Computational Sprinting: Architecture, Dynamics, and Strategies. - Seyed Majid Zahedi; Songchun Fan; Matthew Faw; Elijah Cole; Benjamin C. Lee (ACM Trans. Comput. Syst., 2017)

Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions. - Matthew Faw; Rajat Sen; Karthikeyan Shanmugam; Constantine Caramanis; Sanjay Shakkottai (NeurIPS, 2020)

Single-Sample Prophet Inequalities Revisited. - Constantine Caramanis; Matthew Faw; Orestis Papadigenopoulos; Emmanouil Pountourakis (CoRR, 2021)

The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance. - Matthew Faw; Isidoros Tziotis; Constantine Caramanis; Aryan Mokhtari; Sanjay Shakkottai; Rachel A. Ward (COLT, 2022)

Single-Sample Prophet Inequalities via Greedy-Ordered Selection. - Constantine Caramanis; Paul Dütting; Matthew Faw; Federico Fusco; Philip Lazos; Stefano Leonardi 001; Orestis Papadigenopoulos; Emmanouil Pountourakis; Rebecca Reiffenhäuser (SODA, 2022)

Learning To Maximize Welfare with a Reusable Resource. - Matthew Faw; Orestis Papadigenopoulos; Constantine Caramanis; Sanjay Shakkottai (Proc. ACM Meas. Anal. Comput. Syst., 2022)

Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD. - Matthew Faw; Litu Rout; Constantine Caramanis; Sanjay Shakkottai (COLT, 2023)

In-Context Fine-Tuning for Time-Series Foundation Models. - Abhimanyu Das; Matthew Faw; Rajat Sen; Yichen Zhou (CoRR, 2024)

On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback. - Matthew Faw; Constantine Caramanis; Jessica Hoffmann (CoRR, 2025)