Akshaye Shenoi is a PhD candidate at ETH Zurich based at the Singapore-ETH Centre, specializing in the intersection of machine learning, ubiquitous computing, and digital health with a focus on wearable sensor data for psychiatric assessment. Over a decade of experience spans research roles at NUS and TUMCREATE and industry positions at GE, where he worked on cloud migrations and low-power wireless sensing reliability. His work combines theory-informed modeling and signal-processing expertise to reveal physiological–psychological links that can enable non-invasive, scalable mental health sensing and interventions. He has practical systems experience—designing consensus protocols for resource-constrained sensors and privacy-preserving IoT techniques using trusted execution environments—that underpins his applied ML research. A visiting scholar at Dartmouth and mentor/TA during his academic journey, he blends rigorous research with real-world engineering impact. Beyond publications, he aims to translate sensor-driven insights into intelligent health-sensing systems that support healthy longevity at scale.
10 years of coding experience
5 years of employment as a software developer
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Manipal Institute of Technology
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at ETH Zürich
Master of Computing, Master of Computing at National University of Singapore
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