Hi everyone! I am a PhD candidate in the department of Computer Science and Engineering at UC Riverside. My research interests include heterogeneous computing, storage systems, machine learning frameworks, database, high-performance computing and performance optimization.
[Curriculum Vitae][Google Scholar][LinkedIn]
University of California, Riverside (Ph.D., Computer Science and Engineering, 2017-present)
Advisor: Dr. Hung-Wei Tseng
University of California, San Diego (M.S., Materials Science and Engineering, 2017)
National Sun Yat-Sen University (B.S., Materials and Optoelectronic Science, 2013)
Primary Research Projects
Characterizing the Performance of Machine Learning Applications in Different Compute Platforms
- Profiled a set of augmented reality applications on the server and identified the major bottleneck in the pipeline
- Formulated machine learning tasks using SQL-like syntax
Accelerating the Performance of Database Applications Using Tensor Core Units
- Designed algorithms to transform database operators into tensor algebra to leverage matrix processing in modern AI/ML hardware accelerators
- Developed a database query engine that utilizes matrix-based database operators using Tensor Core Units to speed up data analytics including entity matching, graph analysis and data warehouse queries
Near-Data Approximate Computing
- Designed and developed a storage system that complements conventional approximate computing systems by offloading the data preparation stage to the controllers in modern non-volatile memory-based solid-state devices
- Developed approximate computing kernels for several machine learning, data mining, image processing, and scientific computing applications using Python and C/C++
Improving the Performance of Machine Learning Applications in Heterogeneous Computers
- Developed a framework to profile the GPU memory utilization for TensorFlow based machine learning applications running on AMD’s ROCm (RadeonOpenCompute) platform using Python
- Research Intern, Microsoft Corp., Remote Jun. 2022–Sep. 2022 Advisor: Dr. Matteo Interlandi
- Software Engineer Intern, Samsung America, San Jose May 2018–Aug. 2018 Advisor: Dr. Pankaj Mehra and Dr. Yang-Suk Kee
- Research Assistant, San Diego Supercomputer Center Jul. 2016–Apr. 2017 Advisor: Dr. Andreas Go ̈tz
- Yu-Ching Hu, Yuliang Li, and Hung-Wei Tseng. TCUDB: Accelerating Database with Tensor Processors. In the 2022 ACM SIGMOD/PODS International Conference on Management of Data, SIGMOD 2022, 2022.
- Yu-Ching Hu, Murtuza Taher Lokhandwala, Te I, and Hung-Wei Tseng. Varifocal Storage: Dynamic Multi- Resolution Data Storage. IEEE Micro, Special Issue on the Top Picks from Computer Architecture Conferences, vol- ume 40(3):47–55, 2020.
- Yu-Ching Hu, Murtuza Lokhandwala, Te I and Hung-Wei Tseng. Dynamic Multi-Resolution Data Storage. In the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2019. (Best Paper Honorable Mention. MICRO Top Picks in Computer Architecture 2019)
- Te I, Murtuza Lokhandwala, Yu-Ching Hu, and Hung-Wei Tseng. Pensieve: a Machine Learning Assisted SSD Layer for Extending the Lifetime. In IEEE International Conference on Computer Design (ICCD 2018). October, 2018.