Mathematical Sciences
Our long history of research has had an enduring impact on computer science, operations research, and information theory. We’re currently focused on optimization, probability, complexity, geometry of data, as well as linear and multi-linear algebra, to deliver tools that are fundamental to big data and AI.
Our work
DOFramework: A testing framework for decision optimization model learners
Technical noteOrit DavidovichNew tensor algebra changes the rules of data analysis
ResearchLior Horesh7 minute readRalph Gomory receives the Vannevar Bush Award: The pioneer of applied math
NewsKatia Moskvitch10 minute readIBM-Stanford team’s solution of a longstanding problem could greatly boost AI
ResearchMark Squillante and Soumyadip Ghosh6 minute read
Publications
SPRIG: Stackelberg Perception-Reinforcement Learning with Internal Game Dynamics
- Fernando Martinez
- Juntao Chen
- et al.
- 2025
- AAAI 2025
Higher Order Graph Attention Probabilistic Walk Networks
- Thomas Bailie
- Yun Singh Koh
- et al.
- 2025
- AAAI 2025
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms
- Weiqin Chen
- Mark Squillante
- et al.
- 2025
- AAAI 2025
Spline Quantile Regression
- Ta-hsin Li
- Nimrod Megiddo
- 2025
- arXiv
Sequential uncertainty quantification with contextual tensors for social targeting
- 2024
- KAIS
Throughput-Optimal Scheduling via Rate Learning
- Panagiotis Promponas
- Victor Valls
- et al.
- 2024
- CDC 2024