SDSI-biometrics program v6.indd - Stanford Data Science Initiative

Stanford Data Science Initiative
Workshop on
Data Science for Biomedicine
APRIL 2016
sdsi.stanford.edu
Workshop on Data Science for Biomedicine
WEDNESDAY APRIL 13, 2016
FISHER CONFERENCE CENTER, ARRILLAGA ALUMNI CENTER, STANFORD UNIVERSITY
AGENDA
8:30 am
Registration and continental breakfast
9:00 am
Welcome and Introduction
Session
One
Moderated by Hector Garcia-Molina, Professor of Computer Science & Electrical Engineering; Faculty Director, Stanford Data
Science Initiative
9:15 am
Causal inference in era of big data
Mark Cullen, Professor of Medicine; Director, Stanford Center for Population
Health Sciences
9:45 am
Computational approaches to infer and
predict tumor dynamics Christina Curtis, Assistant Professor of Medicine & Genetics; Co-Director,
Molecular Tumor Board, Stanford Cancer Institute
10:15 am
Break
Session
Two
Moderated by Moses Charikar, Professor of Computer Science
10:45 am
Computational genomics
Gill Bejerano, Associate Professor of Developmental Biology, Computer Science,
& Pediatrics (Medical Genetics)
11:15 am
Extracting information about gene-drug
interactions from text
Russ Altman, Professor of Bioengineering, Genetics, & Medicine
11:45 am
Data Commons
Somalee Datta, Director of Bioinformatics, Stanford Center for Genomics &
Personalized Medicine
12:00 pm
Lunch
1:00pm
Panel Discussion on Biomedical Data:
Sources, Applications, and Analytic
Techniques
Steve Eglash, Executive Director, Stanford Data Science Initiative
Moderator:
Euan Ashley, Associate Professor of Medicine & Genetics; Director, Stanford
Center for Inherited Cardiovascular Disease; Director, Stanford Clinical Genomics
Service; Chair, Biomedical Data Science Initiative
Panelists include:
Somalee Datta, Director of Bioinformatics, Stanford Center for Genomics &
Personalized Medicine
Udi Manber, Researcher, National Institutes of Health
Nigam Shah, Associate Professor, Medicine - Biomedical Informatics Research
Gregory Valiant, Assistant Professor, Computer Science
2:15 pm
Break
Session
Three
Moderated by David Heckerman, Distinguished Scientist and Director, Genomics Group, Microsoft
2:45 pm
Big data for individualized medicine
Michael Snyder, Professor and Chair of Genetics; Director, Stanford Center
for Genomics and Personalized Medicine; Co-Principal Investigator, Center for
Personal Dynamic Regulomes
3:15 pm
DeepDive, machine learning
Christopher Ré, Assistant Professor, Computer Science
3:45 pm
Student and postdoc poster preview
presentations
Presenter names in bold in poster listing
4:15 pm
Poster viewing and wine/beer reception
5:30 pm
Meeting ends
POSTERS
Authors
Title
1
Owen R. Phillips , Alexander K. Onopa , Vivian Hsu ,
Joachim Hallmayer 20, Ian Gotlib 21, Lester Mackey 27,
Manpreet K. Singh 20
Utilizing the “big” PNC Data: Brain Structure Determined “malenessfemaleness” and its Relation to Internalizing and Externalizing
Disorders
2
Kun-Hsing Yu 3, 11, Ce Zhang 6, Gerald J. Berry 19,
Russ B. Altman 3, Christopher Ré 6, Daniel L. Rubin 3,
Michael Snyder 11
Understanding Non-Small Cell Lung Cancer Morphology and Prognosis
by Integrating Omics and Histopathology
3
Tim Althoff 6, Rok Sosic 6, Jennifer L. Hicks 1,
Abby C. King 13,26, Scott L. Delp 1, 15, Jure Leskovec 6
The Mobile Device as a Sensor for Physical Activity and Health from
Personal to Planetary Scale
4
Nathan Chenette 22, Kevin Lewi 6, Stephen A. Weis 9,
David J. Wu 6
Practical Order-Revealing Encryption with Limited Leakage
5
6
Hamsa Bastani 8, Mohsen Bayati 12
Online Decision-Making with High-Dimensional Covariates
Gregory McInnes 24, Cuiping Pan 18,
Somalee Datta 24
Open Source and Collaborative Data Science on Cloud
7
Avanti Shrikumar 6, Peyton Greenside 3,
Nasa Sinnott-Amstrong 11, Anshul Kundaje 6, 11
Not Just a Black Box: Interpretable Deep Learning for Genomics
8
Anton V. Sinitskiy 5, Vijay S. Pande 4, 5, 6, 29
Machine Learning from Atomically Resolved Simulations of Proteins
(exemplified by a study of NMDA receptors)
9
Chuan-Sheng Foo 6, Nasa Sinnott-Armstrong 11,
Avanti Shrikumar 6, Johnny Israeli 4, Anshul Kundaje 6, 11
Integrative Deep Learning Models for Predicting Histone Modifications
