World-class researchers pushing the boundaries of science and engineering
Research Assistant Professor
Entropy Xu (徐策羽) obtained his Ph.D. in the Computer Science Department at Duke University. His research focuses on the intersection of computer architecture and machine learning, with two main directions: designing efficient computer architectures for machine learning algorithms and developing machine learning algorithms for efficient computer architectures. His interests include Machine Learning Hardware Accelerator, Large Language Models, Compression and Quantization, and AI for EDA. HKUST Official Profile Page
Postdoctoral Researcher
My research focuses on compute-in-memory architectures and chips, domain-specific architectures, and hardware-software co-design.
Postdoctoral Researcher
Jun Xia's research interests focus on edge-cloud intelligence systems and trustworthy AI.
Postdoctoral Researchers
Weikai Xu (徐伟凯) is a visiting student from School of Integrated Circuits, Peking University. His doctoral research has primarily focused on emerging device-based in-memory computing and searching, as well as hardware-algorithm co-design for AI acceleration. To date, he has published 15 first-author papers in top-tier conferences and journals, including IEDM, VLSI, DAC, ESSERC, DATE, and EDL, and holds 22 granted or pending patents related to electron devices and their applications in AI. He has received IEEE EDS PhD Student Fellowship (3 awardees worldwide), Best Paper Award at EDTM 2025, Best Paper Award 1st Prize at CSTIC 2022, CAST Young Talent Support Program for Doctoral Students, and his recently accepted work has been nominated for Best Student Paper in Neuromorphic and Novel Computing at IEDM 2025.
Postdoctoral Researcher
Yiyang Yuan (袁易扬) is a Postdoctoral Researcher at FACT Lab, HKUST. His research interests include Computing-in-Memory, RISC-V, AI Accelerator, and Embodied Intelligence.
Postdoc Researcher
My research sits at the intersection of AI, computer architecture, and silicon implementation. I design, tape out, and evaluate energy-efficient chips for edge AI that address real system requirements beyond raw neural-network throughput.
Postdoctoral Researcher
His research interests are efficient AI computing, with a focus on large language models, graph neural networks, and hardware/software co-design.
Ph.D. Student
Ningzhi Ai (艾宁智) is a Ph.D. student. He is dedicated to researching more powerful prefetchers and branch predictors to enhance general-purpose computing performance. He also focus on developing more efficient hardware architectures for AI-related algorithms, including LLMs and embodied AI.
Ph.D. Student
Xin Fan (范心) is currently a second-year Ph.D. student. Her research focuses on interconnects and communication, with a particular interest in data centers and scale-up systems.
Ph.D. Student
Yanqin LI (李燕琴) is a Ph.D. student in the Department of ECE at HKUST. She received her M.S. degree from the Institute of Computing Technology, Chinese Academy of Sciences.
Ph.D. Student
I am interested in 3D vision and embodied AI. I hope my research could be applied in real-world robots and make them smarter.
Ph.D. Student
Qi Liu (刘奇) is a Ph.D. Student at HKUST. He has rich experiences in crypto-accelerator/inference model accelerator. He is also proficient in FPGA backend optimization. His research currently focus on formal validation on hardware.
Ph.D. Student
Ruyi Song (宋如意) is a first-year PhD student, having completed both her Bachelor's and Master's degrees at Zhejiang University. Her current research focuses on 3D integrated circuits.
Ph.D. Student
Wenhao Sun （孙文昊） is a Ph.D. student in the Department of ECE at HKUST. He received his M.S. degree from ICT, CAS. His primary research interests lie in architectural support for embodied AI and robotic systems.
Ph.D. Student
Xiangfeng Sun is an Ph.D. student at the Hong Kong University of Science and Technology specializing in computer architecture and VLSI. He aims for his research to be both meaningful and interesting.
Ph.D. Student
Renati Tuerhong (热纳提·吐尔洪) is a Ph.D. student at Peking University, where he is currently pursuing his Ph.D. degree. His research interests include VLSI design and Computer Architecture, with a focus on emerging applications such as 3D Gaussian Splatting (3DGS) and Embodied AI.
