Akshaye Shenoi

About

Akshaye Shenoi is a PhD candidate at ETH Zurich working at the intersection of artificial intelligence, ubiquitous computing, and digital health. His research explores how physiological and psychological processes interact, using wearable sensor data and computational methods to better understand mental health in everyday life. Rather than treating health signals as isolated measurements, he focuses on how patterns across time can reveal meaningful insights about psychiatric states and their progression. What makes Akshaye’s work especially compelling is its combination of methodological rigor and real-world relevance. Drawing on experience that spans advanced engineering and applied research contexts, he brings together time series modelling, pattern recognition, and emerging language-based AI approaches to tackle one of healthcare’s most difficult challenges: improving the precision with which psychiatric disorders are assessed and, ultimately, supported. His work reflects a broader ambition to make digital health systems more adaptive, clinically useful, and human-centered.