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
Teaching AI models to improve themselves
ResearchPeter HessWhat is retrieval-augmented generation?
ExplainerKim MartineauAccelerating molecular optimization with AI
Deep DivePayel Das, Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, and Pin-Yu Chen11 minute readAI boosts the discovery of metamaterials vital for next-gen gadgets
ResearchYoussef Mroueh, Karthikeyan Shanmugam, and Payel Das10 minute readIBM AI finds new peptides – paving the way to better drug design
ResearchAleksandra Mojsilovic and Payel Das4 minute readDualTKB: A Dual Learning Bridge between Text and Knowledge Base
ResearchPierre Dognin6 minute readImage captioning as an assistive technology
NewsYoussef Mroueh5 minute read
Publications
- Michael Feffer
- Ronald Xu
- et al.
- 2024
- COLM 2024
- Megh Thakkar
- Quentin Fournier
- et al.
- 2024
- ACL 2024
- Afra Feyza Akyürek
- Ekin Akyürek
- et al.
- 2024
- ACL 2024
- Abdelrahman Zayed
- Gonçalo Mordido
- et al.
- 2024
- ACL 2024
- Shreyas Basavatia
- Keerthiram Murugesan
- et al.
- 2024
- ACL 2024
- Oscar Sainz
- Iker García-ferrero
- et al.
- 2024
- ACL 2024