and Chromatin State
10
Christine B. Peterson 13, Marina Bogomolov 10,
Yoav Benjamini 28, Chiara Sabatti 2
Error-Controlling Strategies for Genome-Wide Association Studies of
High-Dimensional Traits
11
Jessilyn Dunn 11, 16, Denis Salins 11, Xiao Li 11,
Michael Snyder 11
Consumer Wearable Devices for Health Surveillance and Disease
Monitoring
12
Zheng Hu 11, 23, Jie Ding 11, 23, Zhicheng Ma 11, 23,
Ruping Sun 11, 23, Carlos Suarez 19, Christina Curtis 11, 17, 23
Inferring the Dynamics of Metastatic Progression through Spatial
Computational Modeling
13
14
Ritesh Kolte 8, Murat Erdogdu 27, Ayfer Özgür 8
Accelerating SVRG via Second-Order Information
Nathan A. Hammond 24, Isaac Liao 24,
Sowmi Utiramerur 25, Somalee Datta 24
Loom Workflow Engine: Collaboration through Portable, Shareable
Data Analysis
Jose A. Seoane 11, 17, Jake Kirkland 7, 19,
Jennifer Caswell-Jin 11, 17, Gerald Crabtree 7, 19,
Christina Curtis 11, 17, 23
Chromatin Regulators as Drivers of Breast Tumor Progression and
Chemotherapeutic Resistance
15
20
20
1Bioengineering
2 Biomedical Data Science, School of Medicine
3 Biomedical Informatics, School of Medicine
4 Biophysics, School of Medicine
5Chemistry
6 Computer Science
7 Developmental Biology, School of Medicine
8 Electrical Engineering
9 Facebook Inc.
10 Faculty of Industrial Engineering & Management, Technion Israel Institute of Technology
11 Genetics, School of Medicine
12 Graduate School of Business
13 Health Research & Policy, School of Medicine
14 Mathematical & Computational Science
14
15 Mechanical Engineering
16 Mobilize Center, Stanford
17 Oncology, School of Medicine
18 Palo Alto Veterans Institute for Research, VA Palo Alt
19 Pathology, School of Medicine
20 Psychiatry, Division of Child & Adolescent Psychiatry, School of Medicine
21Psychology
22 Rose-Hulman Institute of Technology
23 Stanford Cancer Institute
24 Stanford Center for Genomics & Personalized Medicine
25 Stanford Health Care
26 Stanford Prevention Research Center, School of Medicine
27Statistics
28 Statistics & Operations Research, Tel Aviv University
29 Structural Biology
CURRENT CORPORATE MEMBERS
We are pleased to acknowledge the generous support of our corporate members.
FOUNDING MEMBERS
REGULAR MEMBERS
ABOUT THE STANFORD DATA SCIENCE INITIATIVE (SDSI)
T
he Stanford Data Science Initiative (SDSI) is a universitywide organization focused on core data technologies
with strong ties to application areas across campus. Data
has supported research since the dawn of time, but there has
recently been a paradigm shift in the way data is used. In the
past, data was used to confirm hypotheses. Today, researchers
are mining data for patterns and trends that lead to new
hypotheses. This shift is caused by the huge volumes of data
available from web query logs, social media posts and blogs,
satellites, sensors, medical devices, and many other sources.
Data-centered research faces many challenges. Current data
management and analysis techniques do not scale to the huge
volumes of data that we expect in the future. New analysis
techniques that use machine learning and data mining require
careful tuning and expert direction. In order to be effective,
data analysis must be combined with knowledge from domain
experts. Future breakthroughs will often require intimate and
combined knowledge of algorithms, data management, the
domain data, and the intended applications.
SDSI will meet these challenges by striving to achieve a
number of goals. The initiative will develop new algorithms
and analytical techniques, foster collaboration with domain
scientists generating big data, provide a gateway for corporate
partners, develop shared data analysis tools, provide a
repository of data and software, and develop relevant courses.
The SDSI consists of data science research, shared data and
computing infrastructure, shared tools and techniques,
industrial links, and education. As an expression of its
collaborative approach, the SDSI has strong ties to many
groups across Stanford University including medicine,
computational social science, biology, energy, and theory.
For more information, please visit our website,
sdsi.stanford.edu.