I am a PhD student co-advised by Anthony Rowe and Carlee Joe-Wong at the Department of Electrical and Computer Engineering at Carnegie Mellon University. I'm interested in Learning for Systems: applying machine learning to (cyber-physical) systems in ways that leverage both data-driven learning techniques and a theoretical and intuitive understanding of system properties.
Previously, I obtained my B.S. in Electrical Engineering at the University of Texas at Austin in May 2021. While at UT Austin, I worked on Learning to Optimize research with Atlas Wang.
Machine Learning for Radar
Radars are an ideal complement to cameras for applications such as autonomous driving: both are inexpensive, solid-state sensors, with cameras boasting fine angular resolution and radars providing depth resolution and robustness to adverse conditions. Unfortunately, unlike visual images or Lidar points, radar data are harder to interpret, and lack a large body of existing research. In this project, my goal is to develop machine learning-based methods to interpret radar data both spatially and semantically, potentially replacing Lidar as the primary means of 3D perception in robotics and beyond.
Data-Driven Edge Orchestration for Distributed Systems
The ability to execute programs in heterogeneous distributed environments while maintaining security and performance isolation is of critical importance as edge and cloud applications become increasingly entangled. The SilverLine framework is our solution, where we propose using WebAssembly modules as an edge-friendly lighter-weight alternative to virtual machines or containers for maintaining isolation while providing portability.
In this research project, my goal is to explore learning-enabled building blocks for managing distributed systems. Key concerns include performance analysis and runtime prediction, as well as debugging and anomaly detection. My approach emphasizes going beyond black-box approaches (in both a statistical and a systems sense) using techniques like instrumentation injection and statistical machine learning approaches.