Ph.D. Students Named Rising Stars in Machine Learning

Three Georgia Tech Ph.D. students have been named MLCommons Rising Stars for their excellence in research in machine learning (ML) and computer systems. 

Chaojian Li, Jianming Tong, and Biswadeep Chakraborty were recognized for their contributions and future potential in their fields. The MLCommons Rising Stars program provides a platform for young researchers working at the intersection of ML and systems to build connections, engage with experts, and develop their skills.

In July, the Rising Stars took part in a workshop at NVIDIA headquarters in Santa Clara, California. There, they had the opportunity to showcase their work and learn from others in their field. 

"As a computer architect with expertise in systems, this workshop broadened my perspective on full-stack ML systems, offering deeper insights into the synergy among models, systems, compilation, and hardware design to achieve optimal outcomes,” said Tong. “This experience will be invaluable for my ongoing Ph.D. research and will significantly contribute to my academic career aspirations post-graduation."

Li is a Ph.D. student in the School of Computer Science (SCS) studying under the guidance of Associate Professor Yingyan (Celine) Lin. His research interests are dedicated to democratizing advanced deep learning solutions by making them more accessible, eliminating the reliance on costly and dedicated computing resources. 

The approach centers on the co-design of efficient deep learning algorithms and hardware, treating the development as an integrated system. Along with several awards, the practical application of these technologies led to the development of a cost-effective heart health monitoring system on a $10 edge device, which won first place in the ACM/IEEE TinyML Design Contest at ICCAD 2022.

“At the workshop, I had the privilege of presenting our work on on-device, real-time 3D reconstruction and rendering,” Li said. “Being selected as an ML and Systems Rising Star marks a significant milestone in my academic journey and inspires me to continue pushing the boundaries in developing more efficient and effective ML systems in this field.”

Chakraborty is a Ph.D. student in the School of Electrical and Computer Engineering (ECE). His research focuses on advancing AI through spiking neural networks (SNNs), leveraging their event-driven temporal processing capabilities to develop next-generation AI technologies that are both robust and energy-efficient. 

His research contributions have been recognized with several awards, including ECE’s Colonel Oscar P. Cleaver Award for the most outstanding Ph.D. dissertation proposal in 2023-24 and the ECE STEER Fellowship in 2023. Chakraborty is advised by Professor Saibal Mukhopadhyay. 

Tong is a Ph.D. student in SCS researching computer architecture with a goal of full-stack optimizations for privacy-preserving and performance-oriented AI workloads. He has extensive prototype experience and internships at Alibaba DAMO Academy, Pacific Northwest National Lab, and Rivos. His research has previously earned him the Qualcomm Innovation Fellowship in 2023. Tong is advised by ECE Professor Tushar Krishna.