DOFramework: A testing framework for decision optimization model learners
Technical note
Orit Davidovich
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
New 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
POKE: A Compact and Efficient PKE from Higher-dimensional Isogenies
- Andrea Basso
- Luciano Maino
- 2025
- Eurocrypt 2025
Integer Programming Based Methods and Heuristics for Causal Graph Learning
- Sanjeeb Dash
- Joao Goncalves
- et al.
- 2025
- AISTATS 2025
Hybrid Bayesian Optimization with DIRECT
- Hongsheng Liu
- Dzung Phan
- 2025
- SDM 2025
Convergence-Guaranteed Elastic Net Graphical Model Estimation with Applications to Anomaly Localization
- Dzung Phan
- Matt Menickelly
- et al.
- 2025
- SDM 2025
Dense Associative Memory with Epanechnikov energy
- Benjamin Hoover
- Krishnakumar Balasubramanian
- et al.
- 2025
- ICLR 2025
Dense Associative Memories and its Role in Machine Learning
- 2025
- ICLR 2025