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, signal processing, and control, especially 1) online optimization and sequential decision-making under uncertainty, and 2) integrating ideas from machine learning with signal processing and control. Much of my PhD work has been supported by a National Science Foundation Graduate Research Fellowship and an Amazon AI4Science 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.


Contact

ggoel [at] caltech [dot] edu

Gautam Goel
Caltech
1200 E California Blvd
Pasadena, CA 91125


Reviewing

I have reviewed papers for NeurIPS, Transactions on Automatic Control, Transactions on Networking, and Transactions on Information Theory.


Publications

For citation information, please see my Google Scholar profile . (*) denotes equal contribution.

G. Goel, B. Hassibi. Competitive Control. Preprint.

G. Goel, B. Hassibi. Regret-optimal Estimation and Control. Preprint.

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

G. Goel, B. Hassibi. Regret-optimal measurement-feedback control. Learning for Dynamics and Control (L4DC) 2021.

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.