[Please Note: This session has already occurred. Go to the News Story for this event to read about what happened.]
Nowadays, statisticians and health data scientists actively work together on the frontier of biological, medical, and public health research. The transdisciplinary collaboration not only develops the modern foundations of Health Data Science but also accelerates the pace of scientific discovery and innovation.
The First CANSSI-NISS Health Data Science Workshop will be held virtually on May 7-8, 2021, with pre-workshop short courses on May 6th. The workshop brings statisticians and health data scientists from the U.S. and Canada together to explore current approaches and new challenges for learning Big Data in Health Data Science.
The two-day workshop consists of two keynote presentations, four invited sessions, a poster competition for students and new researchers, a late-breaking session on AI and Health Data Science, and a networking happy hour. The themed invited sessions will explore current approaches and new challenges in
(i) Statistical Issues with COVID-19,
(ii) Statistical Problems in Imaging and Genetics,
(iii) Causal Inference for Big Health Data, and
(iv) Methods for Electronic Health Data.
Earlybird Registration Deadline - May 3, 2021 (Registration fees $100 - $50 for students after May 3. Please Also Note: Registration closes when the allocated spaces are filled.)
Conference Registration (all prices in US $)
$100 registration, $50 for students. Select the registration options on the right hand side of the page, check the box 'I am not a Robot" and then "Register for this Event" using your institutional email address.
Due to the Limitations of our Registration System - Please register and pay for conference registration before registering for Short Courses.
Short Course Registration (all prices in US $)
$35 registration per short course. Select the registration options on the right hand side of the page, check the box 'I am not a Robot" and then "Register for this Event". Do this for each course you are interested in attending, (maximum one short course per AM or PM session) using your institutional email address.
Students/Faculty from NISS Affiliates - this event is Affiliate Award Fund Eligible. (Is your institution a NISS Affiliate? Check the List of NISS Affiliates.) Please register & pay. Reimbursment will be after entire workshop is over.
Students from CANSSI Partners or Institutional Members - AFTER THE EVENT, NISS affiliates and CANSSI partner students, can send Randy Freret an email (firstname.lastname@example.org) to request reimbursement only if they attended one or more short course(s) and the conference. (Is your institution a CANSSI Partner or Institutional Member? Check the CANSSI list.)
Due to the Limitations of our Registration System - Please register and pay for one Short Course at a time. Feel free to email email@example.com for registration assistance/questions.
Joel A. Dubin (University of Waterloo)
James L. Rosenberger (Penn State University and National Institute of Statistical Sciences)
Lingzhou Xue (Penn State University and National Institute of Statistical Sciences)
Yeying Zhu (University of Waterloo)
NISS Invites corporations, institutions and individuals to sponsor events that are hosted by NISS. Sponsorship helps to defray the cost of organizing events and is a great way to give your organization visibility that targets statisticians and other related professions who attend NISS events! Learn More
Mary Thompson (University of Waterloo)
Mary is a Distinguished Professor Emerita in the Department of Statistics and Actuarial Science at the University of Waterloo, founding Scientific Director of the Canadian Statistical Sciences Institute (CANSSI), an elected Fellow of the Royal Society of Canada in 2006, and recipient of the Statistical Society of Canada's Gold Medal in 2003 and Lise Manchester Award in 2006 as well as the COPSS Elizabeth L. Scott Award in 2010.
Xiao-Li Meng (Harvard University)
Xiao-Li is the Whipple V. N. Jones Professor of Statistics at Harvard University, and the founding Editor-in-Chief of the Harvard Data Science Review since 2018. He served as Dean, Graduate School of Arts and Sciences (2012-18) and Chair of the Department of Statistics at Harvard University (2004-12). He received the COPSS Presidents' Award in 2001 and served as president of IMS in 2019. He has written numerous research papers about Markov chain Monte Carlo algorithms and other statistical methodology. He edited the journals Bayesian Analysis from 2003 to 2005 and Statistica Sinica from 2005 to 2008. Meng received his B.Sc. from Fudan University in 1982 and his Ph.D. in statistics from Harvard University in 1990. He was elected a fellow of the Institute of Mathematical Statistics in 1997 and of the American Statistical Association in 2004. He was elected fellow of the American Academy of Arts and Sciences (AAAS) in 2020.
