ACM today announced that Ashish Sharma is the recipient of the ACM Doctoral Dissertation Award for his dissertation “Human-AI Collaboration to Support Mental Health and Well Being,” toward a PhD earned at the University of Washington. Sharma is a Senior Applied Scientist at the Microsoft Office of Applied Research.
Sharma developed fundamental advances in natural language processing to positively impact the mental health of many people. His approach involves constructing new machine learning models and algorithms that demonstrate psychological and societal understanding.
Importantly, Sharma’s work addresses the longstanding challenge that access to mental health services can be expensive. His most recent AI-supported mental health tool, for example, has been publicly deployed and used by a group of more than 160,000 people, the majority of whom are low-income. More than fifty percent of the people using these tools report a household income of less than $40,000 per year.
In developing these machine learning models and algorithms, Sharma incorporates principle-based ethical frameworks. Several mental health and technology organizations have adopted these tools, including Mental Health America and TalkLife.
Honorable Mentions for the ACM Doctoral Dissertation Award go to Alexander (Zander) Kelley for his dissertation “Explicit Pseudorandom Distributions for Restricted Models of Computation” toward a PhD earned at the University of Illinois Urbana-Champaign; and Sewon Min for her dissertation “Rethinking Data Use in Large Language Models” toward a PhD earned at the University of Washington.
