Data Management for Meeting Global Health Challenges
Moderator: Tapan S. Parikh (UC Berkeley)
Kuang Chen (UC Berkeley), Lucky Gunasekara (Global Viral Forecasting Initiative), Alon Halevy (Google), Andy Kanter (Columbia University), Rowena Luk (Dimagi Inc.), Peter Speyer (University of Washington

Abstract. Organizations and health agencies working on meeting global health challenges are becoming increasingly data driven. Governments and donors are demanding rigorous and up-todate assessment of patient needs and health service delivery. These organizations work in some of the most difficult data environments - dealing with limited infrastructure and human resources, inconsistent data quality and availability, not to mention significant data backlogs.

This panel combines perspectives from a range of active researchers and practitioners managing large-scale deployments, including: evaluating large-scale health policies and interventions, managing day-to-day clinical activities, and responding to remote outbreaks of disease.

Data challenges in global health intersect with many current topics in database research. Potential topics to be discussed include: trade-offs between data quality, latency and cost; dealing with heterogeneous data sources, privacy challenges, managing provenance and mobile data collection.

Panel Discussion: Maximizing Impact
Moderator: Ed Lazowska (University of Washington)
David DeWitt (Microsoft), Juliana Freire (NYU Polytechnic), Ed Lazowska (University of Washington), Sam Madden (MIT), Jennifer Widom (Stanford)

Abstract. This session is intended as an open discussion between a small group of panelists and the conference attendees. What can we do as a community to increase the impact (whatever that means) of our research, especially with the rise in paper counts, write only conferences, over-specialization of ourselves and our students, migration of distinguished researchers to more development-oriented positions, etc.?
More specifically:

  • What should we look for in graduate applicants?
  • What should be the balance between breadth and depth in graduate programs?
  • Are there emerging high-impact areas on which there should be greater focus (e.g., vi-
    sualization, scienti c data management)?
  • How do we make data management techniques (databases, data mining, machine learning) usable by the masses?
  • How can we contribute to a data-centric view of everything science, engineering, the social sciences, commerce?