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Polymers are ubiquitous in our society thanks to their durability, low manufacturing costs, and ability to be formulated into diverse materials.
The main goal of the heavy ion program of many accelerator facilities (LHC, RHIC and the upcoming CBM/FAIR) is to create new phases of matter and explore their properties under extreme conditions.
This project develops cross-API agents that can complete real workflows spanning multiple software systems where actions must be coordinated end-to-end.
This project develops cross-API agents that can complete real workflows spanning multiple software systems where actions must be coordinated end-to-end.
As Big Tech acquires the advanced hardware components necessary to build private, special-purpose computing clusters, this project advocates for a new approach to publicly supported computing power in general, and on LLM pre-training in particular, through cross-Facility Federated Learning (xFFL).
The objective of this research project is to apply the Federation Learning framework to the fine-tuning process of previously developed connectors, with a particular focus on enhancing their performance in a multilingual context.
This project investigates the learning of 3D world simulators directly from multi-view RGB videos.
The resolution revolution has increasingly enabled single-particle cryo-EM reconstructions of previously inaccessible systems, including large membrane protein complexes that constitute a disproportionate share of drug targets.
RNA molecules span a great variety of biological functions, from genetic information storage to catalysis. This is possible thanks to the highly heterogeneous conformational ensembles that these molecules can adopt.
The goal of this project is to reach a new frontier in precision radiology by developing foundation models for 3D medical imaging, trained with self-supervised learning (SSL) techniques at an unprecedented scale.