the Spring 2022 semester.
December 2021: New paper “Efficient Global Optimization of Two-layer ReLU Networks: Adversarial Training and Quadratic-time Algorithms” by Yatong bai, Tanmay Gautam, and Somayeh Sojoudi.
October 2021: I gave a talk on convex neural network adversarial training at INFORMS 2021.
October 2021: The paper “Efficient Global Optimization of Two-layer ReLU Networks: Adversarial Training and Quadratic-time Algorithms” received the 2021 INFORMS Data Mining Best Student Paper Award Runner-Up (2nd out of 48 papers).
August 2021: I gave a talk on convex neural network adversarial training at MOPTA 2021.
August 2021: I have passed the Preliminary Examination.
April 2021: I received the ECE Senior Scholar Award from the Roger P. Webb Awards Program at Georgia Tech.
December 2020: New paper on convex neural network adversarial training: “Practical Convex Formulation of Robust Two-layer Neural Network Training” by Yatong Bai, Tanmay Gautam, Yu Gai, and Somayeh Sojoudi.
August 2020: I have joined UC Berkeley as a Ph.D. student.
August 2020: I have graduated from Georgia Institute of Technology with B. S. degrees in Computer Engineering and Mechanical Engineering with a cumulative GPA of 4.00 out of 4.00.