LearningKnowledge Distillation
Design supervision that transfers useful knowledge to compact models, improving capability under deployment constraints.
Email: phyang [dot] cs [at] gmail [dot] com
Penghui Yang is currently a PhD candidate at the College of Computing and Data Science, Nanyang Technological University, supervised by Prof. Bo An. He received his B.Sc. degree in Computer Science from Nanjing University of Aeronautics and Astronautics in 2023, advised by Prof. Sheng-Jun Huang. Previously, he collaborated closely with Dr. Ming-Kun Xie and Prof. Lei Feng. He also spent a cherished year and a half at Sea AI Lab, working under the mentorship of Dr. Cunxiao Du.
Google Scholar | DBLP | Semantic Scholar | CV
[Apr 2026] One paper was accepted by ACL'26 Oral.
[May 2025] One paper was accepted by KDD'25 Oral.
[Jul 2023] One paper was accepted by ICCV'23.
My research focuses on knowledge distillation, speculative decoding, and AI for Science through software-in-the-loop systems. Across these areas, I study how paired interactions between complementary models and computational tools make learning, inference, and scientific discovery more efficient.
Design supervision that transfers useful knowledge to compact models, improving capability under deployment constraints.
Combine inexpensive proposals with target verification to accelerate generation without changing the target distribution.
Close the loop between neural reasoning and physics-based computation to refine designs, recover calculations, and investigate mechanisms.
If you are interested in collaboration, feel free to get in touch with me.
Topics:
Efficient ML /
AI for Science /
Others
*: co-first author, †: corresponding author