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Applied Computing for Scientific Discovery

Rafael Zamora-Resendiz

PXL 20250507 042142857.PORTRAIT5
Rafael Zamora-Resendiz
Computer Systems Engineer 2
New Jersey

Rafael Zamora-Resendiz is a Computer Systems Engineer (CSE-2) in the Applied Mathematics and Computational Research Division (AMCR) at Lawrence Berkeley National Laboratory (LBNL). Currently, his focus is on applied Natural Language Processing (NLP) for healthcare, collaborating closely with the Department of Veterans Affairs (VA) as part of the Million Veterans Program (MVP). In this role, he spearheads the development of NLP methods for scalable information retrieval, utilizing high-performance computing.

Rafael earned his Bachelor of Science degree in Computer Science from Hood College in 2017, gaining practical experience conducting scientific research during his Visiting Faculty Program (VFP) internship under the guidance of Dr. Xinlian Liu (Hood College) and Dr. Silvia Crivelli (LBNL). His postbaccalaureate research explored applications of deep learning to structural proteomics, developing methods for representing structural information in machine learning models.

Joining the Applied Computing for Scientific Discovery (ACSD) group as a domain expert in machine learning in 2019, Rafael provides methodological support to VA clinicians in developing and implementing deep learning-enabled electronic health record analysis. His efforts include the development of large language models for clinical text and the creation of scalable search algorithms, specifically aimed at indexing U.S. Veteran mortality factors.

Rafael recently secured an INCITE Award for FY2024 under the project titled "Clinical Foundation LLMs for Scaling Public Health Surveillance." Serving as a Co-Investigator under Dr. Silvia Crivelli, this project will harnesses the computational capabilities of Oak Ridge National Laboratory's HPE-Cray EX, with an allocation of 200,000 Frontier node-hours. The research will contribute to advancing biomedical science by leveraging large language models (LLMs) for precision healthcare. In developing a foundational model for clinical language using the Veteran Affair’s Corporate Data Warehouse (CDW), the team aims to expand the size of clinical LLMs by tens of billions in parameters and train them on a corpus comprising billions of documents, drawn from over 20 years of clinical text for over 23 million patients.

Rafael's goal is to leverage emerging foundation models towards building a comprehensive "map of disease progression" for the U.S. veteran population, which will help in understanding the applicability of pre-trained LLMs for representing clinically meaningful phenotypes and monitoring impacts of medical interventions on patient health over time.

 

Journal Articles

Sean R Miller, Matthew Schipper, Lars G Fritsche, Ralph Jiang, Garthmore authors » "Pan‐Cancer Survival Impact of Immune Checkpoint Inhibitors in a National Healthcare System", November 7, 2024,

Alex K Bryant, Rafael Zamora‐Resendiz, Xin Dai, Destinee Morrow, Yueweimore authors » "Artificial intelligence to unlock real‐world evidence in clinical oncology: A primer on recent advances", Cancer Medicine, June 20, 2024, doi: https://doi.org/10.1002/cam4.7253

Sayera Dhaubhadel, Kumkum Ganguly, Ruy M Ribeiro, Judith D Cohn, James Mmore authors » "High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning", scientific reports, January 20, 2024,

Rafael Zamora-Resendiz , David W. Oslin, Dina Hooshyar, Silvia Crivelli, "Using Electronic Health Record Metadata to Predict Housing Instability amongst Veterans", Preventive Medicine Reports, November 7, 2023,

Nathan A. Kimbrel, Allison E. Ashley-Koch, Xue J. Qin, Jennifer H.more authors » "Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans", JAMA Psychiatry, February 1, 2023, 80:100-191, doi: 10.1001/jamapsychiatry.2022.3896

Xiange Wang, Rafael Zamora-Resendiz, Courtney D. Shelley, Carrie Manore,more authors » "An examination of the association between altitude and suicide deaths, suicide attempts, and suicidal ideation among veterans at both the patient and geospatial level", Journal of Psychiatric Research, July 11, 2022,

Destinee Morrow, Rafael Zamora-Resendiz, Jean C Beckham, Nathan A Kimbrel,more authors » "A case for developing domain-specific vocabularies for extracting suicide factors from healthcare notes", Journal of Psychiatric Research, July 1, 2022, 151:328-338, doi: 10.1016/j.jpsychires.2022.04.009

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