Every day the genome becomes more and more accessible: biochemically, computationally, and most importantly, medically. I build tools to make sense of the genome, with the goal of making genetically targeted medicine accessible for everyone. To get there, I am pursuing an MD-PhD, because going from sequence to symptom requires the latest expertise in both.
I started as a computer engineer and spent years building machine learning systems for health and biomedical research. Today I am a Research Specialist in the Stern Lab at HHMI and the Stowers Institute for Medical Research, where I study population genetics, design deep learning models for evolution, and assemble and annotate the latest high-quality genomes. The Stern Lab is a perfect source of comprehensive mentorship, where I can take science from the field and microscope to the sequencer and AI.
I hold an MPH where I studied psychiatric epidemiology, focusing on autism, ADHD, and gender. For the last four years I have helped teach a graduate public-health course that partners epidemiology students with autistic adults as collaborators, emphasizing accessibility, participatory research, and dignity of care.
An MD-PhD will bring these skills together: investigating mechanisms at the bench, teaching students in the lab, and caring for patients in the clinic. Patients deserve the latest science, and they deserve it explained clearly. Making narrowly tailored, advanced medicine accessible to broad populations is what makes this long journey worth it.
Evolutionary genetics & deep learning
2025 – presentDesigning transformer, CNN, RNN, autoencoder, and BiLSTM models for evolutionary genetics; engineering petabyte-scale pipelines to assemble, decontaminate, and annotate genomes; and evaluating long-read and hybrid assembly tools with hands-on genome curation.
Autism, gender & diagnosis
2023 – 2026Statistical analysis of national datasets (NSCH, ADDM) in Python and STATA on sex and gender differences in autism and ADHD diagnosis, integrating community-partner interviews with epidemiologic data and co-developing participatory methods with autistic adults as research partners.
Machine learning for medicine & health
2018 – 2025Built deep learning systems across biomedicine: UNet CNNs for medical image segmentation on sparse datasets, embedding-based vector storage for flow cytometry and laboratory data, multi-agent reinforcement learning environments, and geospatial pipelines for population-scale disease modeling. Architected secure multi-account AWS environments and advised federal leaders, including Congress and senior military staff, on ML infrastructure tradeoffs.
Children's mental health systems
2022 – 2023Organized more than 40 federally funded mental health programs into structured analytic datasets and modeled funding sensitivity to identify under- and over-funded categories, authoring sections of a published report presented to a council of healthcare executives.
Experience
Research Specialist
Instructional Assistant & Guest Lecturer
Senior Machine Learning Engineer & Health Researcher
Earlier engineering roles · Deloitte, Citigroup, Capital One, ARRIS Group
Education
M.P.H., Public Health
Premedical Coursework
B.S., Computer Engineering
Selected Publications
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Don't jump down my throat: gender gap in HPV vaccinations risk long-term cancer threats
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Artificial Intelligence as a Spontaneous Religious Threat
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A Vision for Mental Health Systems of Care for Young People
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Faithful Diagnoses: Association of Socioeconomic Status with Diagnoses of ASD and ADHD in Girls
Selected Presentations
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Development, Regulatory Genomics & AI
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ASPIRE RIKEN Meeting
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Annual guest lectures on autism epidemiology, subtyping, and pharmacology
Clinical & Service
Volunteer, Virginia Medical Reserve Corps
Wilderness First Responder (NOLS)
USA Climbing Level 2 Certified Coach
Global Affairs and Religion Network
Languages
Selected Skills
Best reached by email. I am always glad to talk about genomics, public health, or physician-scientist training.
Email me