Tong Chen

I am a Ph.D. student in Electrical and Computer Engineering at Purdue University, advised by Dr. Younghyun Kim, and a member of the Networked and Embedded Intelligent Systems (NEIS) Lab. My research focuses on domain-specific hardware accelerators for secure and efficient computing, with particular emphasis on fully homomorphic encryption (FHE) and machine learning workloads.

My recent work investigates performance and cost bottlenecks in CKKS-based systems through profiling and optimization of key operations such as automorphism and number-theoretic transforms (NTT). I am also interested in hardware–software co-design methodologies for scalable accelerator architectures, including stochastic computing techniques for energy-efficient AI inference.

I received my B.Eng. in Computer Engineering from the National University of Singapore. I was honored with the 2025 DAC Young Fellowship and the Emerging Contributor Award.