I build and operate the systems that turn passive smartphone and wearable data into research-grade behavioral signal — and I run the same rigor on myself.
I'm a product leader working at the intersection of digital health, clinical research operations, and mobile/backend engineering. I specialize in developing and maintaining the iOS, Android, and cloud infrastructure behind large-scale digital phenotyping studies.
As Head of Platform for the Beiwe Research Platform at Harvard T.H. Chan School of Public Health, I lead product management for a mobile research tool used in large-scale behavioral health studies — working directly with clients, researchers, and engineers to ship features, debug across the full stack, and manage the pipeline that turns high-throughput sensor data into meaningful behavioral metrics.
Before this, I worked in healthcare data analytics, building workflows to extract insight from complex financial and clinical datasets. My background spans neuroscience, software, and operations — which is mostly what lets me translate what researchers and clinicians actually need into a real development roadmap.
Outside of work: running, skiing, videography, drones, Legos, wearables — and turning all of the above into personal datasets I actually analyze. Reach out if you're working on something at this intersection.
Experience
Direct operations of the Beiwe Service Center and manage ~20 active studies on the Beiwe platform across multiple countries — platform demonstrations, contracting, IRB management, onboarding, technical support, and data analysis support. Maintain costing models and collaborate with finance on center budget, invoicing, and platform funding. Manage a team of 4 research assistants; edit grants and manuscripts for publication.
Lead product development for the platform and its mobile apps, gathering feedback from external vendors and internal research teams to prioritize features and drive development with external technology partners.
MIT delta v is a leading startup accelerator for MIT entrepreneurs. My team built a venture addressing burnout in high-stress professions — banking, healthcare, first responders, law — using in-house HR data and digital phenotyping to surface burnout metrics and recommend interventions. I led digital phenotyping and data visualization feature development through mentorship, funding, and structured customer discovery, culminating in Demo Day.
Led reporting and analysis of financial, clinical, and utilization data, translating it into actionable insight for hospital leadership, physicians, and finance. Built data pipelines using Python, SQL, and Tableau to streamline operations and support decision-making.
Identified a billing workflow error accounting for ~30% of lost revenue annually in the Psychiatry Department.
Co-founded CNOC in the Department of Neurosurgery, providing academic training in the quantitative and methodological principles of clinical outcomes research — large dataset design, data management/analysis, medical genomics, statistical analysis, and the peer-review publication process.
Published 20 peer-reviewed journal articles; presented at national and international conferences.
Supported Dr. Raina Fichorova's research on mucosal inflammation and immunity, independently managing timelines, contributing to experimental design, and developing novel cytokine assays with external vendors. Supervised junior lab members.
Published 10 peer-reviewed journal articles; presented at national and international conferences.
Supported mucosal inflammation and immunity research in a CAP-accredited laboratory: specimen processing, data analysis with QC checks, cell and bacterial culture, ELISA and cytokine assays. Built extensive QC systems for data entry and analysis; co-authored research abstracts and publications.
Research & Publications
Computational Neuroscience Outcomes Center — 20 publications
Laboratory of Genital Tract Biology — 10 publications
Personal Projects
JARVIS — Job Analysis & Role Vetting Intel System
Job searches are overwhelming — juggling resumes, tailoring applications, figuring out which roles actually align with a long-term goal. I wanted a tool that could stay organized, give strategic feedback, and support the whole process. So I treated it like launching a real product: built, tested, refined, and iterated on prompts to fix hallucinations — with my sister, who was job-hunting herself, giving real-time feedback on which features actually mattered.
My own background spans clinical research, operations, analytics, product management, and two startup stints — I needed something that could see the pattern across very different opportunities and gradually build a picture of what an ideal role actually looks like, not just score one job at a time.
"At any point, I've been able to ask JARVIS to show me an updated job ranking table and it's good about giving you a full list of all the jobs you've fed it, sorted by fit score and the details about those jobs."— JARVIS user
"One of the best things about the tool is that it keeps me focused on looking for jobs that align with the long-term plan JARVIS suggested. This has helped me keep my search targeted toward my own growth goals, rather than falling into that desperate mode where we apply to everything."— JARVIS user
What's next: Live job feed integration for proactive matching, ongoing skill-gap analysis, a built-in application tracker with interview prep, real-time ATS scoring, and LinkedIn profile alignment suggestions — building on the same foundation as the search itself evolves.
FRIDAY — Fantasy Research, Data & Analysis for You
Fantasy football is part skill, part luck, and a whole lot of research — juggling analyst sites, injury updates, and endless flex debates. I built a persistent, stateful GPT that acts as a personal GM: it remembers my roster across the season, builds an ideal lineup each week from matchup-based projections, produces sourced player reports (ESPN, FantasyPros, Yahoo, RotoWire, Draft Sharks), evaluates waiver and trade moves with tier-based value, and proactively flags injury news on my own roster with replacement suggestions.
Structuring the role, defining outputs, and wiring in stateful memory so it evolves across a 12-man PPR league season was the real challenge — and it's already saved hours of weekly research.
Health Data Studies
Five personal N-of-1 studies, applying the same study-design rigor I use professionally to my own wearable, CGM, and behavioral data.