Gautam Goel

I am a PhD student in the Computing and Mathematical Sciences (CMS) department at Caltech, where I am extremely fortunate to be advised by Babak Hassibi. I am broadly interested in machine learning, optimization, and control, especially 1) online learning and sequential decision-making under uncertainty, and 2) integrating machine learning with physics, dynamics and control. Much of my PhD work has been supported by a National Science Foundation Graduate Research Fellowship and an Amazon AWS AI Fellowship. In 2021 I was named a Rising Star in Data Science by the University of Chicago Center for Data and Computing.

I enjoy travelling, and have visited the following countries for conferences during my PhD: Australia (CDC 2017), Sweden (COLT 2018), Japan (AISTATS 2019), and Canada (NeurIPS 2019).

For more information, please see my CV.


ggoel [at] caltech [dot] edu

Gautam Goel
1200 E California Blvd
Pasadena, CA 91125


G. Goel, B. Hassibi. Regret-optimal measurement-feedback control. Preprint.

G. Goel, B. Hassibi. Regret-optimal control in dynamic environments. Preprint.

G. Goel, B. Hassibi. The Power of Linear Controllers in LQR Control. Preprint.

Y. Lin, G. Goel, A. Wierman. Online Optimization with Predictions and Non-convex Losses. Sigmetrics 2020.

G. Goel, Y. Lin, H. Sun, A. Wierman. Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization. NeurIPS 2019. Spotlight presentation (top 2.4% of submssions).

G. Goel, A. Wierman. An Online Algorithm for Smoothed Regression and LQR control. AISTATS 2019.

N. Chen, G. Goel, A. Wierman. Smoothed Online Convex Optimization in High Dimensions via Online Balanced Descent. Conference on Learning Theory (COLT) 2018.

G. Goel, N. Chen, A. Wierman. Thinking fast and slow: Optimization decomposition across timescales. Conference on Decision and Control (CDC) 2017.