Ph.D. Student
Hongyi Wang is currently a master student at Tsinghua University. His research interests include hardware-software co-design and FPGA-based accelerators. Hongyi Wang is set to join FACT Lab in 2025 to pursue his doctoral degree.
Ph.D. Student
Yiran Xia （夏一然） is a PhD student (2025-) working at the intersection of computer architecture and artificial intelligence. His research centers on building specialized hardware and system support for AI applications like embodied AI and large language models, while also investigating AI-assisted EDA design for agile chip development.
Ph.D. Student
My research interests focus on quantum computer system design and fault-tolerant quantum computing, to bridge the gap between algorithms and bottom-layer physical devices.
Ph.D. Student
My research focuses on the relationship between Hardware Accelerators and High-Performance Computing (HPC).
Ph.D. Student
My research interests focus on fault-tolerant quantum computing and quantum compiler design, especially for continuous-variable systems, to bridge the gap between resource theory, magic-state engineering, and scalable quantum hardware.
Ph.D. Student
Ziyi Zhou (周子易) is a Ph.D. student in the Department of ECE at HKUST, supervised by Prof. Yuan Xie. She received her M.S. degree in Computer Science from Dartmouth College and her B.S. degree in Computer Science from Southeast University. Her research interests focus on Large Language Models (LLMs), Embodied AI, and hardware-software co-design for AI accelerators.
Exchange Student
Shiyu Huang (黄诗宇) is an undergraduate at Nanjing University and an incoming Ph.D. student at HKUST.
Exchange Student
Yi Shi is an undergraduate student from the Elite Program in Integrated Circuit Design and Integrated System at Nanjing University, and an incoming visiting student at HKUST.
Exchange Student
Yi Sun (孙燚) is an incoming Ph.D. student. His research interests focus on computer architecture and AI for architecture.
Incoming Ph.D. Student
Wenbo Huang (黄文博) is an undergraduate student at Nanjing University. His research interests include Embodied AI, multimodal foundation models, and efficient AI systems, with a focus on vision-language-action models, robotic learning, and hardware-software co-design for emerging intelligent applications.
Incoming Ph.D. Student
Xunyuan Liu (刘洵远) is currently a third-year undergraduate at THUEE and an incoming visiting student at HKUST.
Incoming Ph.D. Student
Xinyao Wang (王歆窈) is a third-year undergraduate from Tsinghua University, EE Department, and an incoming visiting student at HKUST.
Incoming Ph.D. Student
Yi Xu (徐一) is an undergraduate from Tsinghua University, EE Department, and an incoming Ph.D. student at HKUST.
Prof. Yuan Xie of HKUST and five co-authors have received the 2026 A. Richard Newton Technical Impact Award in Electronic Design Automation at the 63rd Design Automation Conference, recognizing the lasting impact of their 2008 DAC paper on 3D-stacked MRAM as a universal memory replacement.
FACT Lab at HKUST has partnered with Intel to establish the “HKUST-Intel Joint Laboratory.” This three-year research initiative will focus on high-efficiency near-memory computing architectures to address performance and energy efficiency challenges in artificial intelligence (AI) applications. Led by Prof. Xie Yuan, the joint laboratory aims to innovate through hardware and software co-design, paving the way for the future of intelligent devices and sustainable AI systems. The collaboration was formalized in a signing ceremony attended by senior officials from both HKUST and Intel, underscoring their commitment to advancing green technological innovation and sustainability.
Professor Yuan Xie has been honored with the 2025 Chinese Institute of Electronics Science and Technology Award for the project “High-Energy-Efficiency Circuit Representation and Optimization Methods for Large-Scale Sparse Matrix Computation.” This prestigious recognition, achieved in collaboration with researchers from Tsinghua and Peking University, highlights the laboratory’s commitment to advancing energy-efficient computing technologies.
带你一览计算机体系结构顶会ISCA 2025的精彩内容！本文深入解读了AI加速器、存储系统、量子计算等前沿方向的最新研究成果，展示了体系结构领域的技术风向与未来趋势。
LLM.265研究发现，视频编码器本身就是一种高效的大模型张量编码器。原本用于播放8K视频的现成视频编解码硬件，其实压缩AI模型数据的效率也非常高，甚至超过了许多专门为AI开发的方案。
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