Rapid Response Data Science
The COVID-19 pandemic has surfaced a spectrum of data science challenges and opportunities. Despite demonstrating strengths in generating new data, models, and outputs, the data science community has found itself insufficiently prepared to contribute in a coordinated fashion to the pandemic effort, across regional, national, and global scales. There is a pressing need for a convergence of agility and coordinated capacity, with academia, government, industry, and community organizations, each bringing complementary resources to bear.
A networked approach means standing up:
shared infrastructure, preparedness training, teams that act interoperably with swift coordination
A Rapid Response Network pitch
Check out the recent article by Eric Kokaczyk, Meredith Lee, Jing Liu, and Micaela Parker in the Harvard Data Science Review
In October 2020, we held a joint session between the ADSA Annual Meeting and the Data Science Leadership Summit on Rapid Response Data Science. Spurred by our collective experiences with COVID-19, and a desire to see more coordinated data science responses to future crises, this session served as a launching point for partnership commitments and future coordination to establish the foundation and strategic path toward a data science rapid response network.
Key outcomes from the session, which will drive our discussions over the coming year include:
- Build a trusted network of data scientists - Leveraging existing networks and relationships, data scientists should define and create a formal data science rapid response network.
- Engage with existing disaster response networks - There are already many organizations and networks in place for addressing large scale disasters, all of whom our network can engage with and learn from. The data science rapid response network should engage with existing networks in the disaster response space, and identify modes of connection and communication across networks.
- Identify and resolve barriers to participation - Rapid response to disaster requires rapid identification of personnel and other resources, but this type of rapid deployment is often challenging in an academic environment. The data science rapid response network should identify barriers to academic participation and promote models for clearing those barriers.
For a more detailed break down of the information shared at the Joint Session, use the links below.
Keep an eye on this space, and on ADSA mailings, for updates on the Rapid Response Data Science Network.
2020 JOINT SESSION
- Notes from the 2020 ADSA Annual Meeting and Data Science Leadership Summit Joint Session (Wednesday October 14, 2020)
- Video of the Joint Session panel discussion - Panelists: Lara Campbell - NSF Convergence Accelerator; Ran Canetti - Boston University; Nicollette Louissaint - Healthcare Ready; Emma Spiro - University of Washington
- Kolaczyk, E. (2020). POV: COVID-19 Shows Us We Need Rapid Response Data Science Teams. BU Today. June 10, 2020. [link]
- Lazer, D. M., Pentland, A., Watts, D. J., Aral, S., Athey, S., Contractor, N., ... & Nelson, A. (2020). Computational social science: Obstacles and opportunities. Science, 369(6507), 1060-1062. [link]
- Lee, M. M., Johnson, A. D., Yelick, K. A., Chayes, J. T. (2020). The Road for Recovery: Aligning COVID-19 efforts and building a more resilient future. IEEE Data Eng. Bull. 43(2): 133-140. [pdf]
- Loeb, A., and Gil, D. (2020). Let’s Create an Elite Scientific Body to Advise on Global Catastrophes. Scientific American, Op Ed. April 30, 2020. [link]
- McGuire, A. L., Aulisio, M. P., Davis, F. D., Erwin, C., Harter, T. D., Jagsi, R., ... & Wynia, M. (2020). Ethical challenges arising in the COVID-19 pandemic: An overview from the Association of Bioethics Program Directors (ABPD) task force. The American Journal of Bioethics, 1-13. [link]
- Peek, L., Tobin, J., Adams, R., Wu, H., & Mathews, M. (2020). A framework for convergence research in the hazards and disaster field: The natural hazards engineering research infrastructure CONVERGE facility. Frontiers in Built Environment, 6, 110. [link]
Eric Kolaczyk is a Professor of Statistics, in the Department of Mathematics and Statistics, a founding member of the Faculty of Computing and Data Sciences, and Director of the Hariri Institute for Computing at Boston University. He is also affiliated with the Division of Systems Engineering, the Programs in Bioinformatics and in Computational Neuroscience, and the BU URBAN program. His research is focused at the point where statistical theory and methods support human endeavors enabled by computing and engineered systems, frequently from a network-based perspective of systems science. He develops novel methodologies for design, representation, modeling, inference, prediction, and uncertainty quantification foundational to new paradigms for data measurement and analysis. He has published nearly 100 articles, including several books on the topic of network analysis. As an associate editor, he has served on the boards of JASA and JRSS-B in statistics, IEEE IP and TNSE in engineering, and SIMODS in mathematics. He formerly served as co-chair of the NAS Roundtable on Data Science Education. He is an elected fellow of the AAAS, ASA, and IMS, an elected senior member of the IEEE, and an elected member of the ISI.
Meredith Lee is the Executive Director and Co-PI of the West Big Data Innovation Hub, one of four regional hubs launched by the National Science Foundation to build and strengthen data science partnerships across academia, industry, nonprofits, and government. Before joining UC Berkeley’s Division of Computing, Data Science, and Society, Dr. Lee led the White House Innovation for Disaster Response & Recovery Initiative under the Obama Administration and focused on multi-agency collaborations with data.gov, challenge.gov, and the National Science & Technology Council. Meredith completed her Ph.D. in Electrical Engineering at Stanford University, a postdoc at the Canary Center at Stanford for Cancer Early Detection, and an AAAS Science & Technology Policy Fellowship at the Homeland Security Advanced Research Projects Agency.
Jing Liu is the Managing Director of Michigan Institute for Data Science at the University of Michigan. Her focus areas include enabling transformative and reproducible data science in a wide range of research domains, and building academia-industry-government-community collaboration. She received her PhD in Biology from the California Institute of Technology, and postdoctoral training in Visual Neuroscience at Stanford University.
Micaela Parker is Executive Director of the Academic Data Science Alliance (ADSA). Before launching ADSA, Micaela served as a Program Coordinator for the Moore-Sloan Data Science Environments and an Executive Director for the University of Washington’s eScience Institute. At eScience, she handled operations, developed research and training programs, and participated in strategic planning and fiscal oversight working directly with university partners and funders. She continues to hold the title of eScience Data Science Fellow and she is a Research Scholar with the Ronin Institute for Independent Scholarship. Prior to 2014, Micaela was a research scientist in UW’s School of Oceanography, where she also earned her PhD. She has been involved in many large, interdisciplinary projects bridging oceanography and genomics. Micaela is an avid skier and enjoys mountain biking and snorkeling/SCUBA diving. She is terrible at baking and dancing, but continues to do both when no one is watching.