Eileen Koski

Vibha Anand

Jianying Hu

Anthony Annunziata

Solomon Assefa

Laura-Jayne Gardiner

Pin-Yu Chen

Payel Das

Michal Rosen-Zvi

Jason Crain

Samuel Hoffman

 Vijil Chenthamarakshan

Kahini Wadhawan

Roger Traub

Anna Paola Carrieri

Graeme Macluskie

Bum Chul (BC) Kwon

Ajay Royyuru

William Ogallo

Kommy Weldemariam

News

Research

Deep Dive

Why now is the time to accelerate discoveries in health care

Computer simulations identify new ways to boost the skin’s natural protectors

Accelerating molecular optimization with AI

How we react to smells could unlock how we form conscious thoughts

New AI research could help find the best treatment for bowel disease

IBM continues advancing disease progression modeling and biomarkers research using the latest in AI

IBM is partnering with the Oxford Pandemic Sciences Institute

Featured

Research leads IBM’s response to COVID-19

Science

ACS Fall 2022

KDD 2022

MICCAI 2022

Trustworthy AI

Trustworthy Generation

Physical Sciences

Life Sciences

Materials Discovery

Healthcare

Impact Science

Explainable AI

Accelerated Discovery

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

It can take over 10 years to come up with new materials. At IBM Research, we’re looking to accelerate the discovery process using new AI methods, robotics, the hybrid cloud, and quantum computers. Our goal is to unlock new properties and materials to address global challenges in years not decades.

The demand for virtual agents that can handle customer needs has continued to increase dramatically. At IBM Research, we’re building the next generation of artificial intelligence systems that can understand what’s being asked of them and how best to respond as efficiently as possible.

Conversational AI

To trust AI systems, explanations can go a long way. We’re creating tools to help debug AI, where systems can explain what they’re doing. This includes training highly optimized, directly interpretable models, as well as explanations of black-box models and visualizations of neural network information flows.

We’re creating tools to help AI systems explain why they made the decisions they did. 

Biases can lead to systematic disadvantages for marginalized individuals and groups — and they can arise in any point in the AI development lifecycle. To increase the accountability of high-risk AI systems, we're developing technologies to increase their end-to-end transparency and fairness.

Fairness, Accountability, Transparency

We’re developing technologies to increase the end-to-end transparency and fairness of AI systems. 

Code for an efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder.

CLaSS: Controlled Latent attribute Space Sampling

A visual platform for exploring the peptide space, modeled by our state-of-the-art peptide autoencoder.

Peptide Walker

Animate gif with that transitions among of images derived using a generative adversarial network (GAN) trained on images of viruses and molecules.

Accelerating-molecular-optimization-with-AI_01.gif

How we react to smells could unlock how we form conscious thoughts.jpg

New AI research could help explain the best treatment for bowel disease symptoms