Awarded Projects

This project leverages cutting-edge generative AI models to design synthetic enzymes optimized for industrial applications in biofuels, agriculture, and pharmaceuticals.

This project addresses the critical gap between the promise of general-purpose AI (GPAI) models in medical imaging and their reliable clinical implementation through a two-phase approach combining systematic robustness evaluation and targeted domain adaptation.

It is widely acknowledged that the prediction of turbulent flows in the presence of separation is one of the most significant challenges in fluid dynamics.

Although current Earth system models (ESMs) project a consistent pattern of future global warming, there are important regional differences that increase the uncertainty at the local scale, which poses a risk for climate adaptation.

This project combines large-scale generation of safe and deliberately unsafe robot demonstrations with frontier vision-language models to produce fine-grained, temporally consistent safety explanations over video sequences

This project aims to advance the frontier of efficient multimodal alignment through the continued development of Modality Linear Representation-Steering (MoReS)—a lightweight, scalable fine-tuning framework for visual instruction tuning in Multimodal Large Language Models (MLLMs).

Large language models (LLMs) are at the core of the current AI revolution, and have laid the groundwork for tremendous advancements in Natural Language Processing.

This project will explore a novel, scalable, and cost-effective approach to instruction tuning and alignment of existing LLMs to new languages.

This proposal focuses specifically on scaling Video-Panda.