1 citations · 1 across the 3 of their papers we have counts for
5 papers
NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem
Irina Espejo Morales, Damon Hinz, Marvin Alberts +3
Structural elucidation from Nuclear Magnetic Resonance (NMR) data remains a fundamental bottleneck across chemistry, materials science, and biology. We demonstrate that an agentic…
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We test this question on the Ray…
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained with…
Making Sense of Data in the Wild: Data Analysis Automation at Scale
Mara Graziani, Malina Molnar, Irina Espejo Morales +2
As the volume of publicly available data continues to grow, researchers face the challenge of limited diversity in benchmarking machine learning tasks. Although thousands of datase…
Interleaving Text and Number Embeddings to Solve Mathemathics Problems
Marvin Alberts, Gianmarco Gabrieli, Irina Espejo Morales
Integrating text and numbers effectively is a crucial step towards enhancing Large Language Models (LLMs) capabilities in assisting in scientific tasks. While most current approach…