I am a 2nd year CS PhD student at Yale University, where I am fortunate to be advised by Quanquan C. Liu.
Research Interests: I am broadly interested in the theory and practice of algorithms for large data and different notions of algorithmic stability. Examples include graph algorithms beyond the static setting and learning algorithms beyond the i.i.d. assumption. I am currently thinking about problems in the following topics:
Email: felix [dot] zhou [at] yale [dot] edu
My amazing collaborators (in no particular order): Quanquan C. Liu, Grigoris Velegkas, Yuichi Yoshida, Tamalika Mukherjee, Alessandro Epasto, Alkis Kalavasis, Anay Mehrotra, Kasper Green Larsen, Amin Karbasi, Lin F. Yang, Vahab Mirrokni, Chaitanya Swamy, Jochen Koenemann, W. Justin Toth
Personal: My other half, Jane Shi, studies number theory at MIT.I interned at Hudson River Trading as an algorithm developer. Previously, I interned at HomeX, where I worked on an online stochastic reservation problem. Even earlier, I interned at the Google Mountain View office, where I worked on distributed graph algorithms.
Replicable Learning of Large-Margin Halfspaces with Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas
Replicability in Reinforcement Learning with Amin Karbasi, Grigoris Velegkas, Ling F. Yang NeurIPS, 2023. [preprint]
Replicable Clustering with Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas NeurIPS, 2023. [preprint] [slides]
On the Complexity of Nucleolus Computation for Bipartite b-Matching Games with Jochen Koenemann, Justin Toth SAGT, 2021. Special Issue [preprint] [slides] [video]
Notes typeset for courses and from self-studying. Errors are abundant. Please use at your own discretion.