The purpose of the UMD-Leidos Seed Grant Program is to facilitate and incentivize research interaction and collaboration between UMD researchers and subject matter experts at Leidos. It is the hope and expectation that these seed grants will serve as a precursor to joint pursuit of third party funding within one year of the completion of the award. Each of the below areas contains a Leidos point of contact. To submit for a seed grant award, it is expected that the UMD researcher will initiate contact with the Leidos POC, to discuss the parameters of their idea or potential “seed.”
A proposal of no more than three pages in length shall be submitted for review and consideration. Please include “Leidos-UMD Seed Grant Program” in the subject line of your submission. Seed grant proposals must be submitted by 5:00 p.m. on October 1. Late submissions will not be considered. Elements to be addressed in the white paper include project scope, impact, timeline of work to be completed, future potential funding sources of the research, and a rough budget estimate for how the funds will be used. Two awards of not more than $50,000 will be made.
The topics of the seed grants to which UMD researchers can apply include the following broad categories:
National Security: topics to consider include: cybersecurity; complex visualization of large data or behavior sets; insider threat detection and monitoring; analysis of Android applications and supporting programming languages; automation of information security analysis; next generation operating systems with security enforcement; underwater
Health: topics to consider include: health IT and analytics, machine learning for health; complex visualization of large data or behavior events; open-source semantic web applications related to biomedical information; application design or prototyping for patient out-care population.
For more information, contact Hana Kabashi at firstname.lastname@example.org.
UMD & Leidos Seed Grant Winners Announced
Two UMD & Leidos Seed Grant Winners were announced in December 2014.
The first project seeks to improve the health of individuals and populations. Ritu Agarwal, professor of information systems, seeks to improve the understanding of clinical and administrative outcomes in health care. Agarwal’s research will yield predictive models that identify appropriate people for intervention and prevention programs.
Ben Shneiderman, professor of computer science, is leading the second seed project along with Catherine Plaisant, senior research scientist. Both are members of the Human- Computer Interaction Lab and are focused on insider threat detection through exploring temporal patterns in large collections of event sequences.