2015 Annual Meeting: Big data and statistics

When:  Jun 5, 2015 from 12:45 to 17:00 (CT)
Associated with  Wisconsin Chapter

The Wisconsin Chapter of the American Statistical Association (ASA)

proudly presents

The 2015 Annual Meeting:  Big Data and Statistics

sponsored by 

The ASA Section on Statistical Graphics 

Revolution Analytics and 

Marquette University

When:  June 5
Where: Marquette University 
Emory T. Clark Hall Rm. 111 (NOTE: building/room change!)
https://www.google.com/maps/place/570+N+16th+St,+Milwaukee,+WI+53233

Parking: 16th Street Parking Structure (NOTE: parking change!)
https://www.google.com/maps/place/749+N+16th+St,+Milwaukee,+WI+53233

11:30-12:45       Roundtable Luncheons on Big Data topics (Alumni Memorial Union 254)
12:45-01:00       Registration
01:00-01:15       Welcome and Introduction
01:15-02:15       First Keynote
02:15-02:30       Break 
02:30-03:00       Networking Opportunity
03:00-04:00       Second Keynote
04:00-05:00       Third Keynote 
05:30-?           Dinner with the Speakers


Current members of WI Chapter or Section on Statistical Graphics; and students: $20
New/former members: $29

You can make reservations via PayPal <elsmith@mcw.edu> or 
send a check made out to The Wisconsin Chapter of the ASA

The Medical College of Wisconsin
Center for Patient Care & Outcomes Research
PO Box 26509
Milwaukee, WI  53226
ATTN: Elizabeth Smith


Keynote Speakers

Yuan Ji
Director of Biomedical Informatics 
NorthShore University HealthSystem
Associate Professor, University of Chicago
http://health.bsd.uchicago.edu/yji/index.html

Title: Zodiac -- A comprehensive depiction of genetic interactions in
cancer by integrating TCGA data

Abstract: The Cancer Genomes Atlas (TCGA) data are unique in that
multimodal measurements across genomics features, such as copy number,
DNA methylation, and gene expression, are obtained on matched tumor
samples. The multimodality provides an unprecedented opportunity to
investigate the interplay of these features. Graphical models are
powerful tools for this task that address the interaction of any two
features in the presence of others, while traditional correlation- or
regression-based models cannot. We introduce Zodiac, an online
resource consisting of two main components, 1) a large database
containing nearly 200 million interaction networks of multiple
genomics features produced by applying novel Bayesian graphical models
on TCGA data through massively parallel computation, and 2) analytics
tools that perform high-quality inference on data-enhanced
networks. Setting a new way of integrating TCGA data, Zodiac, publicly
available at <http://www.compgenome.org/ZODIAC>, is expected to
facilitate the generation of new knowledge and hypotheses by the
community.


David Smith
Chief Community Officer
Revolution Analytics 
https://www.linkedin.com/in/dmsmith

Title: Reproducible Data Science with R

Abstract: Good data science is reproducible. If someone else can’t
independently replicate your results from your data, the consequences
can be severe. Using an open-source data science language like R is a
good first step for reproducibility, and I’ll introduce a new system
for R that manages a changing ecosystem of versions and packages to
ensure reliable results.


Ming Yuan
Professor, Department of Statistics
Senior Investigator, Morgridge Institute for Research
University of Wisconsin-Madison
http://www.stat.wisc.edu/~myuan

Title: Journey into the "world of data"

Abstract: We live in an information era. Immediate access to copious
amounts of data provides unprecedented opportunities but also creates
new challenges, particularly on how to turn them into actionable
insights. Through several examples from my recent academic research, I
will share with you a few lessons learned in dealing with these new
challenges.

Location

Marquette University Emory T. Clark Hall Rm. 111
570 N 16 St.
Milwaukee, WI 53233

Contact

Elizabeth Smith

elsmith@mcw.edu