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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
Building on previous 1D stormwater ML work, this project will develop and train graph‑neural‑network models that emulate coupled 1D/2D hydraulic simulations.
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 proposes to train and validate a novel language model architecture based on energy-based neural networks that provides native explainability — a critical requirement under the EU AI Act for high-risk AI systems.
This project will explore a novel, scalable, and cost-effective approach to instruction tuning and alignment of existing LLMs to new languages.
This research aims to deliver a scalable, edge-ready blueprint for sustainable, robust artificial intelligence.
This proposal focuses specifically on scaling Video-Panda.