Home | Andrew T. Campbell

My research focuses on the development of mobile sensing (phones, wearables), intervention and AI technology capable of accurately assessing and managing mental illness (e.g., anxiety, depression, schizophrenia) at population-scale.

I am the Albert Bradley 1915 Third Century professor in computer science at Dartmouth College — and sometime ago a first generation college student. At Dartmouth, I co-direct the HealthX Lab and Emerging Technologies and Data Analytics @ the Center for Technology and Behavioral Health. Before joining Dartmouth, I spent a decade as a tenured associate professor of electrical engineering at Columbia University, where I worked on mobile computing and wireless networks.

My research focuses on the development of mobile sensing (phones, wearables), intervention and AI technology capable of accurately assessing and managing mental illness (e.g., anxiety, depression, schizophrenia) at population-scale.

Prior to Columbia, I spent ten years in the software industry leading the development of wireless packet networks and operating systems. I have been a visiting professor at CMU Rwanda, University of Salamanca, University College London and Cambridge University. During 2016-2018, I joined Google in Mountain View to work on cardiovascular health as a member of the Android wearables group and Verily (formerly Google Life Sciences) as a research scientist working on mental health and AI technology. My research has been covered widely by popular press (New York Times, Washington Post, Financial Times), TV (BBC, CBS) and radio (NPR, CBC).

My research has also received a number of prestigious awards including the ACM SenSys 2018 Test of Time Award for ".. pioneering applying machine learning across mobile phones and servers", the ACM SIGMOBILE 2019 Test of Time Paper Award 2019 for “.. inspiring a huge body of research and commercial endeavors that has continued to increase the breadth and depth of mobile sensing” and the ACM 2022 UbiComp 10-year Impact Award for " .. showing how smartphone microphones could be used to unobtrusively recognize stress from the user’s voice".

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