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Workshop on Computational Approaches to Mental Health @ ICML 2021
Niranjani Prasad · Caroline Weis · Shems Saleh · Rosanne Liu · Jake Vasilakes · Agni Kumar · Tianlin Zhang · Ida Momennejad · Danielle Belgrave

Sat Jul 24 06:20 AM -- 02:00 PM (PDT) @ None
Event URL: https://sites.google.com/view/ca2mh/ »

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 the key opportunities and challenges in developing solutions for this domain, and discussing the progress made.

Author Information

Niranjani Prasad (Microsoft Research Cambridge)
Caroline Weis (ETH Zurich)
Shems Saleh (Vector institute)
Rosanne Liu (ML Collective; Google)
Jake Vasilakes (University of Manchester)
Agni Kumar (Apple)
Agni Kumar

Agni Kumar is a Research Scientist on Apple’s Health AI team. She studied at MIT, graduating with an M.Eng. in Machine Learning and B.S. degrees in Mathematics and Computer Science. Her thesis on modeling the spread of healthcare-associated infections led to joining projects at Apple with applied health focuses, specifically on understanding cognitive decline from device usage data and discerning respiratory rate from wearable microphone audio. She has published hierarchical reinforcement learning research and predictive analytics work in conferences and journals, including EMBC, PLOS Computational Biology and Telehealth and Medicine Today. She was a workshop organizer for ICML’s first-ever *Computational Approaches to Mental Health* workshop in 2021. She has also volunteered at WiML workshops and served as a reviewer for NeurIPS. For joy, Agni leads an Apple-wide global diversity network about encouraging mindfulness to find pockets of peace each day.

Tianlin Zhang (University of Manchester)
Ida Momennejad (Microsoft Research)
Danielle Belgrave (Microsoft Research / Imperial)

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