Hao (Mark) Chen

Hao (Mark) Chen

PhD candidate at Imperial College London.

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

portfolio

publications

Web-Based AI System for Medical Image Segmentation

Medical Image Understanding and Analysis: 27th Annual Conference, MIUA 2023, Aberdeen, UK, July 19–21, 2023, Proceedings

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

2023 60th ACM/IEEE Design Automation Conference (DAC)

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

Enhancing dropout-based Bayesian neural networks with multi-exit on FPGA

arXiv preprint arXiv:2406.14593

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

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

Proceedings of the 61st ACM/IEEE Design Automation Conference

Zehuan Zhang, Hongxiang Fan, Hao Chen, Lukasz Dudziak, Wayne Luk
[Paper]

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

2025 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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

Parallel Prompt Decoding: A Cost-Effective and Memory-Efficient Approach for Accelerating LLM Inference

Arxiv

Hao Mark Chen, Wayne Luk, Hongxiang Fan, Roberto Bondesan
[Paper]

Progressive Mixed-Precision Decoding for Efficient LLM Inference

2025 The Thirteenth International Conference on Learning Representations (ICLR)

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

AdaBlock-dLLM: Semantic-Aware Diffusion LLM Inference via Adaptive Block Size

arXiv preprint arXiv:2509.26432

Guanxi Lu, Hao Mark Chen, Yuto Karashima, Zhican Wang, Daichi Fujiki, Hongxiang Fan
[Paper]

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

arXiv preprint arXiv:2506.00002

Hao Mark Chen, Zehuan Zhang, Wanru Zhao, Nicholas Lane, Hongxiang Fan
[Paper]

Democratizing Agentic AI with Fast Test-Time Scaling on the Edge

arXiv preprint arXiv:2509.00195

Hao Mark Chen, Zhiwen Mo, Guanxi Lu, Shuang Liang, Lingxiao Ma, Wayne Luk, Hongxiang Fan
[Paper]

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

2025 62th ACM/IEEE Design Automation Conference (DAC)

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

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

Proceedings of the 30th Asia and South Pacific Design Automation Conference (ASP-DAC)

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

FW-Merging: Scaling model merging with frank-wolfe optimization

2025 International Conference on Computer Vision (ICCV)

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

Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling

2025 The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS)

Hao Mark Chen, Guanxi Lu, Yasuyuki Okoshi, Zhiwen Mo, Masato Motomura, Hongxiang Fan
[Paper]

talks

teaching

work