Trustworthy Generation
Data is key to technological innovations. We develop theoretical and algorithmic frameworks for generative AI to synthesize realistic, diverse, and targeted data. Our methods facilitate data augmentation for trustworthy machine learning and accelerate novel designs for drug and material discovery, and beyond.
Our work
What is retrieval-augmented generation?
ExplainerKim Martineau- AI
- Explainable AI
- Generative AI
- Natural Language Processing
- Trustworthy Generation
Accelerating molecular optimization with AI
Deep DivePayel Das, Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, and Pin-Yu Chen11 minute read- Accelerated Discovery
- Generative AI
- Healthcare
- Materials Discovery
- Trustworthy AI
- Trustworthy Generation
AI boosts the discovery of metamaterials vital for next-gen gadgets
ResearchYoussef Mroueh, Karthikeyan Shanmugam, and Payel Das10 minute read- AI
- Materials Discovery
- Trustworthy Generation
- Uncertainty Quantification
IBM AI finds new peptides – paving the way to better drug design
ResearchAleksandra Mojsilovic and Payel Das4 minute read- Accelerated Discovery
- AI
- Generative AI
- Materials Discovery
- Trustworthy Generation
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
ResearchPierre Dognin6 minute read- Knowledge and Reasoning
- Natural Language Processing
- Trustworthy Generation
Image captioning as an assistive technology
NewsYoussef Mroueh5 minute read- Computer Vision
- Trustworthy AI
- Trustworthy Generation
Publications
- Shenshen Wang
- Steven Durr
- et al.
- 2024
- APS March Meeting 2024
- Abdelrahman Zayed
- Gonçalo Mordido
- et al.
- 2024
- AAAI 2024
- Nicolas Deutschmann
- Marvin Alberts
- et al.
- 2024
- AAAI 2024
- Igor Melnyk
- Aurelie Lozano
- et al.
- 2023
- NeurIPS 2023
- Sourya Basu
- Pulkit Katdare
- et al.
- 2023
- NeurIPS 2023
- Miriam Rateike
- Celia Cintas
- et al.
- 2023
- NeurIPS 2023