Yishai Shimoni

Ehud Karavani

Michal Rosen-Zvi

Research

Release

Finding new uses for drugs with generative AI

Machine learning: From “best guess” to best data-based decisions

ECCV 2024

NeurIPS 2024

ISWC 2024

PyData Tel Aviv 2024

Trustworthy AI

Generative AI

Healthcare

Accelerated Discovery

Causality

Causal modeling is crucial to the effectiveness and trust of AI by ensuring that actions lead to intended outcomes. We study the inference of causal effects and relationships, as well as the application of causal thinking to out-of-distribution generalization, fairness, robustness, and explainability.

Even advanced AI systems can be vulnerable to adversarial attacks. We’re making tools to protect AI and certify its robustness, including quantifying the vulnerability of neural networks and designing new attacks to make better defenses. And we’re helping AI systems adhere to privacy requirements.

Adversarial Robustness and Privacy

We’re making tools to protect AI and certify its robustness, and helping AI systems adhere to privacy requirements.

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.

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. 

Moving ML from “best guess” to best data-based decisions

Causality

Our work

Machine learning: From “best guess” to best data-based decisions

Finding new uses for drugs with generative AI

Publications

The WHY in Business Processes: Discovery of Causal Execution Dependencies

Interventional Causal Discovery in a Mixture of DAGs

Single-Microglia Transcriptomic Transition Network-Based Prediction and Real-World Patient Data Validation Identifies Ketorolac as a Repurposable Drug for Alzheimer's Disease

Distilling Event Sequence Knowledge From Large Language Models

Causal Inference with Causallib

Causal Markov Blanket Representation Learning for Domain Generalization Prediction

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