Senior Data Scientist
Led original research into the computational quantification of physiological skin properties, developing novel metrics and deep learning systems to measure attributes — including radiance, tone, and inflammation — that previously existed only as qualitative descriptors.
- Developed lambent, a novel composite scoring system quantifying skin radiance from multi-modal physiological features — the first formalized computational measure of this property. Pearson r = 0.64 96.8% extreme-class accuracy
- Built iridis, a perceptual skin tone classification system developed on 2M+ diverse images, incorporating hue variation and human color perception validated with color scientists under D65-calibrated imaging conditions. Clustering methodology recovered structure consistent with Fitzpatrick and ITA scales while extending to 40+ perceptual categories — capturing skin tone variation that existing clinical scales do not represent.
- Developed argus, a CNN-based anomaly detection system for large clinical imaging databases, deployed via AWS SageMaker.
- Worked with real-time capacitive sensor data capturing physiological skin states, building longitudinal profiling systems modeling individual baselines over time — directly analogous to continuous health sensing from wearable devices.
- Investigated computational quantification of hyperpigmentation, redness/inflammation, and hair follicle structure — characterizing previously unmeasured biological attributes from sensor and image data.
- Led A/B testing and causal inference projects using rigorous experimental protocols to isolate causal effects and inform product decisions across diverse populations.