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GLUCOSE ML
A data platform solving the biggest bottleneck in diabetes AI research by streamlining discovery & evaluation of glucose datasets.
Team: Lead Research Professor, 2 Software Engineers, 1 Data Engineer, 1 Qualitative Researcher, 1 Designer (me).
Timeline: 6 months (ongoing)
Tools: Figma, Miro, Claude Design, Typeform
Outcomes:
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Launched 0->1: 3 full iterations from info architecture to metadata schema to final UI
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Validated with full HCI user study: robust testing of core flows that drove redesign.
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Targeting CHI research publication: generalizable findings on ML dataset repository needs and biases (submitting end of 2026).
Role: As Lead Product Designer, I led the 0->1 design & launch of the web platform end-to-end: user discovery to info architecture to metadata schema to UI to full user study.
RESULTS
6 hours -> 5 minutes to evaluate, access and clean a dataset
1 -> 5 datasets evaluated on average before choosing
0 -> 50+ users in 1 month
CONTEXT
Continuous glucose monitoring (CGM) is becoming an everyday wearable for millions. Minimally invasive sensors now stream real-time glucose data, one of the richest signals in metabolic health that powers AI research and products.
USER
PROBLEM
Finding and vetting a CGM dataset is so tedious that researchers settle for the first one they find.
1. Discovery is manual & scattered
Researchers burn days searching across academic papers, clinical portals, and one-off repos.
2. Evaluation data is
inconsistent
Different metadata, access rules & documentation make it impossible to compare without downloading each one.
3. ML quality gets compromised
Settling for the first dataset means biases slip through & model quality suffers downstream.
BUSINESS
IMPACT
As adoption spreads to everyday consumers, the volume and value of CGM data compounds.
Improving how researchers find & vet data raises the quality on every product built downstream. CGM is a ~$16B market growing 15% a year and one of the fastest growing health AI frontiers.
How might we help researchers discover, compare, and access the right CGM datasets in minutes?
Reduce time-to-dataset discovery
Increase datasets compared & reduce bias
Increase choice confidence & ML quality outcomes
SOLUTION
Glucose ML
A centralized CGM dataset hub built around a three-step flow:
Compare
View dataset characteristics side by side to find the best fit.
Discover
Browse & filter for datasets based on granular dimensions that meet research goals.
Evaluate
Explore visualizations, metadata, and one-click access to choose with confidence.
⚡ Case study in progress - updated daily!
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Let's connect!
If you like what you see, I'd love to chat!
Alternatively, here's a fancy sheet of paper:
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