# Rebecca Roelofs

I am a fifth year Ph.D. student in Computer Science at U.C. Berkeley. I am coadvised by Benjamin Recht and Jim Demmel.

In 2016, I was an intern at Google Brain in NYC, working with Vikas Sindhwani.

I graduated from Swarthmore College in 2013 with a B.A. in Computer Science and a B.S.E. in Engineering. While there, I conducted research with Matt Zucker and Tali Moreshet.

My research interests include first order optimization methods for machine learning, generalization, large scale iterative solvers, and numerical linear algebra.

** Publications **

Wilson, A. C., Roelofs, R., Stern, M., Srebro, N., & Recht, B. "The Marginal Value of Adaptive Gradient Methods in Machine Learning." NIPS, 2017. [paper, slides, poster]

Sindhwani, Vikas, Rebecca Roelofs, and Mrinal Kalakrishnan. "Sequential operator splitting for constrained nonlinear optimal control." American Control Conference (ACC), 2017. IEEE, 2017.

Tu, S., Roelofs, R., Venkataraman, S., & Recht, B. (2016). Large scale kernel learning using block coordinate descent. arXiv preprint arXiv:1602.05310.

Stromme, A., Sutherland, D. J., Felt, N., Burka, A., Lipton, B., Roelofs, R., ... & Welkie, A. (2012, December). Managing User Requests With the Grand Unified Task System (GUTS). In LISA (pp. 101-110).

** Teaching **

In Spring 2016, I was a TA for EE227C: Convex Optimization for Modern Data Analysis taught by Benjamin Recht.

In Fall 2016, I was the head TA for CS188: Introduction to Artificial Intelligence taught by Josh Hug.

** Email **

roelofs@cs.berkeley.edu