ICML 2021 Workshop on
Computational Approaches to Mental Health
24th July 2021
*Link to livestream: https://icml.cc/virtual/2021/workshop/8352*
The rising prevalence of mental illness has posed a growing global burden, with one in four people adversely affected at some point in their lives, accounting for 32.4% of years lived with disability. This has only been exacerbated during the current pandemic, and while the capacity of acute care has been significantly increased in response to the crisis, it has at the same time led to the scaling back of many mental health services. This, together with the advances in the field of machine learning (ML), has motivated exploration of how machine learning methods can be applied to the provision of more effective and efficient mental healthcare, from varied approaches to continual monitoring of individual mental health or identification of mental health issues through inferences about behaviours on social media, online searches or mobile apps, to predictive models for early diagnosis and intervention, understanding disease progression or recovery, and the personalization of therapies.
This workshop aims to bring together clinicians, behavioural scientists and machine learning researchers working in various facets of mental health and care provision, to identify key opportunities and challenges in developing solutions for this domain, and discuss the progress made.
We would like to invite submission of short papers on topics including (but not limited to):
Development of computational models for understanding cognitive processes (such as learning and decision-making) in mental illness
Approaches to passive sensing and signal processing of visual, audio, physiological or multi-modal inputs for assessing human emotions and behaviours
Leveraging static, longitudinal and/or iterative health data for detection, monitoring, or prediction of mental health and wellbeing
ML methods to assist in precision mental health care, predicting health risks, the discovery of disease subtypes, or the development of personalized interventions
Practical challenges in conducting computational research to support mental illness
Reflections on the ethics of ML for the diagnosis and treatment of mental illness
Submissions may be up to four pages in length (excluding references and supplementary material; supplements are unlimited, but will be optional for the reviewers to look at). Submissions should be anonymous and in the ICML 2021 format (see the official style guidelines).
We will not publish archival proceedings, and welcome submissions of work-in-progress, completed work, and papers accepted in other venues.
Sonia Bishop is an associate professor in the Department of Psychology and Helen Wills Neuroscience Institute at UC Berkeley. She directs the computational psychiatry and affective cognitive neuroscience (CPACN) laboratory. Her interests include identifying common and unique disruptions to decision-making in anxiety and depression, delineating attentional dysfunction in anxiety and understanding the neural substrate of the processes concerned. She obtained her PhD from the Institute of Psychiatry at the University of London and has worked previously at the MRC Cognition & Brain Sciences Unit in Cambridge, the Department of Psychology at Cambridge University and the Nuffield Department of Clinical Neurosciences at Oxford University.
Peter Dayan is a Director at the Max Planck Institute for Biological Cybernetics and a Professor at the University of Tübingen. His interests include affective decision making and neural reinforcement learning. He received his PhD from the University of Edinburgh, and has previously worked at the Salk Institute, the University of Toronto, MIT and University College London.
Munmun De Choudhury is an Associate Professor of Interactive Computing at Georgia Tech. Dr. De Choudhury is best known for laying the foundation of a line of research that develops computational techniques to responsibly and ethically employ social media in understanding and improving our mental health. To do this work, she adopts a highly interdisciplinary approach, combining social computing, machine learning, and natural language analysis with insights and theories from the social, behavioral, and health sciences. Dr. De Choudhury has been recognized with the 2021 ACM-W Rising Star Award, 2019 Complex Systems Society – Junior Scientific Award, over a dozen best paper and honorable mention awards from the ACM and AAAI, and extensive coverage in popular press like the New York Times, the NPR, and the BBC. Earlier, Dr. De Choudhury was a faculty associate with the Berkman Klein Center for Internet and Society at Harvard, a postdoc at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University.
Claire Gillan is Associate Professor of Psychology at Trinity College Dublin, where she runs a research lab (www.gillanlab.com) in the Trinity College Institute of Neuroscience and Global Brain Health Institute. Her lab is interested in big data approaches to mental health science, and there are active projects aiming to develop objective tests that can be used to allocate treatments in mental health more effectively (www.antidepressantresearch.com) and predict mental illness or cognitive decline in older adults before it happens (www.neureka.ie).
The lab also has strands in basic cognitive neuroscience, where they work to understand the fundamentals of goal-directed decision-making, metacognitive processes and how people form habits. A final key area of focus is on how we define mental health and illness, with research projects applying transdiagnostic and dimensional approaches to understanding compulsiveness and a complex of anxiety and depression in the general population, as well as in diagnosed patients. The Gillan Lab is currently supported by an ERC Starting Grant, an SFI Frontiers for the Future Award and a fellowship from MQ: transforming mental health.
Akane Sano is an Assistant Professor at Rice University, Department of Electrical Computer Engineering, Computer Science, and Bioengineering. She directs the Computational Wellbeing Group. She is also a member of Rice Scalable Health Labs. Her research focuses on affective, ubiquitous, and wearable computing, and biobehavioral sensing and analysis/modeling. She received her Ph.D. at the Massachusetts Institute of Technology. Her recent awards include the NSF Career Award, the Best Paper Award at IEEE BHI 2019 conference, and the Best Paper Award at the NIPS 2016 Workshop on Machine Learning for Health.
After obtaining a Bachelor’s and Master’s degree in Psychology, an MD, and a Specialization in Psychiatry, Dr. Daniel Vigo worked in clinical, research, teaching, and leadership positions across sectors (public hospitals, NGOs, and for-profit organizations). He developed the first Assertive Community Treatment Department in Argentina, a collaboration between Proyecto Suma, King’s College (UK) and Columbia University (US). He has published several peer-reviewed articles, book chapters, and reports on diverse mental health issues. He obtained his Doctorate at Harvard, where he focused on public mental health, specifically burden of disease estimation, service improvement, and health systems assessment. He is a Lecturer at Harvard’s Department of Global Health and Social Medicine, an Assistant Professor at Simon Fraser University, an Advisor to PAHO and to the Lancet Commission on NCDs and Injuries for the poorest billion, a member of the World Mental Health Surveys Initiative, and holds a clinical and research appointment at St Paul's Hospital/UBC.
Dr. Vigo is currently leading a number of projects including as PI of Needs-Based Planning for Mental and Substance Use Disorder Services in British Columbia, and several other Canada-based projects. At Harvard, he is part of a new initiative on Global Mental Health and Sustainable Development led by Vikram Patel, and of the World Mental Health Surveys Initiative led by Ron Kessler.
Speaker Panels:
Understanding Mechanisms in Mental Health Disorders
Moderator: Ida Mommennejad, Microsoft Research NYC
Developing Technologies for Mental Health and Wellbeing
Moderator: Niranjani Prasad, Microsoft Research Cambridge
Key Dates
5 June 2021 (AoE)
26 June 2021 (AoE)
24 July 2021
Paper submission deadline (extended)
Acceptance notification deadline
Workshop date