---

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 online decision-making; 2) integrating machine learning with physics, dynamics and control; and 3) beyond-worst-case analysis of machine learning algorithms. Much of my PhD work has been supported by a National Science Foundation Graduate Research Fellowship and an Amazon AWS AI Fellowship. Prior to joining Caltech, I obtained a BS in Mathematics from Georgia Tech.

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 91106

---

Publications

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. Advances in Neural Information Processing Systems (2019).

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. 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

---