Talk Abstracts

A Data-Intensive Approach to Large-scale Social Network Structural Analysis: Identifying Hidden Communities

Seed Fund Update
Angelo Mele 1, Lingxin Hao 2, Gerard Lemson3, 1Dept. of Economics, Carey Business School, Johns Hopkins University, 2Dept. of Sociology, Krieger School of Arts and Sciences, Johns Hopkins University, 3Dept. of Physics & Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University
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A big-data engine for large-scale splicing screens

Seed Fund Update
Ben Langmead1, Seth Blackshaw, Jonathan Ling2, 1Dept. of Computer Science, Whiting School of Engineering, Johns Hopkins University, 2Dept. of Neuroscience, School of Medicine, Johns Hopkins University

Artificial Intelligence at the Edge meets Big Data and HPC in the Cloud

KEYNOTE SPEAKER
Pete Beckman, PhD, Computing, Environment & Life Science at Argonne National Laboratory and Northwestern University/Argonne Institute for Science & Engineering
The number of network-connected devices (sensors, actuators, instruments, computers, and data stores) now substantially exceeds the number of humans on this planet. Billions of things that sense, think, and act are connected to a planet-spanning network of cloud and high-performance computing centers that contain more computers than the entire Internet did just a few years … Continued

Closing Remarks

S. Alexander Szalay, PhD, Depts. of Physics & Astronomy and Computer Science, Johns Hopkins University

Data-Driven Discovery in the Next Generation Sky Survey

KEYNOTE SPEAKER
Tony Tyson, PhD, DSci, Physics Department, University of California, Davis
Thanks to advances in software, microelectronics, and large optics fabrication, a new type of sky survey will soon begin. Large Synoptic Survey Telescope will cover the sky deeply every week for ten years. Hundreds of petabytes of high dimensional complex data will be mined and compared with Exascale simulations. After reviewing the LSST project, I … Continued

Data-driven Prediction of Risk of Sudden Cardiac Death

Seed Fund Update
Natalia A. Trayanova 1, Katherine C. Wu2, Dan M. Popescu3, 1Dept. of Biomedical Engineering and Medicine, School of Medicine, Johns Hopkins University, 2Dept. of Medicine, Division of Cardiology, School of Medicine, Johns Hopkins University, 3Dept. of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University

Developing an Analytics Software Tool to Identify Patients Diagnosed with Heart Failure and at High Risk of 30-day Hospital Readmissions based on Physiologic and Clinical Data Obtained from Electronic Health Records

Seed Fund Update
Nauder Faraday1, Alexis Battle2, Kasper Hansen3, Ali Afshar 4, 1Dept. of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University, 2Dept. of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, 3Dept. of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 4 Dept. of Anesthesiology and Critical Care Medicine, School of Medicine, Johns Hopkins University

Harnessing Big Data for Population Health: Advancing Natural Language Processing Techniques to Extract Social-Behavioral Risk Factors from Free Text within Large Electronic Health Record Systems

Seed Fund Update
Jonathan Weiner, Hadi Kharrazi, Elham Hatef* 1, Mark Dredze 2, Christopher Chute 3, 1Center for Population Health Information Technology, Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, 2Center for Language and Speech Processing & Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, 3School of Medicine & Chief Research Information Officer, Johns Hopkins Health System

Is Geo-Location Information Helpful in Trading the Spread between Under Armour and Nike?

Seed Fund Update
Jim Kyung-Soo Liew 1, Tamas Budavari 2, 1Dept. of Finance, Carey Business School, Johns Hopkins University, 2Dept. of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University

Opening Remarks

S. Alexander Szalay, PhD, Depts. of Physics & Astronomy and Computer Science, Johns Hopkins University

SciServer in the Materials Science Domain: From Data to Discovery

David Elbert, Dept. of Earth & Planetary Sciences, Krieger School of Arts & Sciences, Johns Hopkins University

SciServer: Supporting Science Projects

Gerard Lemson, PhD, Institute for Data Intensive Engineering and Science, Johns Hopkins University

The Materials Genome Initiative and AI

KEYNOTE SPEAKER
Jim Warren, PhD, Director of the Materials Genome Program, Material Measurement Laboratory, NIST
The US Materials Genome Initiative is now more than seven years old. With a goal of accelerating the discovery, design, development, and deployment of new materials into manufactured products, the MGI is focused on the creation of a materials innovation infrastructure. My institution, the National Institute of Standards and Technology (NIST), has framed its support … Continued

Toward a National Research Storage Substrate: The Open Storage Network

Alainna White, Institute for Data Intensive Engineering Science, Johns Hopkins University

Transformative Research at MARCC

Jaime Combariza, PhD, Maryland Advanced Research Computing Center

Using epidemiological and simulation data to inform the testing of autonomous vehicles

Seed Fund Update
Johnathon Ehsani1, Tak Igusa2, Hadi Kharrazi3, 1 Center for Injury Research and Policy, Dept. of Health Policy and Management, Dept. of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, 2Center for Systems Science and Engineering, Dept. of Civil Engineering, Whiting School of Engineering, Johns Hopkins University, 3Center for Population Health Information Technology, Dept. of Health Policy and Management, School of Public Health, Johns Hopkins University
Autonomous vehicles (AVs) have the potential to transform mobility and reduce the burden of motor vehicle crashes. Before this promising future can become reality, however, there is a need for extensive testing of AVs. As industries aggressively roll out testing plans, they have found that the most challenging questions are on the location and timing … Continued