Invited Sessions and Speakers
THURSDAY, MAY 6 All times are Eastern Time
10:00 - 13:00 Short Course 1 - "A Brief Introduction to Causal Inference" - Instructor: Yeying Zhu, (University of Waterloo)
10:00 - 13:00 Short Course 2 - "Introduction to Reinforcement Learning in Precision Medicine" - Instructor: Eric Laber, (Duke University)
14:00 - 17:00 Short Course 3 - "Deep Learning" - Instructor: Ming Li, (Amazon)
14:00 - 17:00 Short Course 4 - "Introduction to Disease Modeling" - Instructor: Rob Deardon (University of Calgary)
FRIDAY, MAY 7
10:00 - 10:15 Friday Opening Remarks
10:15 - 11:15 Plenary Talk: Xiao-Li Meng (Harvard University)
"Personalized Treatments: Sounds heavenly, but where on Earth did they find my guinea pigs?"
11:20 - 12:40 Poster Competition for Students and New Researchers (Top 5 selected submissions will be presented!)
Judges will select the top five posters and these top five will present their posters live to attendees during this Poster Session for Students and New Researchers! A first place award will be given, along with prizes for two runners-up! Submit Your Poster Today!
(EXTENDED DEADLINE FOR SUBMISSIONS: April 30, 2021)
12:45 - 13:15 Lunch Break
Session 1: Statistical Issues with COVID-19:
13:15 - 13:40 Nilanjan Chatterjee (Johns Hopkins University)
13:40 - 14:05 Rob Deardon (University of Calgary)
14:05 - 14:30 Xihong Lin (Harvard T.H. Chan School of Public Health)
14:30 - 14:55 Grace Yi (Western University)
14:55 - 15:00 Q&A
Session 2: Causal Inference for Big Health Data:
15:15 - 15:40 Caroline Uhler (Massachusetts Institute of Technology)
15:40 - 16:05 Debashis Ghosh (Colorado School of Public Health)
16:05 - 16:30 Erica Moodie (McGill University)
16:30 - 16:55 Dylan Small (The Wharton School, University of Pennsylvania)
16:55 - 17:00 Q&A
17:00 - 18:00 - Networking Happy Hour
SATURDAY, MAY 8
10:00 - 11:00 Plenary Talk: Mary Thompson (University of Waterloo)
"The interface of health data science and survey methods"
Late-Breaking Session: AI and Health Data Science
11:05 - 11:35 Bin Yu (University of California, Berkeley)
11:35 - 12:05 David Buckeridge (McGill University)
12:05 - 12:35 Rob Tibshirani (Stanford University)
12:35 - 12:45 Q&A
12:45 - 13:00 Lunch Break
Session 3: Statistical Problems in Imaging and Genetics:
13:00 - 13:25 Brian Caffo (Johns Hopkins Bloomberg School of Public Health)
13:25 - 13:50 Radu Craiu (University of Toronto)
13:50 - 14:15 Linglong Kong (University of Alberta)
14:15 - 14:40 Marina Vannucci (Rice University)
14:40 - 14:45 Q&A
Session 4: Methods for Electronic Health Records (EHR) Data:
15:00 - 15:25 Rebecca Hubbard (University of Pennsylvania)
15:25 - 15:50 Marc Suchard (UCLA)
15:50 - 16:15 Eleanor Pullenayegum (Hospital for Sick Children, and the University of Toronto Dalla Lana School of Public Health)
16:15 - 16:40 Sherri Rose (Stanford University)
16:40 - 16:45 Q&A
16:45 - 17:00 Closing Remarks