Pingchuan Ma

As of 29 March 2023

I am a third year Ph.D. candidate at Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST), under the supervision of Prof. Shuai Wang. I received my B.Eng. from Beijing Electronic Science and Technology Institute. My research interests include data management, trustworthy AI (causality, privacy, fairness, etc.) and software engineering.

I can be reached at pmaab at cse dot ust dot hk.

Research Highlight

* means corresponding author.

Preprint

On the Feasibility of Specialized Ability Stealing for Large Language Code Models
Zongjie Li, Chaozheng Wang, Pingchuan Ma, Chaowei Liu, Shuai Wang, Daoyuan Wu, and Cuiyun Gao
arXivPress (in Chinese)
PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis
Zhenlan Ji, Pingchuan Ma*, and Shuai Wang
arXiv

Publications

XInsight: eXplainable Data Analysis Through The Lens of Causality
Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, and Dongmei Zhang
ACM SIGMOD International Conference on Management of Data (SIGMOD ’23)arXivLink
CC: Causality-Aware Coverage Criterion for Deep Neural Networks
Zhenlan Ji, Pingchuan Ma*, Yuanyuan Yuan*, and Shuai Wang
International Conference on Software Engineering (ICSE ’23)Code
sem2vec: Semantics-Aware Assembly Tracelet Embedding
Huaijin Wang, Pingchuan Ma, Shuai Wang, Qiyi Tang, Sen Nie, and Shi Wu
ACM Transactions on Software Engineering and Methodology (TOSEM)Code
Deceiving Deep Neural Networks-Based Binary Code Matching with Adversarial Programs
Wai Kin Wong, Huaijin Wang, Pingchuan Ma, Shuai Wang, Mingyue Jiang, Tsong Yueh Chen, Qiyi Tang, Sen Nie, and Shi Wu
IEEE International Conference on Software Maintenance and Evolution (ICSME ’22)Code
NoLeaks: Differentially Private Causal Discovery Under Functional Causal Model
Pingchuan Ma, Zhenlan Ji, Qi Pang, and Shuai Wang
IEEE Transactions on Information Forensics & Security (TIFS)Code
ML4S: Learning Causal Skeleton from Vicinal Graphs
Pingchuan Ma, Rui Ding, Haoyue Dai, Yuanyuan Jiang, Shuai Wang, Shi Han, and Dongmei Zhang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22)Code
Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning
Zhenlan Ji, Pingchuan Ma, and Shuai Wang
International Conference on Software Engineering and Knowledge Engineering (SEKE ’22)Code
NeuralD: Detecting Indistinguishability Violations of Oblivious RAM with Neural Distinguishers
Pingchuan Ma, Zhibo Liu, Yuanyuan Yuan, and Shuai Wang
IEEE Transactions on Information Forensics & Security (TIFS)Code
Enhancing DNN-Based Binary Code Function Search With Low-Cost Equivalence Checking
Huaijin Wang, Pingchuan Ma, Yuanyuan Yuan, Zhibo Liu, Shuai Wang, Qiyi Tang, Sen Nie, and Shi Wu
IEEE Transactions on Software Engineering (TSE)Code
Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings
Zongjie Li, Pingchuan Ma, Huaijin Wang, Shuai Wang, Qiyi Tang, Sen Nie, and Shi Wu
International Conference on Software Engineering (ICSE ’22)
MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations
Pingchuan Ma and Shuai Wang
International Conference on Very Large Data Bases (VLDB ’22)Code
MetaInsight: Automatic Discovery of Structured Knowledge for Exploratory Data Analysis
Pingchuan Ma, Rui Ding, Shi Han, and Dongmei Zhang
ACM SIGMOD International Conference on Management of Data (SIGMOD ’21)LinkExtended VersionReference Implementation
Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models
Pingchuan Ma, Shuai Wang, and Jin Liu
International Joint Conference on Artificial Intelligence (IJCAI ’20)Code

Award

  • AISTATS "Top Reviewer", February 2023.
  • Microsoft Research Asia "Star of Tomorrow" Award, December 2022.
  • Microsoft Research Asia "Star of Tomorrow" Award, March 2022.
  • NVIDIA Academic Hardware Grant, March 2022.

Experience

  • Research Intern, Microsoft Research Asia, mentored by Justin Ding, 2022.6 - 2022.11
  • Research Intern, Microsoft Research Asia, mentored by Justin Ding, 2021.6 - 2022.4
  • Research Intern, Microsoft Research Asia, mentored by Justin Ding, 2019.12 - 2020.6

Talk

  • Learning Causal Skeleton from Vicinal Graphs, Microsoft Research Asia, 5 Aug, 2022.
  • Towards Dependable and Transparent Data Analytics Platforms, Microsoft Research Asia, 10 Mar, 2022.
  • Automated Fairness Testing and Beyond, Microsoft Research Asia Causality Reading Group, 6 Sept, 2021.
  • Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models (in Chinese), AI Time, 15 Jan, 2021.

Academic Service

  • Conference Program Committee/Reviewer of FAccT '23, KDD '23, AISTATS '23, PETS '23 Artifact Evaluation, PETS '22 Artifact Evaluation, ISSTA '22 Artifact Evaluation, EuroSys '22 Artifact Evaluation
  • Journal Reviewer of IEEE Signal Processing Letters
  • Sub-Reviewer of ISSTA '23, USENIX Security '23, NeurIPS '22, PETS '22, CCS '22, ASE '22, DBTest '22, AsiaCCS '22, AsiaCCS '21, ICSE '20 Artifact Evaluation, and USENIX Security '20 Artifact Evaluation

Teaching Experience

  • Teaching Assistant, COMP4901N: Competitive Programming in Cybersecurity (Fall 2021)
  • Teaching Assistant, COMP6613C: Topics in Computer Security and Privacy (Spring 2021)