Exploratory Science
Our researchers seek to answer some of the most fundamental questions. We strive for a greater understanding of how our world works and how we can harness that knowledge to advance science, mathematics, and computation.
Our work
IBM and Université de Sherbrooke announce two quantum research chairs
Q & AAlexandre ChoquetteAccelerating discoveries with new AI tools, powerful chips — and turkey
ExplainerMike Murphy, Peter Hess, and Kim MartineauFor LLMs, IBM’s NorthPole chip overcomes the tradeoff between speed and efficiency
ResearchPeter HessTeaching AI models to improve themselves
ResearchPeter HessMeet AI-Hilbert, a new algorithm for transforming scientific discovery
NewsPeter HessRemembering Bob Dennard, inventor of the chip that changed the world
NewsPeter Hess- See more of our work on Exploratory Science
Topics
- Computer ScienceWe’re working on automation, information processing, and computation research to complement and extend human performance and advance society.
- Mathematical SciencesWe’re focused on foundational mathematical research with the aim of delivering tools to that are fundamental to big data and AI.
- Physical SciencesWe’re using physics to improve AI algorithms and interpretability, as well as novel materials for new computational platforms.
- Responsible TechnologyTechnology impacts every aspect of our work and lives. It is imperative that technology is built responsibly to reduce differential effects on various populations. At IBM Research, we are working to ensure that the technologies we create promote beneficial outcomes for our clients and for the world.
Publications
MDLab: AI frameworks for Carbon Capture and Battery Materials
- Bruce Elmegreen
- Hendrik Hamann
- et al.
- 2025
- Frontiers in Environmental Science
POKE: A Compact and Efficient PKE from Higher-dimensional Isogenies
- Andrea Basso
- Luciano Maino
- 2025
- Eurocrypt 2025
Dense Associative Memory with Epanechnikov energy
- Benjamin Hoover
- Krishnakumar Balasubramanian
- et al.
- 2025
- ICLR 2025
A new framework for evaluating model out-of-distribution generalisation for the biochemical domain
- 2025
- ICLR 2025
The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG
- Francesco Carzaniga
- Gary Hoppeler
- et al.
- 2025
- ICLR 2025
Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior
- Anming Gu
- Edward Chien
- et al.
- 2025
- ICLR 2025