Yongxin Chen

 

Assistant Professor
School of Aerospace Engineering
Institute for Robotics and Intelligent Machines
Machine Learning Center
Georgia Institute of Technology

Address:
Guggenheim 448B
North Avenue, Atlanta, GA 30332

Email: yongchen@gatech.edu
Phone: 404-894-2765
URL: www.yongxin.ae.gatech.edu
Lab website: Foundations of Learning And Intelligent Robots (FLAIR) lab
Twitter: twitter.com/yongxinchen1

I am currently looking for motivated graduate students interested in Systems and control, Machine learning, Robotics, and Optimization. In particular, I am looking for one student who will be working on the intersection between machine learning and control theories this year. Solid background in math and/or coding is required. Applicants major in mathematics, statistics or automation are of high priority. If you are interested in working with me, please feel free to contact me. Visiting scholars are also welcome to contact me, but please be aware that the paperwork normally takes two months.

Biosketch

Yongxin Chen was born in Ganzhou, Jiangxi, China. He received his BSc in Mechanical Engineering from Shanghai Jiao Tong university, China, in 2011, and a Ph.D. degree in Mechanical Engineering, under the supervision of Tryphon Georgiou, from University of Minnesota in 2016. He is currently an Assistant Professor in the School of Aerospace Engineering at Georgia Institute of Technology. Before joining Georgia Tech, he had a one-year Research Fellowship in the Department of Medical Physics at Memorial Sloan Kettering Cancer Center with Allen Tannenbaum from 2016 to 2017 and was an Assistant Professor in the Department of Electrical and Computer Engineering at Iowa State University from 2017 to 2018. He received the George S. Axelby Best Paper Award (IEEE Transaction on Automatic Control) in 2017 for his joint work ‘‘Optimal steering of a linear stochastic system to a final probability distribution, Part I’’ with Tryphon Georgiou and Michele Pavon. He received the NSF CAREER Award in 2020, the Simons-Berkeley research fellowship in 2021, the A.V. ‘Bal’ Balakrishnan Award in 2021, and the Donald P. Eckman Award in 2022.

News

  • Aug 2022: New PhD students (ML program) Haotian Xue and Zishun Liu join our group. Welcome!

  • Jun 2022: I am honored to receive the Donald P. Eckman Award for Outstanding Young Engineer in the Field of Automatic Control. Thanks!

  • May 2022: New manuscript ‘‘A Proximal Algorithm for Sampling from Non-convex Potentials’’ is posted on arXiv. It achieves the best complexity bound for sampling from semi-smooth non-log-concave distributions. It also provides the first ever high accuracy guarantee for sampling in this regime!

  • May 2022: Rahul starts intern at Intel AI, Qinsheng starts intern at Nvidia, and Jiaojiao starts intern at Microsoft Research. Good luck!

  • May 2022: New PhD student (ROBO program) Utkarsh Mishra joins our group. Welcome!

  • May 2022: Our paper ‘‘Variational Wasserstein gradient flow’’ is accepted to ICML 2022. A scalable algorithm to compute Wasserstein gradient flow is developed.

  • May 2022: Our paper ‘‘Improved analysis for a proximal algorithm for sampling’’ is accepted to COLT 2022. It provides a beautiful convergence analysis for the proximal sampling algorithm.

  • Apr 2022: New manuscript ‘‘Fast Sampling of Diffusion Models with Exponential Integrator’’ is posted on arXiv. DEIS is the best fast sampling algorithm for diffusion models so far!

  • Mar 2022: Our paper ‘‘Inference with Aggregate Data in Probabilistic Graphical Models: An Optimal Transport Approach’’ is accepted by TAC. We develop a novel method based optimal transport for inference using aggregate observations.

  • Mar 2022: New manuscript ‘‘A Proximal Algorithm for Sampling’’ is posted on arXiv. It achieves the best complexity bound for sampling from semi-smooth log-concave distributions.

  • Feb 2022: Our paper ‘‘Path Integral Sampler: a stochastic control approach for sampling’’ is accepted to ICLR 2022. We develop a new method for sampling based on optimal control.

  • Feb 2022: Our paper ‘‘Data-driven Optimal Control of Nonlinear Dynamics under Safety Constraints’’ is accepted by IEEE Control System Letters. A dual approach to data-driven optimal control is proposed.

  • Jan 2022: Our paper ‘‘On the complexity of the optimal transport problem with graph-structured cost’’ is accepted to AISTATS 2022. New complexity bounds are derived for multi-marginal optimal transport with graphical structure.

  • Sep 2021: Our paper ‘‘Diffusion normalizing flow’’ is accepted to NeurIPS 2021. It achieves the best performance among normalizing flow models.

  • Sep 2021: Our paper ‘‘Learning Hidden Markov Models from Aggregate Observations’’ is accepted by Automatica. Our algorithm is able to learn a dynamical system based on aggregate observations.

  • May 2021: Our paper ‘‘Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks’’ is accepted to ICML 2021 as a long talk. A scalable algorithm for Wasserstein Barycenter is developed.

  • Apr 2021: Our paper ‘‘Optimal Transport in Systems and Control’’ is accepted by Annual Review of Control, Robotics, and Autonomous Systems. It is an introduction of optimal transport to the control community.

  • Mar 2021: Our paper ‘‘Stochastic Control Liaisons: Richard Sinkhorn Meets Gaspard Monge on a Schrodinger Bridge’’ is accepted by SIAM Review. It provides an overview of Optimal transport and the Schrodinger bridge problems from the perspective of stochastic control.

  • Mar 2020: I receive the NSF CAREER Award. Thanks!