Is the burden of managing your data growing larger every day? Do you have a scalable and robust data management infrastructure in place to store, process, analyze, and transfer vast quantities of data according to your organization’s policies? Is
your organization using new tools and analytical processes such as AI and deep learning that stress your supporting IT infrastructure beyond the expectations of system designers? Managing data has become a prevalent issue in the life sciences industry.
Organizations are spending millions on systems and platforms to manage, store, and transfer many types of data (e.g., experimental, operational, clinical) from many different disparate sources. The role of data engineering is critical in orchestrating,
configuring, managing, and scaling solutions to manage the data bloat problem. The Data Storage and Transport track presents in-depth case studies from leading life science organizations who are implementing solutions to address data
storage and transfer problems and challenges. These include where to store data (cloud, local, mixture), what is the optimal configuration regarding price vs. access, estimating data storage costs and making financial models, understanding and planning
for costs in the cloud, what to do with large third-party databases (inter-pharma collaborations, genomic/expression datasets), what to do with imaging collaboration that produces 100 TB, "rehydrating" a data archive (from tape) for re-analysis, determining
if you're storing the right stuff, figuring out the best way to deliver data products to customers/collaborators, and more. How are you developing technologies to deal with influx of digital data from digital health devices?
Building a Foundation for a Data Commons at NIEHS
Michael Conway, Data Systems Architect/Engineer, National Institute of Environmental Health Sciences (NIEHS)
Using iRODS as a Platform for Unifying and Managing Research Data
Carlos Rios, PhD, Senior Research Investigator, Computational Genomics - Translational Medicine, Bristol-Myers Squibb
Beyond Discoverability: Metadata to Drive Your Data Management
Terrell Russell, PhD, Chief Technologist, The iRODS Consortium at Renaissance Computing Institute (RENCI)
Boosting Research with Serverless Cloud Computing
Daniel Butnaru, PhD, Research Architect, Roche
Pathways in the Cloud: Facilitating Storage for Analysis Pipelines
Brigitte Raumann, Product Manager, Globus, University of Chicago
Can High-Performance File Systems Be Secure and Inexpensive?
Dirk Petersen, Scientific Computing Director, Fred Hutchinson Cancer Research Center
Aspects of Performance in Data Movement and Management
Vas Vasiliadis, Chief Customer Officer, Globus, University of Chicago
A Deeper Technical Dive to Enabling Advanced Analytics and Data Science across an Organization
Jason Tetrault, Global Head Data Engineering and Emerging Technologies, Takeda
Karl Gutwin, PhD, Senior Scientific Consultant, BioTeam
Phil Eschallier, CTO, RCH Solutions
KEYNOTE SESSION: Trends from the Trenches 2020
Chris Dagdigian, Co-Founder and Senior Director, Infrastructure, BioTeam, Inc.
Additional Speakers to be Announced