Translational data science is an emerging field that applies data science principles, techniques and technologies to scientific challenges that hold the promise of having an important societal impact. In order to understand the important shifts in how data is used and applied to many aspects of daily life, it is important to effectively apply these principles and tools from data science within our diverse research environments for the benefit of human and societal welfare.
Building upon the discussions started at the NSF Workshop on Translational Data Science 2017 in Chicago, which focused on developing a community around translational data science, this workshop will focus on expanding academic research and industry expertise, and how these mostly-divergent activities can cooperate and partner toward better outcomes.
The workshop will be by invitation only, and we expect approximately 40-60 participants from academia, industry, government and foundations to attend. Participants will be asked to address four main themes:
- Defining translational data science
- Identifying the most challenging problems currently being faced in TDS
- Identifying the differences between the strategic goals and outcomes of enterprises (both social/civic/governmental organizations and for-profit industry) and those of academic research and other training environments
- Addressing specific challenges and opportunities where academic institutions and industry/governmental partners can work together
Location and Dates
Berkeley Institute for Data Science (BIDS)
University of California, Berkeley
190 Doe Library
Monday, November 13, 2017 (8:30AM - 5PM) and Tuesday, November 14, 2017 (8:30AM - 3PM)
- David Culler, University of California, Berkeley (Chair)
- Robert L. Grossman, University of Chicago
- Raghu Machiraju, The Ohio State University
- Chaitan Baru, National Science Foundation and University of California, San Diego
- Alicia Johnson, City of San Francisco
- Meredith Marie Lee, West Big Data Innovation Hub
- Jonathan Dugan, Berkeley Institute for Data Science
Attendees may refer this list of local travel resources when planning their travel and accommodations arrangements.
If you have any additional questions, or would like to attend, please contact us at TDS_Workshop@berkeley.edu.
He received his BA from UC Berkeley in 1980 and an MS and PhD from MIT in 1985 and 1989, respectively. He joined the EECS faculty in 1989; is the founding director of Intel Research, UC Berkeley; and was associate chair of the EECS Department, 2010-2012 and Chair from 2012 through June 30, 2014. He won the Okawa Prize in 2013. He is a member of the National Academy of Engineering, an ACM fellow, and an IEEE fellow. He has been named one of Scientific American's Top 50 Researchers and is the creator of one of MIT's Technology Review's 10 Technologies that Will Change the World. He was awarded the NSF Presidential Young Investigator and the Presidential Faculty Fellowship. His research addresses networks of small embedded wireless devices, planetary-scale internet services, parallel computer architecture, parallel programming languages, and high-performance communication. It includes TinyOS, Berkeley Motes, PlanetLab, Networks of Workstations (NOW), Internet services, Active Messages, Split-C, and the Threaded Abstract Machine (TAM).
Jonathan Dugan, Chief Research Officer, has focused his career on the promotion of science, education and open culture. His work in both nonprofit and for-profit businesses includes consulting, business management, entrepreneurship, web services and software development, community engagement, and biomedical informatics systems.
Jonathan works with the Berkeley research community to empower faculty and researchers to develop missing areas of the data science environment (tools, methods, systems, workflows, etc.), and secure the resources to fund them. To accomplish this, he directs our efforts to solve research issues facing the emerging field of data science. He helps us promote our successes, fund our work, and find practical solutions that bring together the best faculty, postdocs, students, and staff to solve immediate challenges for our research and education efforts.
Jonathan completed his PhD from Stanford in 2002 in biomedical informatics, where he developed nonlinear mathematical simulations for protein structure modeling. His current research interests include citations, data sharing, software development, community engagement, identity and reputation systems, and applying machine learning techniques to solve research questions.