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Program Manager
Dr. Randy Garrett joined DARPA in February 2019 as a program manager in the Strategic Technology Office. Prior to arriving at DARPA, he worked for commercial cybersecurity companies.
Complex physical systems, devices and processes important to the Department of Defense (DoD) are often poorly understood due to uncertainty in models, parameters, operating environments and measurements. The goal of DARPA’s Enabling Quantification of Uncertainty in Physical Systems (EQUiPS) program is to provide a rigorous mathematical framework and advanced tools for propagating and managing uncertainty in the modeling and design of complex physical and engineering systems. Of particular interest to the program are systems with multi-scale coupled physics and uncertain parameters in extremely high-dimensional spaces, such as new aerospace vehicles and engines.
The goal of the Fundamental Design (FUN Design) program is to determine whether we can develop or discover a new set of building blocks to describe conceptual designs. The design building blocks will capture the components’ underlying physics allowing a family of nonintuitive solutions to be generated.
Machine learning has shown remarkable success across many application areas in recent years, leveraging advances in computing power and the availability of large sets of training data. It provides a tremendous opportunity to deploy data-driven systems in more complex and interactive tasks including personalized autonomy, agile robotics, self-driving vehicles, and smart cities. Despite dramatic progress, the machine learning community still lacks an understanding of the trade-offs and mathematical limitations of related technologies for a given domain, problem, or dataset.
The science of network analysis is in its infancy. Currently, the structure of real-world networks is described only in terms of coarse and basic details such as diameter, degree distribution, etc. In addition, as networks become large, many problems are intractable as the classical algorithms for these problems run in exponential time with respect to the size of the graph. A large number of important problems (e.g., structural and functional brain dynamics or gene-protein and disease networks) can be formulated as graph problems. A comprehensive mathematical understanding of large networks is needed in order to effectively apply and scale graph-based network analysis techniques for use in DoD-relevant scenarios.