MS in Health Data Science — Dartmouth College (Geisel School of Medicine)
Completed my M.S. in Health Data Science, developing expertise in machine learning, biostatistics, epidemiology, bioinformatics, and biomedical data analysis.
Computational Biology • Data Engineering • Data Science
Hi, I'm Ryan!
I'm a computational biology researcher and data engineer with a background in molecular biology and health data science. At the California Conservation Genomics Project, I work with 22,000+ whole-genome sequencing samples across 150+ non-model species projects, developing bioinformatics workflows to investigate genetic diversity and evolutionary history. I also helped build Glucose-ML, an open-source resource that standardizes millions of continuous glucose monitoring readings for machine learning and biomedical research.
Education
BS in Molecular, Cell, and Developmental Biology — UC Santa Cruz
Built my scientific foundation in molecular biology while developing an interest in computational research, genomics, and data-driven scientific work.
Work Experience
Glucose-ML Project
Glucose-ML — Augmented Health Lab, Emory University
Glucose-ML is an open-source collection of continuous glucose monitoring (CGM) datasets designed to make diabetes data more accessible, standardized, and reproducible for biomedical research and machine learning.
My Contributions:
- As one of two founding team members, helped build Glucose-ML from the ground up, including its repository architecture and data processing infrastructure.
- Developed Python and Bash pipelines to acquire, clean, and harmonize 49M+ CGM readings from 20+ datasets encompassing 4,400+ participants.
- Designed machine learning case studies investigating diabetes classification using logistic regression, random forests, and XGBoost.
- Contributed to scientific publications and research examining the accessibility, standardization, and application of public CGM datasets.
California Conservation Genomics Project
The California Conservation Genomics Project (CCGP) is a large-scale collaborative research initiative generating genomic resources to support biodiversity conservation, population genomics, and species management across California.
My Contributions:
- Execute GATK-based variant calling using snpArcher (Snakemake) on Slurm HPC clusters across 150+ population genomics projects, encompassing 22,000+ WGS samples.
- Develop reproducible Snakemake workflows to investigate historical and recent effective population size, genetic diversity, and evolutionary history across 100+ non-model species.
- Perform end-to-end sequencing and variant quality control, including sequencing depth, mapping rates, population structure, and sample metadata.
- Maintain a MongoDB database containing 20,000+ samples and manage 140,000+ FASTQ files across AWS S3 and Google Cloud.
- Collaborate with 70+ consortium researchers, prepare NCBI submissions, and support the development of a public genomic metadata portal.
Publications
[1] B. Lu, Z. Liang, R. Pontius, S. Tang, T. Prioleau. GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting. NeurIPS 2026 (accepted).
[2] E. D. Enbody, A. Nakamoto, C. Mirchandani, M. Baylis, A. Chambers, C. Miller, R. Pontius, E. Toffelmier, CCGP Consortium, B. Shaffer, R. Corbett-Detig. Regional Conservation Genomics Highlights Factors Affecting Population Health. In preparation for submission to Science.
[3] R. Pontius, W. Pitakanonda, Z. Li, K. Lhabaik, F. Wang, B. Lu, Y. Cui, T. Prioleau. Public Continuous Glucose Monitoring Datasets for Data-Centric AI: A Survey and Tutorial with Glucose-ML. In preparation for ACM Computing Surveys (CSUR).
[4] W. Pitakanonda, D. Guzman Gonzalez, R. Pontius, L. Ding, W. Y. Chan, Z. Li, T. Prioleau. Bridging the Data Gap with Glucose-ML: Designing a Domain-Specific Dataset Repository to Accelerate Machine Learning for Health. Submitted to ACM CHI 2027 (under review).
My Digital Portfolio
Check out examples of my coding projects and research over the years. Visuals and links to the projects are provided too!
View ProjectsMy Email
Please reach out if you want to discuss research science or potential opportunities! Always happy to chat.
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