Curriculum Vitae Apostolos Stamenos

University of Minnesota School of Public Health · Washington, D.C.

• Update online education material to ensure ADA-compliance.
• Grade student assignments to assess biostatistical literacy.
• Provide honest but kind feedback to students about where they went wrong.

Self-Employed · Minneapolis, MN

• Tech consulting (product ops, process design, tooling) for early-stage startups.
• 1:1 instruction in statistics, programming, and computer literacy for undergraduate, graduate, and adult learner students.

U.S. Department of Health & Human Services · Washington, D.C.

• Led agency-wide transition of mission-critical public health systems — HHS Protect and Vaccines.gov — from HHS to CDC and ASPR, ensuring zero-disruption data and operational handoff.
• Designed and engineered a custom automation bridge for the Vaccines.gov service desk transition to CDC, resolving a critical gap in service continuity.
• Built and deployed FedRAMP-compliant ML and NLP automation tooling within Palantir Foundry.
• Engineered high-throughput data pipelines to ingest, harmonize, and standardize health data from disparate federal and state sources; deployed ML-driven anomaly detection on national hospital data.
• Managed the full deployment lifecycle of Healthdata.gov and the HHS OCDO website.
• Mentored and led a technical team of 11 interns in rigorous data practices, reporting dashboards, and professional development.

U.S. Department of Health & Human Services · Washington, D.C.

• Developed Python programs to integrate diverse health equity datasets with federal databases.
• Generated data-driven recommendations that improved agency-wide data practices and communication of health results.

Lead Researcher · T32 Training Grant

• Developing a novel Bayesian ML method for modeling high-dimensional anatomical sensor data (MRI), accounting for complex spatiotemporal correlations.
• Identifying outlier trajectories in pediatric brain development related to mental health conditions.

Algorithm Lead · T32 Training Grant

• Designed and optimized C++ code for a Metropolis-Hastings sampler for simultaneous inference on high-dimensional clinical and genomic datasets.
• Built a multinomial ML classifier to diagnose pulmonary sarcoidosis using PCA-reduced patient protein data.