Awarded Projects

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 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.

The national libraries of Norway and Sweden collect and preserve nearly everything that is published in their respective languages. Both organizations have used these collections to train and release open access AI models that have seen widespread use with millions of combined downloads.

The objective of this project is to study search algorithms in the context of two-players stochastic games.

The primary objective is to evaluate the impact of self-interacting dark matter on alleviating discrepancies between simulation results and observations of galaxy-galaxy strong lensing in cluster environments.

Understanding how merging binary neutron stars (BNSs) can launch powerful relativistic jets and, in turn, produce short gamma-ray bursts (SGRBs) remains a major theoretical challenge.