Towards Saving Lives with Natural Language Processing

Andrew Schwartz

Dept. of Computer Science

Stony Brook

Analyzing language use patterns is proving to be a valuable and unique approach to understanding the psychological, social, and health factors of people. On the individual level, Facebook and Twitter have been found predictive of mental health, personality, demographics, and occupational class (among others). At the community or county-level, Twitter has been found predictive of flu and allergy outbreaks, life satisfaction, atherosclerotic heart disease mortality, health behavioral risk factors, excessive drinking, and HIV prevalence. While these techniques have shown robust links over a plethora of important aspects of human life, it is not clear whether any lives have been saved, at least directly, by such work. At their core, some barriers to improving health care and saving lives are likely not NLP or even AI problems, but others are perhaps technical in nature and suggest changing the way we model data.

This seminar will have two parts: a presentation and a discussion. I will start by going over recent and on-going work toward predicting mental health outcomes --- depression, addiction relapse, future psychological distress --- from human language use patterns. Then, I will present an imperfect vision of a future where NLP helps to save lives and open the floor for discussion of technical barriers and whether such a vision is practical.

Biography: Andrew Schwartz received his PhD in Computer Science from the University of Central Florida in 2011 with research on acquiring lexical semantic knowledge from the Web. He then joined the University of Pennsylvania where he was a Postdoctoral Research Fellow and later Visiting Assistant Professor in Computer & Information Science. He is Lead Research Scientist for the World Well-Being Project, a multidisciplinary group of Computer Scientists and Psychologists studying physical and psychological well-being based on language in social media.

Research challenges in using computer vision in robotics systems

Abstract
The past decade has seen a remarkable increase in the level of performance of computer vision techniques, including with the introduction of effective deep learning techniques. Much of this progress is in the form of rapidly increasing performance on standard, curated datasets. However, translating these results into operational vision systems for robotics applications remains a formidable challenge. This talk with explore some of the fundamental questions at the boundary between computer vision and robotics that need to be addressed. This includes introspection/self-awareness of performance, anytime algorithms for computer vision, multi-hypothesis generation, rapid learning and adaptation. The discussion will be illustrated by examples from autonomous air and ground robots.

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the Ph.D. program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend.

The seminar will be taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu.

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the Ph.D. program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend.

The seminar will be taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu.

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the Ph.D. program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend.

The seminar will be taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu.