2018/08 Ph.D. Computer Science (William & Mary)
2014/12 M.S. Computer Science (William & Mary)
2012/12 B.S. Computer Science (Virginia Tech)
2012/12 B.S. Applied Discrete Mathematics (Virginia Tech)
Kyle has research interests in algorithm design, cryptography, security, mobile sensor data analysis, random number generation, crowdsourcing systems, deep learning, and Internet of Things (IoT). His Ph.D. Dissertation explored random number generation within the context of IoT devices, and adapting algorithms to utilize/assist these devices.
“The generation of random numbers has traditionally been a task confined to the bounds of a single piece of hardware. However, with the rapid growth and proliferation of resource-constrained devices in the Internet of Things (IoT), standard methods of generating randomness encounter barriers that can limit their effectiveness. In this work, we explore the design, implementation, and efficacy of a Collaborative and Distributed Entropy Transfer protocol (CADET), which aims to move random number generation from an individual task to a collaborative one…”
K. Wallace, G. Zhou and K. Sun, "CADET: Investigating a Collaborative and Distributed Entropy Transfer Protocol," 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, 2018, pp. 797-807.
“The importance of random number generators (RNGs) to various computing applications is well understood. To ensure a quality level of output, high-entropy sources should be utilized as input. However, the algorithms used have not yet fully evolved to utilize newer technology…“
K. Wallace, K. Moran, E. Novak, G. Zhou and K. Sun, "Toward Sensor-Based Random Number Generation for Mobile and IoT Devices," in IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1189-1201, Dec. 2016.