Launched by MLCommons this year, the Machine Learning and Systems Rising Stars initiative was created to identify and support a cohort of current and recently graduated Ph.D. students specializing in machine learning and systems in order to develop community, foster research and career growth, and enable collaborations across academia and industry. This year 35 early-to-late-stage and recently graduated Ph.D. were selected as Rising Starts from over 100 applicants from dozens of global universities.
Wan is advised by ECE professors Arijit Raychowdhury and Tushar Krishna. His research interests center around computer architecture, VLSI, and embedded systems, with a focus on building hardware and systems for cognitive intelligence and autonomous machines (such as robots, drones, and unmanned vehicles), aiming to advance their performance, efficiency, resilience, and trustworthy.
A highlight of Wan’s work is his award-winning research, “Intelligence in Robotic Computing: Agile Design Flows for Efficient and Resilient Autonomous Machines.” Through synergistic advances in systems, hardware architectures, solid-state technologies, and collective intelligence, Wan led a team in developing an efficient and adaptive hardware accelerator, resilient and robust system optimization, and agile and scalable design methodology for autonomous machine computing.
His research has been recognized by the ECE Graduate Research Assistant (GRA) Excellence Award in 2023, IEEE Micro Top Picks Honorable Mention in 2023, first place in ACM Student Research Competition in 2022, the CRNCH Ph.D. Fellowship in 2022, and the Best Paper Award from the IEEE/ACM Design Automation Conference (DAC) in 2020.
As part of the Machine Learning and Systems Rising Star cohort, Wan presented at this year’s rising star program at Google, Mountain View, CA on Aug. 17-18, 2023.
In addition to Wan, three other Georgia Tech Ph.D. students were recognized as Machine Learning and Systems Rising Stars: Yonggan Fu, Haoran You, and Yang (Katie) Zhao, all in the College of Computing.
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