Thilo Stöferle

Cristiano Malossi

Peter Hess

Alexandre Choquette

Stanisław Woźniak

Hironobu Takagi

Masaki Kuribayashi

Seita Kayukawa

Chieko Asakawa

News

Research

Q & A

Silicon waveguides move us closer to faster computers that use light

Biologically-inspired deep learning predicts chords of Bach

NeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI

Remembering Bob Dennard, inventor of the chip that changed the world

Teaching AI models to improve themselves 

IBM, Université de Sherbrooke announce two quantum research chairs

New smartphone app to navigate blind people to stand in lines with distances

Neuro-inspired AI to optimize learning and computing efficiency of next generation AI.

Neuromorphic Computing

ACS Spring 2025

ICASSP 2025

IWCT 2025

Hybrid Cloud

Trustworthy AI

Trustworthy Generation

Automated AI

Computer Vision

Human-Centered AI

Natural Language Processing

Scaling AI

Generative AI

Exploratory Science

Computer Science

Quantum Community

Hardware Technology

Semiconductors

Science

AI Hardware

Explainable AI

IBM has been at the forefront of computer science research since the field’s inception. Our research today focuses on achieving breakthroughs in automation, information processing, and computation. Our goal is to complement and extend human performance — and advance society as a whole. 

The combination of increasingly powerful computers and AI offers the possibility to be able to detect, diagnose, and cure diseases like never before. At IBM Research, we’re working on creating software and AI systems that can convert reams of health data into useable information for clinicians the world over.

Healthcare and Life Sciences

We're building systems that can convert reams of health data into useable information for clinicians the world over.

AI systems are proliferating in everyday life, and it’s imperative to understand those systems from a human perspective. We design and investigate new forms of human-AI interactions and experiences that enhance and extend human capabilities for the good of our products, clients, and society at large. 

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.

Mathematical Sciences

We’re focused on foundational mathematical research with the aim of delivering tools to that are fundamental to big data and AI.

Our physical sciences researchers seek to understand materials and their properties through advanced characterization methods, explore novel devices and materials for new computational platforms, and apply physics to improve AI algorithms and interpretability.



Physical Sciences

We’re using physics to improve AI algorithms and interpretability, as well as novel materials for new computational platforms. 


Darío Gil stands before a photo of Bob Dennard at the memorial event for the late IBM engineer

IBM Research scientists are working on different approaches to teach AI models how to improve themselves

Computer Science

Our work

IBM, Université de Sherbrooke announce two quantum research chairs

Teaching AI models to improve themselves

Remembering Bob Dennard, inventor of the chip that changed the world

New smartphone app to navigate blind people to stand in lines with distances

Silicon waveguides move us closer to faster computers that use light

Biologically-inspired deep learning predicts chords of Bach

NeuNetS: Automating Neural Network Model Synthesis for Broader Adoption of AI

Projects

Neuromorphic Computing

Publications

Contextual Value Alignment

Combinatorial Test Design Model Creation using Large Language Models

Uncertainty Analysis of Molecular Quantum Properties Prediction Using Chemical Foundation Models

Comparative Study of Open-source LLMs for text classification and knowledge extraction in the Material Discovery Domain

User centered approach of applicability domain analysis for improving AI-assisted decision-making

LionHeart: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

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