Stanford Data Science Initiative

Workshop on Data Science for Biomedicine

A P R I L 2 0 1 6

sdsi.stanford.edu

Workshop on Data Science for Biomedicine

8:30 am Registration and continental breakfast

9:00 am Welcome and Introduction Steve Eglash, Executive Director, Stanford Data Science Initiative

SessionOne

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 BreakSessionTwo

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 Lunch1:00pm Panel Discussion on Biomedical Data:

Sources, Applications, and Analytic Techniques

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 BreakSession 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

WEDNESDAY APRIL 13, 2016FISHER CONFERENCE CENTER, ARRILLAGA ALUMNI CENTER, STANFORD UNIVERSITY

AGENDA

POSTERSAuthors Title

1 Owen R. Phillips 20, Alexander K. Onopa 20, Vivian Hsu 14, Joachim Hallmayer 20, Ian Gotlib 21, Lester Mackey 27, Manpreet K. Singh 20

Utilizing the “big” PNC Data: Brain Structure Determined “maleness-femaleness” 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 Hamsa Bastani 8, Mohsen Bayati 12 Online Decision-Making with High-Dimensional Covariates

6 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 Ritesh Kolte 8, Murat Erdogdu 27, Ayfer Özgür 8 Accelerating SVRG via Second-Order Information

14 Nathan A. Hammond 24, Isaac Liao 24, Sowmi Utiramerur 25, Somalee Datta 24

Loom Workflow Engine: Collaboration through Portable, Shareable Data Analysis

15 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

1 Bioengineering2 Biomedical Data Science, School of Medicine3 Biomedical Informatics, School of Medicine4 Biophysics, School of Medicine5 Chemistry6 Computer Science7 Developmental Biology, School of Medicine8 Electrical Engineering9 Facebook Inc.10 Faculty of Industrial Engineering & Management, Technion - Israel Institute of Technology11 Genetics, School of Medicine12 Graduate School of Business13 Health Research & Policy, School of Medicine14 Mathematical & Computational Science

15 Mechanical Engineering16 Mobilize Center, Stanford17 Oncology, School of Medicine18 Palo Alto Veterans Institute for Research, VA Palo Alt19 Pathology, School of Medicine20 Psychiatry, Division of Child & Adolescent Psychiatry, School of Medicine21 Psychology22 Rose-Hulman Institute of Technology23 Stanford Cancer Institute24 Stanford Center for Genomics & Personalized Medicine25 Stanford Health Care26 Stanford Prevention Research Center, School of Medicine27 Statistics28 Statistics & Operations Research, Tel Aviv University29 Structural Biology

CURRENT CORPORATE MEMBERSWe are pleased to acknowledge the generous support of our corporate members.

FOUNDING MEMBERS

REGULAR MEMBERS

The Stanford Data Science Initiative (SDSI) is a university-wide 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.

ABOUT THE STANFORD DATA SCIENCE INITIATIVE (SDSI)