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:

- Online, streaming, and dynamic graph algorithms, possibly under privacy/Lipschitz constraints.
- Learning algorithms for coarsened/truncated data, possibly under privacy/replicability constraints.
- Statistical/computational connections between different notions of stability.
- Fair allocation in cooperative combinatorial optimization games.

**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

My other half, Jane Shi, studies number theory at MIT.

Previously, I was an undergraduate student at the University of Waterloo, where I was fortunate to be advised by Jochen Koenemann and Chaitanya Swamy. I worked on combinatorial optimization, approximation algorithms, and algorithmic game theory.

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.

On the Computational Landscape of Replicable Learning with Alkis Kalavasis, Amin Karbasi, Grigoris Velegkas [preprint]

Pointwise Lipschitz Continuous Graph Algorithms via Proximal Gradient Analysis with Quanquan C. Liu, Grigoris Velegkas, Yuichi Yoshida [preprint] [slides]

Replicable Learning of Large-Margin Halfspaces
with Alkis Kalavasis, Amin Karbasi, Kasper Green Larsen, Grigoris Velegkas
to appear in *ICML, 2024*.
**Spotlight**
(top 3.5% of accepted papers)
[preprint]

Replicability in Reinforcement Learning
with Amin Karbasi, Grigoris Velegkas, Ling F. Yang
*NeurIPS, 2023*.
[preprint]
[video]

Replicable Clustering
with Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas
*NeurIPS, 2023*.
[preprint]
[slides]
[video]

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.

- Math 627: Probability Theory
- CPSC 516: Algorithms via Convex Optimization
- Intro to Stochastic Calculus
- Intro to Manifolds
- Intro to Differential Geometry (in progress)

- Math 247: Calculus III (Advanced)
- PMath 340: Number Theory
- PMath 347: Groups & Rings
- PMath 351: Real Analysis
- PMath 450/650: Lebesgue Integration & Fourier Analysis
- PMath 453/753: Functional Analysis

- CS 246E: Object-Oriented Programming (Advanced)
- CS 341: Intro to Algorithms
- CS 350: Operating Systems
- CS 365: Models of Computation (Advanced)
- CS 466/666: Algorithm Design & Analysis
- CS 480/680: Intro to Machine Learning
- CS 467/667: Intro to Quantum Information Processing
- CS 762: Graph-Theoretic Algorithms