My first Ph.D. student Juncheng Gu graduated earlier this month after successfully defending his dissertation titled “Efficient Resource Management for Deep Learning Clusters.” This is a bittersweet moment. While I am extremely proud of everything he has done, I will miss having him around. I do know that a bigger stage awaits him; Juncheng is joining the ByteDance AI Lab to build practical systems for AI and machine learning!

Juncheng started his Ph.D. in the Fall of 2015 right before I started in Michigan. I joined his then advisor Kang Shin to co-advise him as he started working on a pre-cursor to Infiniswap as a term project for the EECS 582 course I was teaching. Since then, Juncheng worked on many projects that ranged from hardware, systems, and machine learning/computer vision with varying levels of luck and success, but they were all meaningful works. I consider him a generalist in his research taste. Infiniswap and Tiresias stand out the most among his projects. Infiniswap heralded the rise of many followups we see today on the topic of memory disaggregation. It was the first of its kind and introduced many around the world to this new area of research. Tiresias was one of the earliest works on GPU cluster management and certainly the first that did not require any prior knowledge about deep learning jobs’ characteristics to effectively allocate GPUs for them and to schedule them. To this day, it is the best of its kind for distributed deep learning training. I am honored to have had the opportunity to advise Juncheng.

Juncheng is a great researcher, but he is an even better person. He is very down-to-earth and tries his best to help others out whenever possible. He also understates and underestimates what he can do and has achieved, often to a fault.

I wish him a fruitful career and a prosperous life!