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María Rodríguez Martínez, PhD, MSc

Associate Professor of Biomedical Informatics and Data Science
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Titles

Associate Professor of Biomedical Informatics and Data Science

Biography

Prof. María Rodríguez Martínez is an Associate Professor in Biomedical Informatics & Data Science at the Yale School of Medicine. Before joining Yale, she was the Technical Leader of Systems Biology at IBM Research Europe (Switzerland), where she established the team of computational systems biology. Initially trained as a physicist, she transitioned to computational biology during her postdoctoral work at the Weizmann Institute of Science (Israel) and Columbia University (USA). At IBM Research, her research centered on developing computational approaches for cancer personalized medicine and she led two major EU-funded consortia focused on prostate and pediatric cancers.

Currently, her work focuses on understanding T and B cell function in the context of complex diseases, such as cancer and autoimmune diseases. Her research integrates mechanistic and AI models, emphasizing interpretable deep learning methods to uncover the rules behind model predictions. In this field, her team has developed interpretable models to predict T cell receptor binding and investigated B cell development.

Prof. Rodríguez Martínez serves as an editor for ImmunoInformatics, Frontiers in Systems Biology, BMC Bioinformatics, and IEEE Transactions on Molecular, Biological, and Multi-Scale Communications. She is also a frequent speaker and organizing committee member at leading Computational and Mathematical Biology conferences, such as ISMB, the Society for Mathematical Biology, and ECCB.

Last Updated on May 10, 2024.

Appointments

Other Departments & Organizations

Education & Training

PhD
Institut d'Astrophysique de Paris, Gravitation and Cosmology (2003)
MSc
Observatoire de Paris-Meudon (2000)

Research

Research at a Glance

Yale Co-Authors

Frequent collaborators of María Rodríguez Martínez's published research.

Publications

2026

  • A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability
    Erasmus M, Bedinger D, Hopkins E, Ferguson G, Strickler J, Graff C, Summers S, Capehart S, Slocum J, Richardson C, Kumar S, Sun Z, Shang Y, Zhang J, Gu M, Yi L, Wellner A, Zheng S, Lu W, Sormanni P, Greenig M, Zhang H, Mann B, Baghbanzadeh M, Rahnavard A, Moore G, Sun H, Ding Y, Nisthal A, Kanodia J, Bernett M, Pélissier A, Shao Y, Rodriguez Martinez M, Ramesh K, Nastri H, Evers A, Abdelatif A, Bordner A, Bordyuh M, Heo L, Kidd B, Serhat Tetikol H, Wei S, Shin J, Peckner R, Manley L, Lunge A, Devasurmutt Y, Guloglu B, Copoiu L, McGibbon M, Fernandez-Quintero M, Mishra N, Callaghan S, Swanson O, Bader D, Ferguson J, Raghavan S, Nemoz B, Maillie C, Bowman C, Briney B, Ward A, Marcatili P, Akbar R, He B, Wu F, Yao J, Hu B, Kucer M, Gibson K, Somasundaram R, Hung L, Kaszuba T, Fremont D, Jeong H, Kurella V, Malhotra S, Kumar S, Liu Y, Xu L, Misa J, John A, Vogt J, Dávila-Hernández F, Xu D, Chungyoun M, Huggan Z, Gray J, Parkinson J, Ko Y, Wang W, Geiger F, Ziegler J, Haas N, Challacombe C, Qamar A, Singh A, Tang Y, An Z, Jiang X, Kim Y, Zhao X, Swanson E, Klattig J, Winkler K, Bataa T, Sandig V, Denzler L, Liu C, Brezski R, Spector L, Perea-Schmittle K, D’Angelo S, Ferrara F, Bradbury A. A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability. Nature Biotechnology 2026, 1-13. PMID: 42618805, DOI: 10.1038/s41587-026-03238-6.
    Peer-Reviewed Original Research
  • Spatial organisation of tumor-infiltrating B-cells informs immune checkpoint inhibitor response in Claudin 18.2-expressing gastric cancer.
    Zhao J, Lee C, Chan A, Blum S, Li C, Ma H, Luo W, Srivastava S, Lum H, Tay S, Kwon W, Park S, Joseph C, Chong L, Jung M, Sridhar S, Pelissier A, Bhatt N, Lee J, Dai Y, Loo L, Tan A, Hagihara T, Wee F, Chia D, Sim K, Teh M, Nakayama I, Xu J, Ng M, Walsh R, Yeong J, Jeyasekharan A, Grabsch H, So J, Yong W, Vazquez-Garcia I, Bod L, Klempner S, Shitara K, Janjigian Y, Tan P, Rodríguez Martínez M, Rha S, Sundar R. Spatial organisation of tumor-infiltrating B-cells informs immune checkpoint inhibitor response in Claudin 18.2-expressing gastric cancer. Clinical Cancer Research 2026, of1-of14. PMID: 42565557, DOI: 10.1158/1078-0432.ccr-26-1382.
    Peer-Reviewed Original Research
  • IMMREP25: Unseen Peptides
    Richardson E, Aarts YJM, Altin JA, Baakman CAB, Bradley P, Chen B, Clifford J, Dhar M, Diepenbroek D, Fast E, Gowthaman R, He J, Karnaukhov V, Marzella DF, Meysman P, Nielsen M, Nilsson JB, Deleuran SN, Parizi FM, Pelissier A, Pierce BG, Rodriguez Martinez M, Roran A R D, Saravanakumar S, Shao Y, Smit N, Van Houcke M, Visani GM, Wan YTR, Wang X, Woods L, Wuyts S, Xiao C, Xue LC, Barton J, Noakes M, May DH, Peters B, . IMMREP25: Unseen Peptides. 2026 DOI: 10.64898/2026.03.30.715276.
    Peer-Reviewed Original Research
  • Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks
    Pélissier A, Phan M, Le Bail D, Beerenwinkel N, Rodríguez Martínez M. Unifying non-Markovian dynamics and agent heterogeneity in scalable stochastic networks. Nature Communications 2026, 17: 3345. PMID: 41771859, PMCID: PMC13066373, DOI: 10.1038/s41467-026-69817-y.
    Peer-Reviewed Original Research
    This study introduces MOSAIC, a computational framework that efficiently simulates complex systems with individual-level diversity and memory, enabling accurate modeling across biology, physics, and social networks.
    Full Summary
  • Pediatric acute myeloid leukemia tumor composition predicts patient outcomes at diagnosis and reveals mechanisms of resistance to chemotherapy.
    NajafPanah MJ, Stevens AM, Krueger MJ, Rochette M, Sandhu S, Kim L, Addanki S, Cooper J, Chiu HS, Epps J, Somvanshi S, Zorman B, Martinez MR, Rapsomaniki M, Unger S, Becher B, Yi JS, Man TK, Redell ML, Sumazin P. Pediatric acute myeloid leukemia tumor composition predicts patient outcomes at diagnosis and reveals mechanisms of resistance to chemotherapy. Res Sq 2026 PMID: 41646346, DOI: 10.21203/rs.3.rs-4669225/v1.
    Peer-Reviewed Original Research

2025

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Contacts

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101 College Street, PO Box 208009

New Haven, CT 06520-8009

United States

Locations

  • Department of Biomedical Informatics & Data Science

    Academic Office

    101 College Street, Fl 10, Rm 1021R

    New Haven, CT 06510

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