JUSTIN T. REESE

Dr. Reese is a computational biologist with over two decades of research experience at the intersection of artificial intelligence, biomedical informatics, and genomic data integration. His work focuses on developing computational frameworks that extract actionable biological and clinical insights from complex datasets, including electronic health records and large-scale omics data. He has authored more than 80 peer-reviewed publications, is an inventor on two patents, and has an h-index of 39. At Lawrence Berkeley National Laboratory, Dr. Reese leads multiple federally funded projects through NIH, DoD, and private foundations to apply artificial intelligence, graph-based machine learning, and large-language-model (LLM) methods to translational and precision-medicine research. His recent work has focused on integrating LLM-based reasoning with biomedical knowledge graphs, developing open-source AI systems such as KG-Hub, Graph RAG, and GRAPE to support disease subtyping, drug repurposing, and automated scientific discovery. Dr. Reese’s career integrates sustained independent scholarship, collaborative leadership, and experience in research management and biotechnology entrepreneurship, supported by a strong record of mentorship and teaching at the undergraduate and graduate levels.

• 1. Sierk M.L., Danis D., Patil S., … Reese J.T. (2025). Oncopacket: Integration of Cancer Research Data Using GA4GH Phenopackets. Bioinformatics. Senior author.
• 2. Groza T., Caufield J.H., Gration D., … Reese J.T. (2024). An Evaluation of GPT Models for Phenotype Concept Recognition. BMC Medical Informatics and Decision Making, 24(1):30. Senior author.
• 3. Caufield J.H., Putman T., Schaper K., … Reese J.T. (2023). KG-Hub: Building and Exchanging Biological Knowledge Graphs. Bioinformatics, 39(7). Senior author.
• 4. Chan L.E., Casiraghi E., Laraway B., Coleman B., Blau H., Zaman A., Harris N.L., Wilkins K., Antony B., Gargano M., Valentini G., Sahner D., Haendel M., Robinson P.N., Bramante C., Reese J.T.; N3C Consortium. (2022). Metformin Is Associated with Reduced COVID-19 Severity in Patients with Prediabetes. Diabetes Research and Clinical Practice, 194:110157. doi:10.1016/j.diabres.2022.110157. Co-senior author.
• 5. Chimirri L., Caufield J.H., Bridges Y., Matentzoglu N., … Reese J.T., Robinson P.N. (2025). Consistent Performance of Large Language Models in Rare Disease Diagnosis Across Ten Languages and 4,917 Cases. Bioinformatics (submitted). Co-senior author.
• 6. Niyonkuru E., Caufield J.H., Carmody L.C., … Reese J.T., Robinson P.N. (2025). Leveraging Generative AI to Assist Biocuration of Medical Actions for Rare Disease. Bioinformatics Advances (accepted). Co-senior author.
• 7. Reese J.T., Chimirri L., Danis D., … Robinson P.N. (2024). Evaluation of the Diagnostic Accuracy of GPT-4 in Five Thousand Rare Disease Cases. medRxiv preprint. Senior author.
• 8. Reese J.T., Blau H., Bergquist T., … Robinson P.N. (2023). Generalizable COVID Subtypes: Findings from the NIH N3C and RECOVER Programs. EBioMedicine, 87:104413. Senior author.
• 9. Cappelletti L., Fontana T., Casiraghi E., … Reese J.T., Valentini G. (2023). GRAPE: Fast and Scalable Graph Processing and Random-Walk-Based Embedding. Nature Computational Science, 3(6):552–568. Co-senior author.

