News [Feb 2025]: 2 PHD positions for 2026 August are available. Applications please follow here.

Eric Lo is an Associate Professor of Computer Science and Engineering (CSE) at the Chinese University of Hong Kong (CUHK). His research mainly focuses on video analytics, supercomputing, database, distributed systems, data science, and blockchain.

Dr. Eric Lo is a seasoned Interventional Cardiologist with a global perspective, originally from Hong Kong and now a U.S. citizen. He has advanced expertise in transcatheter aortic valve replacement (TAVR) and Watchman procedures, as well as extensive experience in pacemaker implantation, echocardiography, nuclear cardiology, and ...

Eric Lo, MD, FACC, FCRP has joined Complete Cardiology Care. Over the course of his career, he’s performed more than 5,000 cardiac catheterizations, more than 3,000 other interventions and implanted more than 1,000 pacemakers and defibrillators.

PengFei Zhang, Eric Lo, Baotong Lu: “High-Performance Depthwise and Pointwise Convolutions on Mobile Devices“. In Proceedings of AAAI , 2020. Cloud, Distributed, Database, Operating Systems, and HPC (Supercomputing)

[VLDB’24] Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines. – C. Chang, Eric Lo, C. Yu. Abstract: Machine learning inference pipelines commonly encountered in data science and industries often require real-time responsiveness due to their user-facing nature.

Eric Lo received the PhD degree from ETH Zurich, Switzerland, in 2007. He is currently an associate professor in the Department of Computer Science and Engineering, Chinese University of Hong Kong. His research interests include big data analytic, cloud computing, and any algorithmic and system aspect with respect to modern hardware.

Eric Lo is an Associate Professor of Computer Science and Engineering (CSE) at the Chinese University of Hong Kong (CUHK). Professor Eric Lo’s research mainly focuses on video analytics, supercomputing, database, distributed systems, and data.

Eric Lo副教授先介绍了主存列存储的几个核心技术，在单列上的排序、哈希、扫描和查找，从微观的角度通过数据按字节存储的排列方式，讲述了在单列上的SIMD以及“Column-at-a-Time”方法的实现过程，接着从宏观的角度引出此方法的优点和不足，并讨论可行的优化方案，最后给出了实验关键技术和结果数据。 通过这次报告，Eric...