Qian Zhang

I have moved to University of California, Riverside as an assistant professor in July 2022. This website is no longer maintained. Please check my new homepage.

I am a Tenure-Track Assistant Professor in Computer Science and Engineering at University of California, Riverside. Prior to that, I was a Postdoctoral Researcher at UCLA during 2019-2022. I worked with Prof. Miryung Kim on software developer tools for democrotizing heterogeneous computing. I got my Ph.D. from The Chinese University of Hong Kong in 2018, adviced by Prof. Johnny Qiang Xu.

My research lies at the intersection of software engineering, heterogeneous hardware design, and data-intensive scalable computing (DISC) systems. I have been selected as one of the eight MIT EECS rising stars in Systems (broadly defined).

• May 2022: I am serving on the ICPADS 2022 program committee.
• May 2022: I am serving on the FSE 2022 Tool Demonstration Track program committee.
• Jan 2022: I am serving on the ICSME 2022 program committee.
• Dec 2021: I am serving on the FUZZING'22 program committee. This event is co-located with NDSS 2022.
• Nov 2021: Our paper on C to HLS-C transpiler is accepted to ASPLOS 2022!
• Oct 2021: I was invited to give a talk on efficient fuzz testing at Technology Innovation Institute, Abu Dhabi.
• Sep 2021: I am chairing the Smell / Debt session in ICSME 2021!
• Aug 2021: Selected as one of the EECS Rising Stars, 2021, hosted by MIT!
• Jul 2021: Our paper on differential testing for quantum software stacks is accepted to ASE 2021!
• Jun 2021: NSF funded my research on reinventing fuzz testing for data and compute intensive applications! I am participating as a senior personnel.
• May 2021: Our paper on fuzz testing for heterogeneous applications is accepted to FSE 2021!
• Jan 2021: I am serving on the ICSME 2021 program committee!

Emerging hardware, like FPGAs and quantum circuits, is shaping the future of heterogeneous computing; however, the use of such extraordinary computing power is restricted to a few software developers with microprocessor expertise. The vision of my research is to democratize the use of heterogeneous computing with re-designed developer productivity tools. I am particularly interested in developing techniques that lower the barriers of porting traditional software to heterogeneous platforms, and making emerging hardware resources equally accessible across a diverse range of developers. This includes research over reinventing testing, refactoring, program transformation, and interactive programming tools.

• HeteroGen: Transpiling C to Heterogeneous HLS Code with Automated
  ASPLOS'22
  
  Test Generation and Program Repair
  Acceptance Rate: 20%
  
  by Qian Zhang, Jiyuan Wang, Harry Xu, Miryung Kim
  The 27th ACM International Conference on Architectural Support for
  Programming Languages and Operating Systems, 13 pages, 2022
• QDiff: Differential Testing of Quantum Software Stacks
  ASE'21
  
  by Jiyuan Wang, Qian Zhang, Harry Xu, Miryung Kim
  Acceptance Rate: 20%
  
  The 36th IEEE/ACM International Conference on Automated
  Software Engineering, 13 pages, 2021
• HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence
  ESEC/FSE'21
  
  by Qian Zhang, Jiyuan Wang, Miryung Kim
  Acceptance Rate: 24.5%
  
  The 29th ACM Joint European Software Engineering Conference
  and Symposium on the Foundations of Software Engineering, 13 pages, 2021
• Efficient Fuzz Testing for Apache Spark Using Framework Abstraction
  ICSE'21 Demonstration
  
  by Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar, Rohan Padhye, Miryung Kim
  Acceptance Rate: 37%
  
  The 43rd IEEE/ACM International Conference on Software Engineering,
  4 pages, Demonstrations, 2021
• BigFuzz: Efficient Fuzz Testing for Data Analytics using Framework Abstraction
  ASE'20
  
  by Qian Zhang, Jiyuan Wang, Muhammad Ali Gulzar, Rohan Padhye, Miryung Kim
  Acceptance Rate: 22.5%
  
  The 35th IEEE/ACM International Conference on Automated
  Software Engineering, 13 pages, 2021 [slides][video][tool]
• HeteroRefactor: Refactoring for Heterogeneous Computing with FPGA
  ICSE'20
  
  by Jason Lau*, Aishwarya Sivaraman*, Qian Zhang*, Muhammad Ali Gulzar,
  Acceptance Rate: 20.9%
  
