Call for Papers: Conference on Health, Inference and Learning (CHIL) 2020

Jan 13, 2020
All day


Location: Toronto, Canada

Dates: 2nd-4th April 2020

Submission site: 

The ACM Conference on Health, Inference and Learning (CHIL), targets a cross-disciplinary representation of clinicians and researchers (from industry and academia) in machine learning, health policy, causality, fairness, and other health-related areas. ACM CHIL 2020 builds on the successes of the ML4H Unconference, held in Toronto (, and the Machine Learning for Health (ML4H) Workshop at NeurIPS (

Health problems impact human lives, and data plays a fundamental role in addressing health. Machine learning’s ability to extract information from data, paired with the centrality of data in health, makes research in machine learning for health crucial. Our goal is to illuminate the challenges and opportunities in machine learning in health and health-related fields, bringing in technical innovation to important issues.

CHIL combines unconference-style breakout sessions on specialist topics with disseminating peer-reviewed contributions in the form of spotlight talks, keynotes from leading researchers, and workshops. CHIL solicits work across a variety of disciplines, including machine learning, statistics, epidemiology, health policy, operations, and economics. Specifically, authors are invited to submit 8-10 page papers (with unlimited pages for references) to each of the tracks described below:

Track 1: Machine Learning: Models, Algorithms, Inference, and Estimation

Advances in machine learning are critical for a better understanding of health. This track seeks contributions in modeling, inference, and estimation in health-focused or health-inspired settings. We welcome submissions that develop novel methods and algorithms, introduce relevant machine learning tasks, or identify challenges with prevalent approaches.

Track 2: Applications: Investigation, Evaluation, and Interpretation

The goal of this track is to highlight works applying robust methods, models, or practices to identify, characterize, audit, evaluate, or benchmark systems. We welcome submissions focused on solving carefully-motivated problems grounded in application, methods which are designed to work particularly robustly (e.g., fail gracefully in practice), scale particularly well either in terms of computational runtime or data required, or work across real-world data modalities and systems.

Track 3: Policy: Impact, Economics, and Society

Algorithms do not exist in the digital world alone: indeed, they often explicitly take aim at important social outcomes. This track considers issues at the intersection of algorithms and the societies they seek to impact. This track welcomes theoretical, methodological, and applied contributions for understanding and accounting for fairness, accountability, and transparency of algorithmic systems and for societal applications including mitigating discrimination, inequality, public health, health systems, policy applications, and other societal impacts from the deployment of such systems in real-world contexts.

Track 4: Practice: Deployments, Systems, and Datasets

The transformation of healthcare through computational approaches is dependent on understanding how to empirically evaluate these systems, widely sharing tools for conducting research, and publicly accessible data allowing fair comparison of methods. This track seeks descriptions of the implementation or evaluation of informatics-based studies, computer software which has direct utility for medical researchers, and new datasets which support healthcare research.

Submission information

ACM CHIL 2020 submission website:

This system will go online on December 1, 2019.

Double blind peer reviews will be conducted to determine acceptance at the end of January 2020. The proceedings are planned to appear in the ACM Digital Library under the SIG CHIL designation.

Additional details:

Financial support

Those who lack the means to pay for registration or who cannot afford to travel to attend the conference may apply for financial support, which consists of (1) a registration fee waiver and/or (2) a travel grant of a maximum of $1000.

Important dates

Call for Papers – October 30, 2019

Submission System Online – December 1, 2019

Submissions due – January 13, 2020

Notification of Acceptance – Feb 17, 2020

Registration Opens – Feb 17, 2020

Camera Ready – March 6, 2020

Conference Date – April 2-4, 2020


Dr. Marzyeh Ghassemi of University of Toronto, Vector Institute

Dr. Tristan Naumann of MSR Seattle

Dr. Joyce Ho of Emory

Dr. Leo Celi of MIT

Dr. Shalmali Joshi of the Vector Institute

Dr. Andrew Beam of Harvard

Dr. Ziad Obermeyer of Berkeley

Dr. Oluwasanmi Koyejo of UIUC

Dr. Avi Goldfarb of Rotman School

Dr. Laura Rosella of Dalai Lana School of Public Health

Dr. Adrian Dalca of MIT and HMS

Dr. Rajesh Ranganath of NYU

Irene Chen of MIT

Matthew McDermott of MIT

Dr. Katherine Heller of Duke

Dr. Uri Shalit of Technion

Dr. Stephanie Hyland of MSR Cambridge, UK

Dr. Danielle Belgrade of MSR Cambridge, UK

Dr. Shakir Mohamed of DeepMind

Dr. Alistair Johnson of MIT

Dr. Tom Pollard of MIT