Research
Professor Moseley’s research spans operations research, theoretical computer science, and machine learning.
He designs and analyzes algorithms for optimization, with interests in online and stochastic decision-making, scheduling, clustering, and large-scale computation.
A central focus of his recent work is learning-augmented algorithms, which use data-driven predictions to improve performance while retaining provable guarantees when predictions are inaccurate.
Browse publications MPC lecture notesResearch recognition
- Best Paper Awards
- FOCS 2026 · IPDPS 2015 · SPAA 2013
- Best Student Paper Award
- SODA 2010
- Oral presentations
- NeurIPS 2021 · NIPS 2017Additional NeurIPS spotlight presentations
- NSF CAREER Award
- Project details
His research has been supported by the National Science Foundation, the Office of Naval Research, Google, Yahoo, Infor, and Bosch.