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Program

WEDNESDAY, JANUARY 13, 2021

THURSDAY, JANUARY 14, 2021

WEDNESDAY, JANUARY 13, 2021  

Welcome and Opening Keynote 
9:30 – 10:15 A.M.


Welcome and Introduction of Keynote

Keynote Address 
John Quackenbush, Harvard School of Public Health, Boston, Massachusetts

Break 
10:15-10:30 A.M 
Plenary Session 1: Learning from Images: Pathology 
10:30 A.M.-12 P.M


Title to be announced
Andrew H. Beck, PathAI, Cambridge, Massachusetts

Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning 
Phedias Diamandis, University of Toronto – University Health Network, Toronto, ON, Canada 

Title to be announced 
Thomas J. Fuchs, Icahn School of Medicine at Mount Sinai, New York, New York 

Proffered talk from highly rated abstract  

BREAK 
12-12:15 P.M.
Plenary Session 2: Learning from Images: Radiomics 
12:15-1:45 P.M

Deep learning for automated quantification of tumor phenotypes
Ahmed Hosny, Dana Farber Cancer Institute, Boston, Massachusetts

Title to be announced
Hugo Aerts, Brigham and Women’s Hospital, Boston, Massachusetts  

Radiomics: Medical images are quantitative data for diagnosis, prognosis, prediction, follow-up and clinical trials
Philippe Lambin, MAASTRO Clinic, Maastricht, The Netherlands

Proffered talk from highly rated abstract 

Break
1:45-2 P.M.
Plenary Session 3: Learning from Images: Multiplex Imaging and Small Molecule Design
2-3:30 P.M.


Title to be announced
Garry P. Nolan, Stanford University School of Medicine, Stanford, California

Interpreting the cancer genome through physical and functional models of the cancer cell
Trey Ideker, UC San Diego School of Medicine, La Jolla, California 

Title to be announced
Muneeb Sultan, insitro, South San Francisco, California

Proffered talk from highly rated abstract   

Break 
3:30-3:45 P.M.
Plenary Session 4: Learning from Genome Biology
3:45-5:25 PM


Title to be announced
Douglas M. Fowler, University of Washington, Seattle, Washington

Machine learning approaches in cancer genomics

Olga Troyanskaya, Princeton University, Princeton, New Jersey  

AI for variant interpretation
Rachel Karchin, Johns Hopkins University, Baltimore, Maryland

Title to be announced
Quaid Morris, Sloan Kettering Institute, New York, New York

Thursday, January 14, 2021

Panel 1: Development of Data Resources, Data Standards, Access Policy, Reproducibility, Benchmarking  
9:30-11 A.M

Benjamin Haibe-Kains, University Health Network Princess Margaret Hospital, Toronto, ON, Canada 

Jeffrey Tullis Leek, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland

Lincoln D. Stein, Ontario Institute for Cancer Research, Toronto, ON, Canada   

Break 
11–11:15 A.M.
Plenary Session 5: Learning from Clinical Genomics 
11:15 A.M.–12:45 P.M.


Title to be announced
Jinghui Zhang, St. Jude Children’s Research Hospital, Memphis, Tennessee

Exploring the dark corners of DNA in clinical cancer genomics
Matija Snuderl, New York University Langone Medical Center, New York, New York
 
Methods to map the dynamics of tumor progression: Initiation, drug response and metastasis
Dana Pe’er, Memorial Sloan Kettering Cancer Center, New York, New York

Proffered talk from highly rated abstract 

Break
12:45-1 P.M.
Plenary Session 6: Clinical Implementation of Machine Learning Models in Oncology 
1-2:30 P.M. 


AI for precision medicine: Clinical insights 
Constance Lehman, Massachusetts General Hospital, Boston, Massachusetts

Clinical deployment of machine learning based radiation treatment planning 
Thomas Purdie, Techna Institute for the Advancement of Technology for Health, Toronto, ON, Canada  

Multi-scale modeling of cancer patients 
Olivier Gevaert, Stanford University, Stanford, California

Proffered talk from highly rated abstract 

Break 
2:30-2:45 P.M. 
Panel 2: Challenges and Opportunities in Machine Learning Algorithms for Cancer Research 
2:45-4:20 P.M. 


Title to be announced
Regina Barzilay, Massachusetts Institute of Technology, Cambridge, Massachusetts

An interpretable deep learning system for automatic medical image segmentation
Bo Wang, University of Toronto, Toronto, ON, Canada

Cancer genomics with machine learning: An FDA regulatory science perspective 
Weida Tong, Food and Drug Administration-National Center for Toxicological Research, Jefferson, Arkansas

Closing Remarks