Justin Whitehouse

I am a postdoc at Stanford University, where I am fortunate to work with Vasilis Syrgkanis and Ramesh Johari. In my postdoc, I am mainly working on problems at the intersection of causal inference and machine learning. I am also interested in problems in anytime-valid statistical inference.

I recently graduated from Carnegie Mellon University, where I recieved my PhD in computer science. I was advised by Aaditya Ramdas and Steven Wu.

Before starting my PhD, I was an undergraduate at Columbia University in New York City. There, I majored in mathematics and computer science. I was fortunate enough to be advised by Allison Bishop and Suman Jana .

Publications and Preprints

Mean Estimation in Banach Spaces Under Infinite Variance and Martingale Dependence (with Ben Chugg, Diego Martinez Taboada, and Aaditya Ramdas). Arxiv Preprint, 2024.
Orthogonal Causal Calibration (with Vasilis Syrgkanis, Christopher Jung, Bryan Wilder, and Steven Wu). Arxiv Preprint, 2024.
Multi-Armed Bandits with Network Interference (with Abhineet Agarwal, Anish Agarwal, and Lorenzo Masoero). Neurips, 2024.
Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (with Aaditya Ramdas and Steven Wu).
In Submission.
On the Sublinear Regret of GP-UCB (with Aaditya Ramdas and Steven Wu).
Neurips, 2023.
Adaptive Principal Component Regression with Applications to Panel Data (with Anish Agarwal, Keegan Harris, and Steven Wu).
Neurips, 2023.
Fully-Adaptive Composition in Differential Privacy (with Aaditya Ramdas, Steven Wu, and Ryan Rogers).
ICML, 2023.
Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints (with Aaditya Ramdas, Steven Wu, and Ryan Rogers).
Neurips, 2022.
The Case for Phase-Aware Scheduling of Parallelizable Jobs (with Benjamin Berg, Benjamin Moseley, Mor Harchol-Balter, and Weina Wang).
39th International Symposium on Computer Performance, Modeling, Measurements and Evaluation, 2021.
Optimal Resource Allocation for Elastic and Inelastic Jobs (with Benjamin Berg, Benjamin Moseley, Mor Harchol-Balter, and Weina Wang).
ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2020).
Bringing Engineering Rigor to Deep Learning (with Kexin Pei, Shiqi Wang, Yuchi Tian, Carl Vondrick, Yinzhi Cao, Baishakhi Ray, Suman Jana, and Junfen Yang).
ACM SIGOPS Operating Systems Review, Volume 53 Issue 1 (SIGOPS 2019).
Efficient Formal Safety Analysis of Neural Networks (with Shiqi Wang, Suman Jana, Kexin Pei, and Junfeng Yang).
Neurips, 2018.
Formal Security Analysis of Neural Networks Using Symbolic Intervals (with Shiqi Wang, Suman Jana, Kexin Pei, and Junfeng Yang).
27th USENIX Security Symposium, 2018.

Teaching

Graduate Algorithms (Spring 2022, CMU).
Foundations of Privacy (Fall 2021, CMU).
Computer Science Theory (Spring 2019, Columbia).
Modern Algebra II (Spring 2019, Columbia).
Complexity Theory (Fall 2018, Columbia).
Introduction to Cryptography (Fall 2018, Columbia).
Number Theory and Cryptography (Spring 2018, Columbia).