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 note
Orit Davidovich
02 Feb 2023
New tensor algebra changes the rules of data analysis
Research
Lior Horesh
08 Jul 2021
7 minute read
Ralph Gomory receives the Vannevar Bush Award: The pioneer of applied math
News
Katia Moskvitch
18 May 2021
10 minute read
- Mathematical Sciences
- Quantum
IBM-Stanford team’s solution of a longstanding problem could greatly boost AI
Research
Mark Squillante and Soumyadip Ghosh
16 Dec 2020
6 minute read
- Mathematical Sciences
- Neuro-symbolic AI

Publications

Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions
- - Ning Yang
  - Yuhai Tu
  - et al.
- 2023
- APS March Meeting 2023
When Machine Learning meets Dynamical Systems: Theory and Applications
- - Lam Nguyen
  - Trang H. Tran
  - et al.
- 2023
- AAAI 2023
Knowledge-augmented Risk Assessment (KaRA): a hybrid-intelligence framework for supporting knowledge-intensive risk assessment of prospect candidate
- - Carlos Raoni De Alencar Mendes
  - Emilio Ashton Vital Brazil
  - et al.
- 2023
- AAAI 2023
Optimization with Constraint Learning
- - S. Ilker Birbil
  - Donato Maragno
  - et al.
- 2023
- AAAI 2023
Detection of Unobserved Common Cause in Discrete Data Based on the MDL Principle
- - Masatoshi Kobayashi
  - Kohei Miyaguchi
  - et al.
- 2022
- Big Data 2022
Distributionally Robust Optimization for Input Model Uncertainty in Simulation-Based Decision Making
- - Soumyadip Ghosh
  - Mark Squillante
- 2022
- WSC 2022

View all publications

Related topics

Computer Science

Physical Sciences

Quantum Information Science

Cryptography