  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, 2020 [slides][video][tool]
• ApproxIt: A Quality Management Framework of Approximate Computing for Iterative Methods
  TCAD Journal'20
  
  by Qian Zhang, Qiang Xu
  The IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020
• ARSketch: Sketch-Based User Interface for Augmented Reality Glasses
  ACM MM'20
  
  by Zhaohui Zhang*, Haichao Zhu*, Qian Zhang
  Acceptance Rate: 27%
  
  [* are equal co-first authors, ordered alphabetically by their last names.]
  The 28th ACM International Conference on Multimedia, 9 pages, 2020
• Lookup Table Allocation for Approximate Computing with Memory Under Quality Constraints
  DATE'18
  
  by Ye Tian, Qian Zhang, Ting Wang, Qiang Xu
  Acceptance Rate: 24%
  
  The Design, Automation and Test in Europe Conference and Exhibition, 2018
• ApproxLUT: A Novel Approximate Lookup Table-Based Accelerator
  ICCAD'17
  
  by Ye Tian, Ting Wang, Qian Zhang, Qiang Xu
  Acceptance Rate: 26%
  
  The IEEE/ACM International Conference on Computer-Aided Design, 2017
• ApproxQA: A Unified Quality Assurance Framework for Approximate Computing
  DATE'17
  
  by Ting Wang, Qian Zhang, Qiang Xu
  Acceptance Rate: 24%
  
  The Design, Automation and Test in Europe Conference and Exhibition, 2017
• ApproxEigen: An Approximate ComputingTechnique for Large-Scale Eigen-Decomposition
  ICCAD'15
  
  by Qian Zhang, Ye Tian, Ting Wang, Feng Yuan, Qiang Xu
  Acceptance Rate: 22.6%
  
  The IEEE/ACM International Conferenceon Computer-Aided Design, 2015
• ApproxANN: An Approximate ComputingFramework for Artificial Neural Network
  DATE'15
  
  by Qian Zhang, Ting Wang, Ye Tian, Feng Yuan, Qiang Xu
  Acceptance Rate: 22.4%
  
  The Design, Automation and Test in Europe Conference and Exhibition, 2015
• ApproxIt: An Approximate Computing Framework for Iterative Methods
  DAC'14
  
  by Qian Zhang, Feng Yuan, Rong Ye, Qiang Xu
  Acceptance Rate: 22.1%
  
  The IEEE/ACM Design Automation Conference, 2014
• ApproxMA: Approximate Memory Accessfor Dynamic Precision Scaling
  GVLSI'15
  
  by Ye Tian, Qian Zhang, Ting Wang, Feng Yuan, Qiang Xu
  invited
  
  The Great Lakes Symposium on VLSI, 2015
• On Effective and Efficient Quality Management for Approximate Computing
  ISLPED'16
  
  by Ting Wang, Qian Zhang, Nam Sung Kim, Qiang Xu
  Acceptance Rate: 32%
  
  The IEEE/ACM International Symposium on Low Power Electronicsand Design, 2016
• On Resilient Task Allocation and Scheduling with Uncertain Quality Checkers
  ASPDAC'17
  
  by Qian Zhang, Ting Wang, Qiang Xu
  Acceptance Rate: 30%
  
  The IEEE/ACM Asia and South Pacific Design Automation Conference, 2017
• ApproxMap: On Task Allocationand Scheduling for Resilient Applications
  ASPDAC'16
  
  by Yi Juan, Qian Zhang, Ye Tian, Ting Wang, Weichen Liu, Edwin H.-M. Sha, Qiang Xu
  Acceptance Rate: 31%
  
  The IEEE/ACM Asia and South Pacific DesignAutomation Conference, 2016
• On hybrid memory allocation for FPGA behavioral synthesis
  FPGA'14
  
  by Qian Zhang, Chenfei Ma, Qiang Xu
  poster track
  
  The ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2014

• Teaching Assistant:
  CUHK ENGG 5101 Advanced Computer Architecture Spring 2015, Spring 2016
  CUHK CMSC 5719 Seminar Fall 2014
  CUHK CENG 3420 Computer Organization and Design Spring 2014
  CUHK ENGG 2020 Digital Logic and Systems Fall 2012, Fall 2013
  CUHK ENGG 1410b Engineering Mathematics I Spring 2013
  CUHK ENGG 1000 IT Foundation 2012-2016
• Mentoring: I have been lucky to work with the following undergraduate and graduate students at CUHK and UCLA. Female students are highlighted in bold.
  • Jason Teoh, PhD student at UCLA: fuzz testing for detecting performance issues in Apache Spark.
  • Jiyuan Wang, PhD student at UCLA: testing for quantum computing and heterogeneous computing.
  • Ayushi Agarwal, master student at UCLA: input generation for debloating Java bytecode.
  • Aditya Jain, master student at UCLA: input generation for debloating Java bytecode.
  • Harini Suresh, master student at UCLA: detecting numerical errors in python data science.
  • Apoorv Garg, master student at UCLA: detecting numerical errors in python data science.
  • Xilai Zhang, master student at UCLA, now at Google.
  • Aishwarya Sivaraman, PhD student at UCLA.
  • Ye Tian, PhD student at CUHK, now at HiSilicon.
  • Juan Yi, visiting PhD student at CUHK, now at Tencent.
  • Minhao Liu, undergraduate student at CUHK, now PhD student at CUHK.
  • Chenfei Ma, master student at CUHK, now at Wells Fargo.