Kun Woo Cho
Princeton University
kwcho@princeton.edu
Bio
Kun Woo Cho is a Ph.D. candidate in the Computer Science Department at Princeton University. She received her master’s in computer science from Princeton in 2020 and her bachelor’s in computer engineering from the University at Buffalo in 2018, graduating Summa cum laude. She was also a research intern at the University of Cambridge in 2017 and Meta in 2021. Cho’s research focuses on machine learning and high frequency reconfigurable intelligent surfaces for wireless networks. Notable achievements include the publication of her smart surface research in USENIX NSDI 2023 with coverage in Princeton News and TechXplore and a Best Paper Award at ACM MobiHoc 2023 for her work on machine learning for wireless networks. She is also a recipient of the 2025 Siebel Scholars Award, the Princeton SEAS Excellence Award in 2023 and the Dean’s Undergraduate Achievement Award in 2018.
Areas of Research
- Communications and Networking
Programmable Smart Radio Environments: From Theory to Hardware Implementation
Today’s wireless networks are undergoing a rapid transformation, scaling in traffic volume, spectral efficiency, and radio count as never seen before. We address the critical challenges emerging from this evolution in next-generation (NextG) wireless networks, focusing on realizing three key services: enhanced mobile broadband (Gbps or above), ultra-reliable low latency communication (order of milliseconds), and massive machine type communication (up to one million per squared kilometer). To meet these diverse and demanding requirements, we pose a central question: Can we build a smarter radio environment controlled and learned by software, configuring itself in real-time to meet different application needs? Current approaches to handle uncontrolled wireless signals are end-to-end. Unfortunately, sending and receiving endpoints are limited in their ability to shape this inherent propagation behavior. For instance, although multiple antennas can shape the beam pattern departing the sender, it cannot control how the resulting signals arrive at the receiver after traversing environmental obstacles. By focusing on changing the environment itself rather than the communication endpoints, we offer a significant shift in design paradigms for modern wireless networks. We introduce the concept of a programmable smart radio environment using two key technologies: programmable smart surface and artificial intelligence. At the physical layer, we have designed programmable smart surface systems and deployed them on the buildings and vehicles to manipulate the wireless radio in real-time. At the link/MAC layer, we have introduced a machine learning-assisted massive IoT system that predicts the wireless radio and accordingly allocates networking resources. Real world systems are often highly complex and cannot always be explained with conceptual models alone. My solutions are implemented into physical hardware prototypes, integrated with existing network protocols, and rigorously evaluated through real-world experiments, providing an answer to the central question posed.