BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:America/Phoenix X-WR-TIMEZONE:America/Phoenix BEGIN:VEVENT UID:1589@azbio.org DTSTART;TZID=America/Phoenix:20210413T080000 DTEND;TZID=America/Phoenix:20210414T130000 DTSTAMP:20210411T233942Z URL:https://www.azbio.org/events/machine-learning-in-genomics-tools-resour ces-clinical-applications-and-ethics SUMMARY:Machine Learning in Genomics: Tools\, Resources\, Clinical Applicat ions and Ethics - - 13 Apr 21 08:00 DESCRIPTION:\n\n\n\n\n\n\nMachine Learning in Genomics: Tools\, Resources\, Clinical Applications and Ethics\n\n\n\n\n\n\n\n\n\n\n\n\nEvent Details\n \n\n\n\n\nThe \;NHGRI Genomic Data Science Working Group \;of the National Advisory Council for Human Genome Research is hosting the Machine Learning in Genomics virtual workshop \;on April 13 \;- \;Apr il \;14\, 2021.\n\nThe workshop has a capacity of 1000 participants in Zoom. Anyone joining after the limit is reached will be redirected to a l ivestream of the workshop.\n\nRecordings will be made available following the meeting on this webpage.\n\nDay 1 Agenda \; \;| \; \;D ay 2 Agenda\n\nOrganizing Committee\n\n\n\n\n\nApr 13\, 2021\, 11:00 AM&nb sp\;to\nApr 14\, 2021\, 4:00 PM\n\n\n\n\nWorkshop Registration \;\n\nR egistration is free and open to all.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAddition al Information\n\n\n\nThe primary purpose of the workshop is to stimulate discussion around the opportunities and obstacles underlying the applicati on of machine learning (ML) methods to basic genome sciences and genomic m edicine\, to define the key scientific topic areas in genomics that could benefit from ML analyses and NHGRI’s unique role at the convergence of g enomic and ML research. \;\n\nThe data-intensive fields of genomics an d ML are in an early stage of convergence. This workshop \;will \; include \;a combination of \;lectures from \;ML\, genomics\, a nd ethics researchers with substantial time set aside for \;virtual Q& amp\;A sessions \;in which \;all attendees\, \;irrespective of  \;expertise and \;background\, \;are encouraged to participat e. \;Topics of interest within genomics cover the full spectrum of bas ic and clinical research. \;The workshop \;will also \;focus o n \;the \;ethical aspects of \;ML \;applications \;to genomics data. \;ML scientists without any connection to genomics are as welcome to join as those already applying their analytical methods to g enomic data. \;\n\n\n\n\n\n\n \;\n\n\n\n\n\n\nDay 1 Agenda\n\n\n\n \n April 13\, 2021\n11:00 a.m. – 5:00 p.m. EDT\n 11:00 a.m. - WelcomeC o-chairs:\nTrey Ideker\, Ph.D.\, University of California San Diego\nMark Craven\, Ph.D.\, University of Wisconsin\n\nSpeaker: \;\nEric Green\, M.D.\, Ph.D.\, \;Director\, National Human Genome Research Institute\n Keynote Session: What are the opportunities and challenges for ML in gen omics research?\n\nModerator: \;\nShannon McWeeney\, Ph.D.\, Oregon He alth and Sciences University\n \n11:10 a.m. - \;Eric Topol\, M.D.\,&n bsp\;Scripps Research\nGenomics in the Machine Learning Space\n\n11:40 a.m . - \;Brad \;Malin\, Ph.D.\, \;Vanderbilt University Medical C enter\nChallenges and Opportunities for Machine Learning in Genomics\n\n12 :10 p.m. - \;Q&\;A Session\n 12:40 p.m. - Break\n Session 1: Algo rithm development and machine learning approaches in genomics\n\nModerator s: \;\nTrey Ideker\, Ph.D.\, University of California San Diego\nAntho ny Philippakis\, M.D.\, Ph.D.