A Report on NIH/NCI Visit (12/12/13)

• Seminar on CCC report was well-attended by over 30 NCI researchers and program managers. Besides technical information, it had following asks:
  1. create a home for spatial computing in an NIH institute
  2. increased support for spatial computing research
  3. increased outreach to computing community, e.g., ACM SIG-Spatial
  4. secure enclave for sharing privacy-sensitive spatial data among researchers

• Individual meetings with Brad Hesse (Chief, Health Communication and Information Branch), Zaria Tatalovich (Acting Chief, Surveillance Systems Branch), Rick Moser (Program Director, Science of Research & Technology), Patricia Weber (Program Director, SBIR) Li Zhu (Surveillance), David Berrigan (Cancer Conrol & Population Sc.), etc.

  Hosts (Tatalovich, Zhu, Berrigan) felt that the talk was well-received and the visit was very successful. It bolstered the case of strengthening support for spatial computing research initiatives at NCI.

  There are mechanisms to increase support for spatial computing research. Program announcements may specify special review considerations using PAR or set-aside funds using PAS. NIH institutes may also leverage relevant review panels at other agencies. For example, NCI Health Communication and Information branch will participate in NSF review panel (smart and connected health) and may use its recommendations to fund a few projects.

  NCI Surveillance systems invited a followup visit, e.g., a seminar from Prof. Goodchild to articulate shared and enhanced spatial computing infra-structure at NIH. NCI Science of Research program will like to include spatial computing researchers in their workshops and panels. For example, they are looking for a panelist on information integration in context of a set of surveys of population.

  There is also strong interest in specific collaborations to explore emerging spatial computing ideas to explore active and persistent surveillance (to enhance NCI/SEER program), to investigate hypothesis generation from spatial big data using pattern families (e.g., colocation, hot-geographic-features) via basic/applied research as well as SBIR, and STTR.