First CANSSI-NISS Health Data Science Workshop

May 6-8, 2021

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, and two split-out virtual lunch & networking sessions. 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.

Registration

Conference Registration

$50 registration, $25 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".

Short Course Registration

$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).
Students from NISS Affiliates - this event is Affiliate Award Fund Eligible. (Is your institution a NISS Affiliate?  Check the List of NISS Affiliates.) 
Students from CANSSI Partners or Institutional Members - please send Randy Freret an email at officeadmin@niss.org to request free short course registration. (Is your institution a CANSSI Partner or Institutional Member?  Check the CANSSI list.)

 

Organizing Committee 

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)


Become a Sponsor!

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!


Agenda

Link to the Full Program

Plenary Speakers

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 - Info and Registration comining soon!
14:00 - 17:00 Short Course 3 - "Deep Learning" - Instructor: Ming Li, (Amazon)
14:00 - 17:00 Short Course 4 - Info and Registration comining soon!

FRIDAY, MAY 7

10:00 - 10:15  Friday Opening Remarks
10:15 - 11:15  Plenary Talk: Xiao-Li Meng (Harvard University)

11:15 - 12:15  Poster Competition for Students and New Researchers (Top 5 selected submissions will be presented!)

See the full program for competition details!

12:15 - 13:00  Themed Networking Virtual Lunch/Breakfast for Junior Researchers and Students

13:00 - 13:15  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

SATURDAY, MAY 8  

10:00 - 11:00 Plenary Talk: Mary Thompson (University of Waterloo)

11:00 - 12:00 

Late-Breaking Session: AI and Health Data Science

Bin Yu (University of California, Berkeley)
David Buckeridge (McGill University)

12:00 - 12:45 Themed Networking Virtual Lunch/Breakfast for Junior Researchers and Students

12:45 - 13:00 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  David Madigan (Northeastern University)
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

 

Event Type

Host

National Institute of Statistical Sciences
Canadian Statistical Sciences Institute

Cost

$50 registration, $25 for students.

Location

Plenary Speakers Mary Thompson (University of Waterloo) and Xiao-Li Meng (Harvard University).