News and Events

Congrats to the Class of 2018!

Jan 3, 2019

The Ted and Karyn Hume Center for National Security and Technology is proud to have graduated 23 students in the Fall 2018 semester, including 13 graduate and 10 undergraduate students. The Hume Center would like to congratulate these exceptional students on their graduation, including:  

Kiran Karra, PhD student, Electrical Engineering.  Dissertation Title: Modeling and Analysis of Non-Linear Dependencies using Copulas, with Applications to Machine Learning

Abstract: Many machine learning (ML) techniques rely on probability, random variables, and stochastic modeling.  Although statistics pervades this field, there is a large disconnect between the copula modeling and the machine learning communities.  Copulas are stochastic models that capture the full dependence structure between random variables and allow flexible modeling of multivariate joint distributions.  Elidan was the first to recognize this disconnect, and introduced copula based models to the ML community that demonstrated magnitudes of order better performance than the non copula-based.  However, the limitation of these is that they are only applicable for continuous random variables and real world data is often naturally modeled jointly as continuous and discrete.  Our work involves bridging this gap of modeling and analyzing data that is jointly continuous and discrete using copulas, and applying this to data analysis and machine learning.

Kevin Nastasi, Graduate student, Graduate Recruitment Fellowship student. Thesis Title: Autonomous and Responsive Surveillance Network Management for Adaptive Space Situational Awareness

Abstract: As resident space object populations grow, and satellite propulsion capabilities improve, it will become increasingly challenging for space-reliant nations to maintain space situational awareness using current human-in-the-loop methods. This dissertation develops several real-time adaptive approaches to autonomous sensor network management for tracking multiple maneuvering and non-maneuvering satellites with a diversely populated Space Object Surveillance and Identification network. The proposed methods integrate suboptimal Partially Observed Markov Decision Processes (POMDPs) with covariance inflation or multiple model adaptive estimation techniques to task sensors and maintain viable orbit estimates for all targets. The strategies developed in this dissertation successfully track 207 non-maneuvering and maneuvering spacecraft using only 24 ground and space-based sensors. The results show that multiple model adaptive estimation coupled with a multi-metric, suboptimal POMDP can effectively and efficiently task a diverse network of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for autonomous and adaptable sensor network command and control for real-world space situational awareness.

David Flores, Undergraduate student, Winner of the Association of Old Crows Capitol Club Chapter Spring 2018 Electronic Warfare Scholarship; Major: Electrical Engineering

Click to learn more about the academic and professional benefits of the National Security Education Program, the Hume Scholars Program, and Undergraduate and Graduate Research opportunities with the Hume Center.

Congratulations to the Class of 2018!