About me

I am a first-year PhD student at Imperial College London, advised by Prof. Wayne Luk and Dr. Hongxiang Fan. I am a recipient of the President’s PhD Scholarship at Imperial. Previously, I completed both my bachelor’s and master’s degrees at Imperial College London.

My research focuses on bridging the gap between advanced AI algorithms and hardware platforms through efficient hardware-aware algorithms, cost-effective model merging, ML-assisted hardware design, and algorithm-system co-design. My current research directions include:

  • Multi-token generation for LLMs
  • LLM quantization for edge deployment
  • Model merging
  • Code agents for hardware design


Education

  • Ph.D in Computing Research, Imperial College London, 2024 - 2028 (expected)
  • MEng in Computing, Imperial College London, 2020 - 2024


Selected Publications


FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization

Manuscript.

Hao (Mark) Chen, Shell Xu Hu, Wayne Luk, Timothy Hospedales, Hongxiang Fan
[Paper]



Advancing AI-assisted Hardware Design with Hierarchical Decentralized Training and Personalized Inference-Time Optimization

Manuscript.

Hao (Mark) Chen, Zehuan Zhang, Wanru Zhao, Nicholas Lane, Wayne Luk, Hongxiang Fan
[Paper]



Progressive Mixed-Precision Decoding for Efficient LLM Inference

International Conference on Learning Representations (ICLR), 2025.

Hao (Mark) Chen, Fuwen Tan, Alexandros Kouris, Royson Lee, Hongxiang Fan, Stylianos I. Venieris
[Paper] [Code]



Enhancing LLM-based Quantum Code Generation with Multi-Agent Optimization and Quantum Error Correction

Design Automation Conference (DAC), 2025.

Charlie Campbell, Hao (Mark) Chen, Wayne Luk, Hongxiang Fan
[Paper]



Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysis

Asia and South Pacific Design Automation Conference (ASP-DAC), 2025.

Jiahao Gai, Hao (Mark) Chen, Zhican Wang, Hongyu Zhou, Wanru Zhao, Nicholas Lane, Hongxiang Fan
[Paper]



Algorithm and Hardware Co-Design for Multi-Exit Dropout-based Bayesian Neural Networks

Manuscript.

Hao (Mark) Chen, Liam Castelli, Martin Ferianc, Shuanglong Liu, Wayne Luk, Hongxiang Fan
[Paper]



Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference

Manuscript.

Hao (Mark) Chen, Wayne Luk, Ka Fai Cedric Yiu, Rui Li, Konstantin Mishchenko, Stylianos I. Venieris, Hongxiang Fan
[Paper] [Code]



Hardware-Aware Neural Dropout Search for Reliable Uncertainty Prediction on FPGA

Design Automation Conference (DAC), 2024.

Zehuan Zhang, Hongxiang Fan, Hao (Mark) Chen, Lukasz Dudziak and Wayne Luk
[Paper]



Web-based AI System for Medical Image Segmentation

Conference on Medical Image Understanding and Analysis (MIUA), 2023.

Hao (Mark) Chen, Taowen Liu, Songyun Hu, Leyang Yu, Yiqi Li, Sihan Tao, Jacqueline Lee, Ahmed E. Fetit
[Paper]



When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA

Design Automation Conference (DAC), 2023.

Hongxiang Fan, Hao (Mark) Chen, Liam Castelli, Zhiqiang Que, He Li, Kenneth Long, Wayne Luk
[Paper] [Code]