I'm a postdoc at UCLA working with Prof. Miryung Kim. I got my PhD degree from The Chinese University of Hong Kong in Dec. 2017, under the supervision of Prof. Qiang Xu. I have mentored five students, including three female students, at both CUHK and UCLA. All of them have published research papers in top-tier conferences.
I am on the academic job market this year!
My research belongs to the exciting cross-disciplinary area Software Engineering for Data and Compute Intensive Systems, an upcoming field with a unique combination of software engineering, heterogeneous computing, data-intensive scalable computing (DISC) systems, and quantum computing. The vision of my research is to lower the programming, testing, and performance barriers of applicatons that are data and compute intensive.
My PhD research focused on heterogeneous computing with energy-efficient ASIC accelerators and I have published at TCAD, DAC, ICCAD, and DATE. My approach bridges the gap between application characteristics and innovative hardware technologies by trading accuracy for better performance and energy efficiency. During my research, I noticed the difficulties in developing applications on emerging computing platforms. Therefore, my most recent research at UCLA has been focusing on improving developer productivity via automated analysis tools in the emerging domain of big data applications and heterogeneous computing and I have published at ICSE and ASE.
- BigFuzz (ASE2020) adapts fuzz testing to big data analytics, wherein the testing complexity comes from long setup latency and sources other than control flow such as equivalence class cases of dataflow operators. My work is unique in making fuzzing feasible for big data analytics using framework abstraction.
- HeteroRefactor (ICSE2020) expands the exising automated code refactoring to reduce the human effort of creating an efficient synthesizable FPGA accelerator using HLS. It contains a novel combination of dynamic invariant analysis, automated refactoring, and selective offloading. This work is the first attempt, to our knowledge, for software engineering researchers to address programming difficulties in heterogeneous computing. This work is a part of Intel/NSF CAPA project. The Intel researchers are encouraging us to tech transfer this work to Intel's HLS compiler I++ as the computer architecture is becoming increasingly inclusive of FPGA incorporation.
- ApproxIt (TCAD2020,DAC2014) is a quality managment framework of heterogeneous computing with apprximaite kernels for iterative methods. To our knowledge, this is the first approximate computing work in the literature that ensures the final output quality of iterative methods at algorithm-level.
- ApproxANN (DATE2015) is an energy efficient accelerator for neural networks, ranking within the top 1% most cited papers published that year.
Software Engineering for Data and Compute Intensive Systems
- BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction
by Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar, Rohan Padhye, Miryung Kim
The 35th IEEE/ACM International Conference on Automated Software Engineering, 13 pages, ASE'20 [slides][video][tool] (Acceptance Rate: 22.5%)
- HeteroRefactor: Refactoring for Heterogeneous Computing with FPGA
by Jason Lau*, Aishwarya Sivaraman*, Qian Zhang*, Muhammad Ali Gulzar, Jason Cong, Miryung Kim [* are equal co-first authors, ordered alphabetically by their last names.]
The 42nd IEEE/ACM International Conference on Software Engineering, 13 pages, ICSE'20 [slides][video][tool] (Acceptance Rate: 20.9%)
Energy-Efficient Compute Intensive Platforms
- ApproxIt: A Quality Management Framework of Approximate Computing for Iterative Methods
by Qian Zhang, Qiang Xu
The IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2020
- ARSketch: Sketch-Based User Interface for Augmented Reality Glasses
by Zhaohui Zhang*, Haichao Zhu*, Qian Zhang [* are equal co-first authors, ordered alphabetically by their last names.]
The 28th ACM International Conference on Multimedia, 9 pages, ACM MM'20 (Acceptance Rate: 27.8%)
- Lookup Table Allocation for Approximate Computing with Memory Under Quality Constraints
by Ye Tian, Qian Zhang, Ting Wang, Qiang Xu
The IEEE/ACM Design, Automation, and Test in Europe, full paper, DATE'2018 (Acceptance Rate: 24%)
- ApproxLUT: A Novel Approximate Lookup Table-Based Accelerator
by Ye Tian, Ting Wang, Qian Zhang, Qiang Xu
The IEEE/ACM International Conference on Computer-Aided Design, full paper, ICCAD'2017 (Acceptance Rate: 26%)
- ApproxEigen: An Approximate ComputingTechnique for Large-Scale Eigen-Decomposition
by Qian Zhang, Ye Tian, Ting Wang, Feng Yuan, Qiang Xu
The IEEE/ACM International Conferenceon Computer-Aided Design, full paper, ICCAD'2015 (Acceptance Rate: 24.6%)
- ApproxANN: An Approximate ComputingFramework for Artificial Neural Network
by Qian Zhang, Ting Wang, Ye Tian, Feng Yuan, Qiang Xu
The IEEE/ACM Design, Automation, and Test in Europe, full paper, DATE'2015 (Acceptance Rate: 22.4%)
- ApproxMA: Approximate Memory Accessfor Dynamic Precision Scaling
by Ye Tian, Qian Zhang, Ting Wang, Feng Yuan, Qiang Xu
The Great Lakes Symposium on VLSI, GLVLSI'2015 (invited)
- ApproxIt: An Approximate Computing Framework for Iterative Methods
by Qian Zhang, Feng Yuan, Rong Ye, Qiang Xu
The IEEE/ACM Design Automation Conference, full paper, DAC'2014 (Acceptance Rate: 22.1%)
- On hybrid memory allocation for FPGA behavioral synthesis
by Qian Zhang, Chenfei Ma, Qiang Xu
The ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, poster, FPGA'2014
Runtime Quality Management
- ApproxQA: A Unified Quality Assurance Framework for Approximate Computing
by Ting Wang, Qian Zhang, Qiang Xu
The IEEE/ACM Design, Automation, and Test in Europe, full paper, DATE'2017 (Acceptance Rate: 24%)
- On Effective and Efficient Quality Management for Approximate Computing
by Ting Wang, Qian Zhang, Nam Sung Kim, Qiang Xu
The IEEE/ACM International Symposium on Low Power Electronicsand Design, full paper, ISLPED'2016 (Acceptance Rate: 32%)
Resilience-Aware Task Scheduling
- On Resilient Task Allocation and Scheduling with Uncertain Quality Checkers
by Qian Zhang, Ting Wang, Qiang Xu
The IEEE/ACM Asia and South Pacific Design Automation Conference, full paper, ASPDAC'2017 (Acceptance Rate: 31%)
- ApproxMap: On Task Allocationand Scheduling for Resilient Applications
by Yi Juan, Qian Zhang, Ye Tian, Ting Wang, Weichen Liu, Edwin H.-M. Sha, Qiang Xu
The IEEE/ACM Asia and South Pacific DesignAutomation Conference, full paper ASPDAC'2016 (Acceptance Rate: 34.3%)
- Journal Reviewer: IEEE Transactions on Computers (TC), IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), IEEE Transactions on Circuits and Systems forVideo Technology (TCSVT)
- Conference Reviewer: ICFPT'2013, ICCD'2013, ICCD'2014, AC'2016, DFT'2017
- Technical Committee Member: SIGDA Student Research Forum at ASPDAC'2020 and ASPDAC'2021