========== CHIL: Conference on Health, Inference and Learning
Website: http://www.chilconference.org Location: Toronto, Canada Dates: 2nd-4th April 2020 Submission site: https://chil2020.hotcrp.com/ Submissions due: January 13, 2020* (in 10 days!)* ========== 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 (https://www.ml4h.org/), and the Machine Learning for Health (ML4H) Workshop at NeurIPS ( https://ml4health.github.io/2019/). 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 == http://www.chilconference.org/acm-chil-track-1-cfp/ 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 == http://www.chilconference.org/acm-chil-track-2-cfp/ 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 == http://www.chilconference.org/acm-chil-track-3-cfp/ 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 == http://www.chilconference.org/acm-chil-track-4-cfp/ 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: https://chil2020.hotcrp.com/ 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: http://www.chilconference.org/call-for-papers/ ===== 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 - Doctoral Consortium Submissions due – February 3, 2020 - Notification of Acceptance – Feb 17, 2020 - Registration Opens – Feb 17, 2020 - Camera Ready – March 6, 2020 - Conference Date – April 2-4, 2020 ===== Doctoral Consortium ===== The 2020 ACM Conference on Health, Inference, and Learning (CHIL) is excited to announce our inaugural Doctoral Consortium. This event, targeted towards current PhD student expected to graduate within 2 years or a current postdoctoral researcher who graduated no more than 1 year ago. It offers an unparalleled opportunity to get valuable feedback, engagement with excellent peers and experienced researchers, stimulating discussions, and possibly new collaborations. Submission Deadline is February 3, 2020 - Presentations by attending students on their thesis work - Engaging discussions with other attendees and experienced researchers - Individual meetings with researchers throughout the overall conference - Peer feedback - Panel discussions Find more details here - http://www.chilconference.org/doctoral-consortium-call-for-phd-students/ ===== Organisers ===== Dr. Marzyeh Ghassemi of University of Toronto, Vector Institute Dr. Tristan Naumann of Microsoft Research Seattle Dr. Joyce Ho of Emory University Dr. Leo Celi of MIT Dr. Shalmali Joshi of the Vector Institute Dr. Andrew Beam of Harvard University Dr. Ziad Obermeyer of University of California, Berkeley Dr. Oluwasanmi Koyejo of University of Illinois at Urbana-Champaign Dr. Avi Goldfarb of Rotman School of Management, University of Toronto Dr. Laura Rosella of Dalla Lana School of Public Health, University of Toronto Dr. Adrian Dalca of MIT and Harvard Medical School Dr. Rajesh Ranganath of NYU Irene Chen of MIT Matthew McDermott of MIT Dr. Katherine Heller at Duke University Dr. Uri Shalit of Technion Dr. Stephanie Hyland of Microsoft Research Cambridge, UK Dr. Danielle Belgrave of Microsoft Research Cambridge, UK Dr. Shakir Mohamed of DeepMind Dr. Alistair Johnson of MIT Dr. Tom Pollard of MIT Dr. Alan Karthikesalingam of Google Health UK Dr. Brett Beaulieu-Jones of Harvard Medical School Sam Finlayson of Harvard Medical School and MIT Emily Alsentzer of Harvard Medical School and MIT Dr. Ahmed Nasir of Trillium Health Partners Bret Nestor of the University of Toronto and the Vector Institute Tasmie Sarker of the University of Toronto and the Vector Institute For questions, email us at i...@chilconference.org For announcements, follow us on Twitter @CHILconference <https://twitter.com/chilconference>
_______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
