Selected research projects · question, method, finding, impact
A selection of research projects framed around what I usually care about most: the question we needed to answer, the methods I chose and why, what we learned, and the decisions the work informed. Each draws on study designs, mixed methods, and quantitative analysis I led end to end.
How do people with Type 1 diabetes, and the clinicians and caregivers around them, actually make daily management decisions, and where could a gamified tool genuinely help rather than add burden?
End-to-end research program anchored in Northeastern's NSF i-Corps site, the National Science Foundation's lean-startup curriculum for moving research from lab to market. Following the I-Corps customer-discovery model, I ran 110+ semi-structured interviews across patients, clinicians, caregivers, and payers under weekly mentor-led coaching, testing demand and willingness-to-pay before committing to build. That discovery foundation fed a moderated, task-based usability study of the app and survey design and analysis of self-reported outcomes.
Three studies converged on a clear picture. In the ML evaluation on the OhioT1DM benchmark, a Sequence-to-Sequence BiLSTM gave the most consistent glucose forecasts across subjects (mean RMSE 21.9 mg/dL at 30 minutes), which set the prediction engine. Usability testing (7 participants) showed strong acceptance of the glucose-forecast feature (71% would use it daily) but flagged that the initial gamification display felt overwhelming. And need-finding with 6 caregivers surfaced a core tension: supporters wanted safety without surveillance, preferring "status at a glance" and gentle nudges over raw data streams.
The research directly shaped the product. The forecast findings drove a progressive-unlocking redesign of the gamification and clearer prediction explanations; the caregiver insights became an entire companion app, Sugar Slay Care, built around role-based, user-controlled data permissions rather than always-on monitoring. The work was published as a first-author paper in Frontiers in Digital Health and supported by roughly $86K in non-dilutive funding: the NSF I-Corps site that structured the discovery work above, the MassVentures Acorn Innovation Grant (a Massachusetts program backing startups that commercialize federally funded research), and the Northeastern SPARK Fund, which seeded the early prototype.
The patient app: glucose forecasting, gamified challenges, and progress tracking.
Sugar Slay Care: the companion app for caregivers, with role-based, user-controlled permissions.
How do people resolve conflict between competing goals and motor demands when acting under uncertainty in an immersive environment?
Designed and built an immersive Virtual Reality phase-matching task combining hand-tracking, eye-tracking, and pupillometry; developed the interactive paradigm in Unity (C#) and analyzed multi-stream behavioral data.
In a pilot of 11 non-autistic participants, transiently delaying the visual image of either the target hand or the participant's own virtual hand caused people to spontaneously delay their own movement, even though the instruction was simply to track the target. A Dynamic Time Warping analysis quantified this: synchronization stayed tight in control trials but showed a measurable increase in lag during the perturbation window, confirming that visual conflict alone shifts motor output. The work also contributes a Kalman-filter formulation of the active-inference synchronization model, a simpler alternative to prior sinusoidal approaches, that frames the behavior in terms of how much precision a person assigns to vision versus proprioception.
The task establishes a measurable framework for studying how people weigh competing sensory streams under conflict, with direct relevance to XR and immersive interface design, where a system constantly negotiates what the user sees against what they feel their body doing. The model predicts that autistic individuals, given documented differences in sensory precision-weighting, should respond differently to these perturbations; recruitment of an autistic cohort was underway at publication. If borne out, the result would link several previously separate sensorimotor findings in autism under a single active-inference account and point to concrete targets for intervention.
Across 15+ years of coaching, how does engagement and retention differ between current and past clients, and what does that tell leadership about where the program is and isn't serving people well?
Independently designed and executed a retrospective cohort study of 15+ years of private-pay LifeMAP coaching data (2008–November 2025), comparing 213 active clients (16,466 sessions) against 1,165 past clients (29,740 sessions) across 28 predefined metrics spanning engagement, goal areas, retention, living situation, geography, and payer tier. I defined the cohorts and inclusion criteria, integrated and reconciled multiple source exports, fully de-identified the data to a single Client/Profile ID, and documented every data-quality limitation up front: no session-level timestamps (so frequency measures are explicitly modeled proxies), and a gender field absent from the active export (so that breakdown is reported for the past cohort only). The deliverable was a structured report with professional visualizations written for a non-technical executive audience.
Because active clients are still in coaching, a naive comparison would have overstated their engagement. I treated this survivorship effect as a first-class methodological issue, framing every active-vs-past contrast as ongoing rather than complete and flagging it in the interpretation notes so leadership would not over-read the gap. I also called out small-sample artifacts that visually dominated certain charts (for example, a 13-client "living situation unknown" group whose ~306 average sessions was an outlier effect, not a real signal), so decisions would rest on the stable findings rather than the noise.
The active cohort was far more deeply engaged than past clients: a median of 47 sessions versus 14, and 86.85% pursuing multiple goals versus 61.55%. A retention-style curve held active clients above past rates at every interval (about 79% still engaged at ~6.5 months, against a much faster drop-off in the past cohort). Engagement was also highly concentrated: the most-engaged segment (50+ sessions) was roughly 46% of active clients but accounted for about 84% of all coaching hours. Employment, Independent Living/Self Care, and Life Skills were the dominant goal areas across both cohorts, and goal tracking had clearly improved over time, with only 1.88% of active clients lacking a recorded goal.
I designed and delivered the study end to end and presented it to both the program team and the executive team. The findings are now informing budgeting and marketing strategy for the LifeMAP program, which generates roughly 30% of the organization's revenue and underwrites many of its free and low-cost services for the autistic community. This had been a long-standing organizational priority left uncompleted because of its complexity; the engagement-concentration and retention results gave leadership a concrete, data-backed basis for where to focus retention efforts and how to position the program.