\, Broad Institute\n \n1:00 p.m. - \;Jia n Peng\, Ph.D.\, \;University of Illinois at Urbana-Champaign\nMachine learning algorithms for structural and functional genomics\n\n1:25 p.m. -  \;Sara Mathieson\, Ph.D.\, Haverford College\nAutomatic evolutionary inference using Generative Adversarial Networks\n\n1:50 p.m. - \;Chris tina Leslie Ph.D.\, \;Memorial Sloan-Kettering Cancer Center\nThe 3D g enome and predictive gene regulatory models\n\n2:15 p.m. - Q&\;A Sessio n\n 2:45 p.m. - Break\n Session 2: Ethical\, Legal and Social Implicatio ns (ELSI) of machine learning in genomics\n\nModerators:\nDave Kaufman\, P h.D.\, NHGRI\nEimear Kenny\, Ph.D.\, Icahn School of Medicine at Mount Sin ai\n \n3:10 p.m. - \;Pamela Sankar\, Ph.D.\, \;University of Penn sylvania\nMachine learning: broadening the scope of ethical questions\n\n3 :35 p.m. - \;Varoon Mathur\, \;AI Now Institute\nConsiderations fo r building ethical and socially responsible AI systems in Health Care\n\n4 :00 p.m. - \;Danton Char\, M.D.\, \;Stanford University\nIdentifyi ng and Anticipating Ethical Challenges with Machine Learning for Genomics\ n\n4:25 p.m. - Q&\;A Session\n 4:55 p.m. - Day 1 Wrap-up\n\nCo-chairs: \nTrey Ideker\, Ph.D.\, University of California San Diego\nMark Craven\, Ph.D.\, University of Wisconsin\n 5:00 p.m. - Adjourn\n\n\n\n\n\n\n\n\n&n bsp\;\n\n\n\n\n\n\nDay 2 Agenda\n\n\n\n\n April 14\, 2021\n11:00 a.m. – 4:00 \;p.m. EDT\n 11:00 a.m. - Day 2 Opening\n\nCo-chairs:\nTrey Ide ker\, Ph.D.\, University of California San Diego\nMark Craven\, Ph.D.\, Un iversity of Wisconsin\n Session 3: Data and resource needs for machine le arning in genomics\n\nModerators:\nChristina Leslie\, Ph.D.\, Memorial Slo an Kettering Cancer Center\nMark Craven\, Ph.D.\, University of Wisconsin\ n \n11:10 a.m. - \;Alexis Battle\, Ph.D.\, Johns Hopkins University\n Integrative machine learning for regulatory genomics\n\n11:35 a.m. - \ ;Anshul Kundaje\, Ph.D.\, \;Stanford University\nMachine learning for genomic discovery\n\n12:00 p.m. - \;Gregory Cooper\, M.D.\, Ph.D.\,&nb sp\;University of Pittsburgh\nPersonalized Causal Machine Learning Using G enomic Data\n\n12:25 p.m. - Q&\;A Session\n 12:55 p.m. - Break\n Sess ion 4: Machine learning in clinical genomics\n\nModerators:\nCasey Overby Taylor\, Ph.D.\, \;Johns Hopkins University\nEric Boerwinkle\, Ph.D.\,  \;University of Texas Health Science Center\n \n2:00 p.m. - \;Su -In Lee\, Ph.D.\, \;University of Washington\nExplainable AI for cance r precision medicine\n\n2:25 p.m. - \;Sriram Sankararaman\, Ph.D.\,&nb sp\;University of California Los Angeles\nMachine Learning for large-scale genomics\n\n2:50 p.m. - \;Russ Altman\, M.D.\, Ph.D.\, \;Stanford University\nDeep learning to predict the impact of rare variation in drug metabolism genes\n\n3:15 p.m. - Q&\;A Session\n 3:45 \;p.m. - Day 2 Wrap-up\n\nCo-chairs:\nTrey Ideker\, Ph.D.\, University of California S an Diego\nMark Craven\, Ph.D.\, University of Wisconsin\n 4:00 p.m. - Adj ourn\n\n\n\n\n\n\n\n\n \;\n\n\n\n\n\n\nOrganizing Committee\n\n\n\n\nN HGRI Genomic Data Science Working Group\n\nCarolyn Hutter\n\nValentina Di Francesco\n\nShurjo Sen\n\nKris Wetterstrand\n\nNatalie Kucher\n\nSean Gar in\n\n\n\n\n\n\n\n CATEGORIES:Conventions and National Events END:VEVENT BEGIN:VTIMEZONE TZID:America/Phoenix X-LIC-LOCATION:America/Phoenix BEGIN:STANDARD DTSTART:20200413T080000 TZOFFSETFROM:-0700 TZOFFSETTO:-0700 TZNAME:MST END:STANDARD END:VTIMEZONE END:VCALENDAR