COOL Autonomy Lab

The Control, Optimization, and Online Learning (COOL) for Autonomy lab at the University of Texas, Austin focuses on developing advanced real-time decision-making strategies for autonomy to complement humans in performing complex tasks. We are very interested in any opportunities to educate and train the next generation of scientists, researchers, and engineers in this rapidy advancing field. Together as a team we are definitely COOLer, have more fun, and can do much better. Our lab members.

Hiring! We are always looking for motivated students with strong background on control, optimization, and machine learning. If you are interested, send us an email with your CV and a few paragraphs on your motivations and how you can contribute to our Lab.

Recent News

» Thinh’s paper on fast two-time-scale stochastic approximation was accepted to IEEE Transactions on Automatic Control. ✍

» Congratulations, Dr. Amit Dutta, for successfully defending his Ph.D. thesis in ECE at Virginia Tech. ✌

» Congratulations, Duy Anh Do and Hunter Ellis, for successfully defending their M.S. thesis in ECE at Virginia Tech. ✌

» Thinh is elevated to be an IEEE Senior Member! ✌

» Our newest work, leaded by Sihan Zeng at JPMorgan AI Research, on the optimal complexity of multi-time-scale stochastic approximation is up. ✍

» Our paper on developing a new fast two-time-scale stochastic gradient method with an optimal convergence rate, joint work with Sihan Zeng (JP Morgan AI Research), was accepted to the Conference on Learning Theory (COLT). ✍

» Our paper, leaded by our PhD student Amit Dutta, on exact fault-tolerance in Byzantine federated learning is up. ✍

» Our lab is honored to receive an NSF CAREER Award. ✌

» Our lab is honored to receive the AFOSR Young Investigator Program (YIP) award. ✌

Contact Information

Office: ASE 3.206 Aerospace Engineering Building 2617 Wichita Street, Austin, Texas 78712-1221

Email: thinhdoan (at) utexas.edu.

Phone: ‭+1 (512) 471-7593‬