• Private Foundation – Tracy Family SILQ Center, Washington University School of Medicine | Co-Investigator | 2025 Project: Knowledge Graph–based AI Assistant for Alzheimer’s Disease Discovery Developed a beta AI assistant integrating LLM reasoning, Graph RAG, and the KG-Hub platform for Alzheimer’s knowledge-graph construction.
• Department of Defense | U.S. Air Force (Contract LX06000079) | Co-Investigator | 2023 Project: Graph-Based ML Algorithm to Identify FDA-Approved Small-Molecule Drugs for High-Altitude Flight Integrated gene-expression and disease-phenotype data with biomedical ontologies to identify candidate interventions.
• Department of Defense | U.S. Air Force (Contract LX06000056) | Co-Investigator | 2022 Project: Knowledge Graphs for Organization and Visualization of Multisource Data Created an integrated knowledge-graph platform linking clinical and molecular datasets; implemented ontology-based mapping of Air Force health-record data.
• National Institutes of Health | NCATS (NCATS-P00438-B) | Co-Investigator | 2022 – 2023 Project: N3C Open Data Portal and Community Engagement Support Services Applied ML to electronic-health-record data from 77 hospital systems to define subtypes of viral and post-viral illness.
• National Institutes of Health | NCATS (7U24TR002306-05) | Co-Investigator | 2021 – 2022 Project: A National Center for Digital Health Informatics Innovation Coordinated development of ML workflows for clinical record integration supporting precision-medicine research.

• Publications: 84 peer-reviewed papers across Nature, Science, PNAS, Nature Computational Science, Bioinformatics, EBioMedicine, and others.
• Citations: h-index = 39; i10-index = 75 (Google Scholar, 2025).
• Patents:
• Device, System, and Method for Securing and Comparing Genomic Data — U.S. Patent 9449191 B2.
• [Second patent – details TBD.]
• Software & Open-Source Tools: KG-Hub, Graph RAG, GRAPE, Ensmallen, Embiggen.
• Training & Mentorship: Supervised [FILL IN LATER] trainees and research staff in computational biology and biomedical AI.

• TEACHING EXPERIENCE
• University of Missouri, Columbia, MO — Assistant Research Professor, Department of Animal Science Lectured on bioinformatics and scientific programming.
• Georgetown University, Washington, DC — Affiliate Research Professor, Department of Biology Lectured on bioinformatics and scientific programming; co-supervised graduate and postdoctoral researchers.
• Lawrence Berkeley National Laboratory, Berkeley, CA — Research Scientist Leads informal and consortium-based training activities on AI methodologies, knowledge-graph engineering, mob programming, and reproducible computational workflows for NIH and DOE collaborative programs.
• MENTORSHIP
• Mentored and supervised [FILL IN LATER] graduate students, postdoctoral fellows, and research software engineers in computational biology and AI-based biomedical research.
• Advises interdisciplinary teams within the Monarch Initiative, N3C Consortium, and KG-Hub on integrating ontologies, ML models, and data pipelines.

• SCIENTIFIC AND PROGRAM LEADERSHIP
• Lawrence Berkeley National Laboratory — Principal or co-investigator on NIH, DOE, and DoD projects integrating AI, knowledge graphs, and clinical data for translational research.
• Leads AI and data-integration efforts in the Monarch Initiative, N3C Machine Learning Domain Team, and KG-Hub, coordinating multi-institutional collaborations among biomedical, computational, and clinical researchers.
• Co-founded and served as Chief Operating Officer of two biotechnology startups (Genformatic LLC and Vital Scientific LLC), managing technical strategy and product development.
• EDITORIAL AND PEER REVIEW SERVICE
• Journal peer review (selected): Nature Communications, Lancet Digital Health, Lancet Infectious Diseases, EBioMedicine, JAMIA, Bioinformatics, Clinical and Translational Science.
• PROFESSIONAL AND COMMUNITY SERVICE
• Organizer, SC24 Workshop on Graph Machine Learning (GrAPL @ SC24) — organized invited talks, reviewed abstracts, and led session logistics for the flagship international HPC conference.
• Serves on organizing and program committees for community events on AI and knowledge-graph methods, including ISMB/BOSC and ICBO.
• Provides mentorship and technical guidance within national collaborations on AI/ML applications to precision medicine and rare-disease diagnostics.