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Open Call for Science and Technology Created by Early-Stage (e.g. University) Teams

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-19-P-A218
Agency Tracking Number: F19B-001-0119
Amount: $24,933.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF19B-T001
Solicitation Number: 2019.2
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-08-02
Award End Date (Contract End Date): 2019-11-08
Small Business Information
1313 N Market St, Wilmington, DE, 19801
DUNS: 117116773
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Anthony Rossi
 (302) 722-6272
Business Contact
 Duanyi Wei
Phone: (302) 722-6272
Research Institution
 University of Delaware (Horn Entrepreneurship)
 Christina Pellicane
 132 E. Delaware Ave.
Suite 100
Newark, DE, 19711
 (302) 831-0961
 Nonprofit college or university
Managing multi-level risks ranging from the fiscal/personnel resources to fluctuating material/production costs have always been challenges due to the integrated nature of Air Force (AF) with governmental and industrial partners.Establishing a quantitative framework that provides basis for making informed decisions on these pressing issues can greatly impact how Air Force strategically allocate valuable resources and deploys operations. Our team has developed a consolidated system that address underlying risks in the asset management and quantitative trading space. Our proprietary technology aggregates AI-based predictive knowledge at higher level with low-level autonomous strategic decision-making process, providing real-time insights and dynamic strategy coordination. The teams believe that our risk management approach can address a variety of problem areas concerning AF including supply chain, cost modelling, and predictive maintenance, where the performance is largely affected by quantifiable systematic risks. The translation of the technology comes from the fact that the problems that underlie finance, operations, and resource allocation can be formulated and optimized in a similar cost-benefit analysis framework that is addressed in quantitative finance. By combining the predictive power from AI with our existing technical infrastructure, our robust and adaptive approach can greatly benefit AF by improving efficiency, productivity, and reducing cost.

* Information listed above is at the time of submission. *

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