FigureAsia 35 Under 35 · Healthcare
Yuzhe Yang
Age 31 · Wearables, multimodal biomarkers and health AI · Los Angeles, United States
Young laboratory leader contributing to 2026 large-cohort work on insulin resistance and passive smartphone-based physiological measurement.
- Approximate age at 31 December 2025
- 31
- Field
- Healthcare
- Country or region
- Los Angeles, United States
- FigureAsia U35 Assessment
- 84.7 / 100
Profile
Career and documented record
Yuzhe Yang's research sits between consumer sensing and clinical interpretation. In 2026, he coauthored a study of insulin-resistance prediction that combined wearable-derived physiology with blood biomarkers in 1,165 participants. The work asked whether continuous signals can add a dynamic view of metabolic dysfunction to measurements usually taken at isolated visits.
He also contributed to 2026 research on passive heart-rate measurement using smartphones, a direction with obvious reach but demanding validation across devices, skin tones, behaviour and clinical contexts. Yang's independent laboratory at UCLA extends those questions into movement, neurodegeneration and longitudinal health intelligence.
FigureAsia does not attribute team science to a single author. Yang was not the sole or necessarily lead author of every cited study, and the systems are research tools rather than cleared diagnostics. His inclusion rests on the coherence of the programme, early faculty leadership and an ability to connect multimodal machine learning to measurements that ordinary people can actually generate.
FigureAsia selection
Why Yuzhe Yang is on the list
FigureAsia selected Yang because his 2025–2026 work advances a practical model of health intelligence: combine low-friction sensing with clinically meaningful reference data, then test at cohort scale. The 1,165-participant study provides substance beyond a prototype. His score remains below that of honourees with direct regulatory or patient-outcome evidence, reflecting the difference between prediction research and implemented care.
Verified work
The 2025–26 record
Principal milestone
1,165 participants in the insulin-resistance study
Evidence record
Two 2026 multimodal sensing contributions
Scale or implementation
Independent health-intelligence laboratory established at UCLA
Field context
The work in its field
Within wearables, multimodal biomarkers and health ai, the relevant test is whether a result can survive scrutiny of maturity, attribution, validity and practical fit. That distinction matters: completed evidence is not projected benefit, and individual responsibility is not interchangeable with the wider team’s achievement.
FigureAsia U35 Assessment
Assessment breakdown
84.7out of 100
Substantive 2025–2026 contribution
18 / 20
The score reflects completed 2025–26 work in wearables, multimodal biomarkers and health ai, assessed at the documented maturity of large-cohort digital-biomarker research.
Verified impact
12 / 15
Impact credit is limited to the measured study, regulatory, implementation or operating record stated in the profile; unsupported patient benefit is excluded.
Originality and distinction
9 / 10
The work creates or materially advances a distinctive capability within wearables, multimodal biomarkers and health ai rather than relying on title or institutional association.
Field and industry influence
8 / 10
The assessment recognises demonstrated effects on research, product development, care delivery or professional practice, with publicity alone carrying no weight.
Individual agency
8 / 10
Named authorship and the documented role of Assistant Professor of Computational Medicine and Computer Science establish individual responsibility while preserving credit for collaborators.
Durability and trajectory
4.5 / 5
The cited work forms part of a continuing programme, platform or research trajectory rather than a single uncompleted announcement.
Asian significance and global relevance
4.5 / 5
The Asian connection is material to the person's identity, operating base or populations served: Originally from Wuhan, China; now leads a computational-health laboratory in the United States.
Clinical and scientific validity
6.3 / 7
Clinical and scientific validity is calibrated to large-cohort digital-biomarker research, with the profile retaining the evidence boundary attached to the result.
Safety, quality and responsible governance
5.6 / 7
Safety and governance credit reflects accurate regulatory language, study limitations, data stewardship and the refusal to turn early evidence into clinical certainty.
Translation and care-pathway fit
4.8 / 6
The work is scored for its demonstrated fit with a laboratory, regulatory, clinical, operational or public-health pathway, not for projected future adoption.
Access, equity and resource stewardship
4 / 5
Access credit reflects documented reach, capacity, affordability or inclusion while distinguishing service volume from proven clinical outcome.