Irina Espejo Morales

Irina Espejo Morales is a Research Scientist in AI for Science, working on machine learning for physics, chemistry, and biology.

AI for Science

Irina Espejo Morales

Research Scientist in AI for Science

About

Hi, I’m Irina! I’m a Research Scientist at PolymathicAI and the NYU Center for Data Science, working (very) broadly on AI for Science across physics, chemistry, and biology (catch them all!).

My work has spanned agentic systems for NMR spectroscopy, multimodal foundation models for biomolecular data, and AI for chemistry at IBM Research Zurich. Before that, I completed a PhD in Data Science at NYU with Kyle Cranmer, applying active learning to searches for new physics at the LHC.

My PhD was supported by Fulbright, DeepMind, and IRIS-HEP scholarships. I previously studied theoretical physics at Oxford and earned bachelor’s degrees in Physics and Mathematics from Universitat Autonoma de Barcelona.

Updates

  1. Our paper NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem will be presented as a spotlight at the AI4Science workshop at ICML 2026.

  2. Preprint is out for MIMIC: A Generative Multimodal Foundation Model for Biomolecules.

Selected Publications

  1. NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem

    Irina Espejo Morales, Damon J. Hinz, Marvin Alberts, Geraud Krawezik, Haewon Jeong, Shirley Ho

    ICML 2026 Workshop AI4Science (Spotlight Talk), 2026

    Chemistry
  2. MIMIC: A Generative Multimodal Foundation Model for Biomolecules

    Siavash Golkar, Jake Kovalic†, Irina Espejo Morales†, Samuel Sledzieski†, Minhuan Li†, Ksenia Sokolova, Geraud Krawezik, Alberto Bietti, Claudia Skok Gibbs, Roman Klypa, Shengwei Xiong, Francois Lanusse, Liam Parker, Kyunghyun Cho, Miles Cranmer, Tom Hehir, Michael McCabe, Lucas Meyer, Rudy Morel, Payel Mukhopadhyay, Mariel Pettee, Helen Qu, Jeff Shen, David Fouhey, Hadi Sotoudeh, Vikram Mulligan, Pilar Cossio, Sonya M. Hanson, Alisha N. Jones, Olga G. Troyanskaya, Shirley Ho

    arXiv preprint, 2026

    Biology
  3. Semantic Search for 100M+ Galaxy Images Using AI-generated Captions

    Nolan Koblischke, Liam Parker, Francois Lanusse, Jo Bovy, Irina Espejo, Shirley Ho

    The Astrophysical Journal, 2026

    Physics
  4. Efficient search for new physics using Active Learning in the ATLAS Experiment

    Irina Espejo, Patrick Rieck and the ATLAS Collaboration

    Journal of Physics: Conference Series, 2026

    Physics
  5. Scaling MadMiner with a deployment on REANA

    Irina Espejo, Sinclert Pérez, Kenyi Hurtado, Lukas Heinrich, Kyle Cranmer

    Journal of Physics: Conference Series, 2026

    Physics
  6. Unified lookup tables: training foundation models on encoded data

    Nikita Janakarajan†, Irina Espejo Morales†, Marvin Alberts, Andrea Giovannini, Matteo Manica, Antonio Foncubierta-Rodríguez

    Machine Learning: Science and Technology, 2025

    MLChemistry
  7. Making Sense of Data in the Wild: Data Analysis Automation at Scale

    Mara Graziani, Malina Molnar, Irina Espejo Morales, Joris Cadow-Gossweiler, Teodoro Laino

    arXiv preprint, 2025

    ML
  8. Activity recognition in scientific experimentation using multimodal visual encoding

    Gianmarco Gabrieli, Irina Espejo Morales, Dimitrios Christofidellis, Mara Graziani, Andrea Giovannini, Federico Zipoli, Amol Thakkar, Antonio Foncubierta, Matteo Manica, Patrick W. Ruch

    Digital Discovery, 2025

    ML
  9. Interleaving Text and Number Embeddings to Solve Mathemathics Problems

    Marvin Alberts, Gianmarco Gabrieli, Irina Espejo Morales

    arXiv preprint, 2024

    ML
  10. MadMiner: Machine Learning-Based Inference for Particle Physics

    Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer

    Computing and Software for Big Science, 2020

    Physics
  11. Constraining effective field theories with machine learning

    Johann Brehmer, Kyle Cranmer, Irina Espejo, Alexander Held, Felix Kling, Gilles Louppe, Juan Pavez

    EPJ Web of Conferences, 2020

    Physics

† Equal contribution