My research is primarily in the area of systems and networking, with an emphasis on enhancing networked systems through practical machine learning (ML) techniques. To achieve this, I work with students to build real-world systems, design ML-based policies to optimize system performance, and develop mechanisms to make ML more deployable in practice (e.g., safe, robust, generalizable, and efficient).

Given the interdisciplinary nature of my research, students with expertise in either systems and networking or ML are strongly encouraged to apply to my group. I mostly publish in top-tier systems and networking conferences such as USENIX NSDI and ACM SIGCOMM, but I intend to publish in ML venues whenever applicable.

In my group, you will receive guidance to hone your research and communication skills, along with my full support for your career development. For instance, when there is a good fit, I will be happy to recommend you for summer internships at Microsoft Research, SystemsResearch@Google, or other research labs. It is my goal to help you succeed in your academic and professional pursuits. Additionally, I am committed to fostering a diverse, inclusive, and respectful research environment where everyone feels welcome and valued.