Sufi Zafar

Eileen Koski

Kenney Ng

Vibha Anand

Anna Weber

Jannis Born

María Rodríguez Martínez

Kristen Severson

Soumyadip Ghosh

Jianying Hu

Pablo Meyer

News

Research

Researchers use AI to better spot risk factors of Type 1 diabetes

Neural networks take TITAN-sized step toward T cell specificity prediction

IBM and The Michael J. Fox Foundation use AI to help predict progression of Parkinson’s disease

Crowdsourcing to trace cell lineages

NanoDx licenses IBM CMOS-compatible nanosensors for rapid COVID-19 testing tech

Featured

Research leads IBM’s response to COVID-19

Science

KDD 2021

ICWS 2021

IJCAI 2021

Machine Learning

Life Sciences

Hardware Technology

Healthcare

Impact Science

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.

Materials Discovery

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.

Explainable AI

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

Researchers use AI to better spot-risk factors of Type 1 diabetes

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https://research-website.bx.cloud9.ibm.com/blog/t-cell-prediction

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DREAM AI competition helps explain how cells turn into specialists, and where the process can go awry. (Credit: Irepan Salvador)

Healthcare and Life Sciences

Our work

Crowdsourcing to trace cell lineages

IBM and The Michael J. Fox Foundation use AI to help predict progression of Parkinson’s disease

Neural networks take TITAN-sized step toward T cell specificity prediction

NanoDx licenses IBM CMOS-compatible nanosensors for rapid COVID-19 testing tech

Tools + code

CLaSS: Controlled Latent attribute Space Sampling

Peptide Walker

Publications

Software Services Engineering Manifesto – A Cross-Cutting Declaration

Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

MEDTO: Medical Data to Ontology Matching Using Hybrid Graph Neural Networks

Applied Data Science for Healthcare

Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks

Research leads IBM’s response to COVID-19

Related teams

Explainable AI

Fairness, Accountability, Transparency

Materials Discovery

Conversational AI