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Dr. Vincent M.B. Silenzio
Date : May 15, 2018 (Tuesday)
Time : 10:00 – 11:00 am
Venue : Studio 1, 2/F, The HKJC Building for Interdisciplinary Research, 5 Sassoon Road, Pokfulam.
At the height of the early Cold War, the need for an early warning system was recognized to help identify a nuclear attack. The Distant Early Warning (D.E.W.) Line was one of the most well known of the systems developed in response to this challenge. Rapid advances in Artificial Intelligence and Machine Learning, combined with the relentless pace of innovation in social and other communication technologies, continue to create novel opportunities to potentially detect individuals at proximal and distal temporal risk for suicide. In this session, we will address some of the early research efforts in this area, as well as how the example of the D.E.W. Line has informed some the recent work our group has been engaged in to apply machine learning to study mental health using data derived from social technologies. We will conclude by outlining some of the major gaps in the field that remain, and how these may represent some of the most intriguing opportunities for future suicide prevention research.
About the speaker
Dr. Vincent M. B. Silenzio, M.D., M.P.H, is Associate Professor of Psychiatry, Public Health Sciences, and Family Medicine at the University of Rochester. He is Director of the LINCS / Network Science Laboratory and a faculty member in the Center for Study and Prevention of Suicide. He is an affiliated faculty member of the Goergen Institute for Data Science. Dr. Silenzio’s major area of interest is in the development of network analysis, machine learning, and related data science applications to study the biological, psychological, and social mechanisms relevant to suicide prevention, and in HIV/AIDS behavioral research. His research has included sexual minority youth and adult populations in the US, South Africa, Turkey, China, Hong Kong, and other settings in East Asia. Another current focus is on the development of integrated machine learning and mobile health applications to study or target behavioral factors in diseases such as HIV/AIDS, and to develop interventions using machine learning and crowd-computational approaches to target “hidden” or difficult-to-reach populations. In addition, Dr. Silenzio leads the training and educational components of an NIH-funded eCapacity Program, which aims to train researchers from low and middle-income countries in the Asia and Pacific region in data science and mHealth research applications.
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