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 ESA open-source TerraMind, the best performing generative AI model for Earth observation
ReleaseAshley PetersonIBM 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 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
CIRCUITSYNTH-RL: LLM-Based Circuit Topology Synthesis with RL Refinement
- Prashanth Vijayaraghavan
- Luyao Shi
- et al.
- 2025
- DAC 2025
Guardrails in generative AI workflows via orchestration
- Gaurav Kumbhat
- Evaline Ju
- 2025
- ODSC East 2025
POKE: A Compact and Efficient PKE from Higher-dimensional Isogenies
- Andrea Basso
- Luciano Maino
- 2025
- Eurocrypt 2025
The Literary Canons of Large-Language Models: An Exploration of the Frequency of Novel and Author Generations Across Gender, Race and Ethnicity, and Nationality
- Paulina Toro Isaza
- Nalani Kopp
- 2025
- NAACL 2025
ASTER: Natural and Multi-language Unit Test Generation with LLMs
- Rangeet Pan
- Myeongsoo Kim
- et al.
- 2025
- ICSE 2025