Justin Whitehouse

I am a rising fifth year PhD student in the Computer Science Department at Carnegie Mellon University. I am currently co-advised by Aaditya Ramdas and Steven Wu. On the theoretical side, I am interested in studying the growth of self-normalized processes in high or infinite-dimensional settings. Practically, I am interested in studying how self-normalized concentration can facilitate adaptive data analysis, with particular interest in applications in causal inference, online learning, and private machine learning (see papers below).

Before working in the above areas, I studied problems in stochastic scheduling and queueing with Mor Harchol-Balter and Weina Wang. In particular, we developed optimal algorithms for scheduling parallelizable jobs in multiserver systems.

Before coming to Carnegie Mellon, 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

Improved Self-Normalized Concentration in Hilbert Spaces: Sublinear Regret for GP-UCB (with Aaditya Ramdas and Steven Wu).
Arxiv Preprint.
Adaptive Principal Component Regression with Applications to Panel Data (with Anish Agarwal, Keegan Harris, and Steven Wu).
Arxiv Preprint.
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).