Pingchuan Ma

As of 24 April 2024

I am a fourth 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 am a visiting scholar in Sky Lab at UC Berkeley, hosted by Prof. Alvin Cheung. I received my B.Eng. from Beijing Electronic Science and Technology Institute. My research interests include data management and software engineering.

I can be reached at pmaab at cse dot ust dot hk or pmaab at berkeley dot edu.

Publications

PP-CSA: Practical Privacy-Preserving Software Call Stack Analysis
Zhaoyu Wang, Pingchuan Ma†, Huaijin Wang, and Shuai Wang†
ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA ’24)Code
Testing Graph Database Systems via Graph-Aware Metamorphic Relations
Zeyang Zhuang, Penghui Li, Pingchuan Ma, Wei Meng, and Shuai Wang
International Conference on Very Large Data Bases (VLDB ’24)Code
Evaluating C/C++ Vulnerability Detectability of Query-Based Static Application Security Testing Tools
Zongjie Li, Zhibo Liu, Wai Kin Wong, Pingchuan Ma, and Shuai Wang
IEEE Transactions on Dependable and Secure Computing (TDSC)
On Extracting Specialized Code Abilities from Large Language Models: A Feasibility Study
Zongjie Li, Chaozheng Wang, Pingchuan Ma, Chaowei Liu, Shuai Wang, Daoyuan Wu, Cuiyun Gao, and Yang Liu
International Conference on Software Engineering (ICSE ’24)arXivPress (in Chinese)
Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning
Pingchuan Ma, Zhenlan Ji, Peisen Yao, Shuai Wang, and Kui Ren
International Conference on Software Engineering (ICSE ’24)arXivCode
InsightPilot: An LLM-Empowered Automated Data Exploration System
Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han, and Dongmei Zhang
The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP ’23 Demo Track)arXiv
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
Ao Sun, Pingchuan Ma, Yuanyuan Yuan, and Shuai Wang
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS ’23)arXivPress (in Chinese)Code
Causality-Aided Trade-off Analysis for Machine Learning Fairness
Zhenlan Ji, Pingchuan Ma†, Shuai Wang†, and Yanhui Li
IEEE/ACM International Conference on Automated Software Engineering (ASE ’23)arXivCode
PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis
Zhenlan Ji, Pingchuan Ma†, and Shuai Wang
IEEE/ACM International Conference on Automated Software Engineering (ASE ’23)arXivCode
Towards Practical Federated Causal Structure Learning
Zhaoyu Wang, Pingchuan Ma†, and Shuai Wang
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD ’23)arXivCode
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)arXivPress (in Chinese)Code
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

Preprint

Benchmarking Multi-Modal LLMs for Testing Visual Deep Learning Systems Through the Lens of Image Mutation
Liwen Wang, Yuanyuan Yuan, Ao Sun, Zongjie Li, Pingchuan Ma, Daoyuan Wu, and Shuai Wang
arXiv
Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning
Ao Sun, Yuanyuan Yuan, Pingchuan Ma, and Shuai Wang
arXiv
An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarios
Zongjie Li, Wenying Qiu, Pingchuan Ma, Yichen Li, You Li, Sijia He, Baozheng Jiang, Shuai Wang, and Weixi Gu
arXiv
VRPTEST: Evaluating Visual Referring Prompting in Large Multimodal Models
Zongjie Li, Chaozheng Wang, Chaowei Liu, Pingchuan Ma, Daoyuan Wu, Shuai Wang, and Cuiyun Gao
arXiv
InstructTA: Instruction-Tuned Targeted Attack for Large Vision-Language Models
Xunguang Wang, Zhenlan Ji, Pingchuan Ma, Zongjie Li, and Shuai Wang
arXiv
Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approach
Zhenlan Ji, Pingchuan Ma, Zongjie Li, and Shuai Wang
arXiv
Split and Merge: Aligning Position Biases in Large Language Model based Evaluators
Zongjie Li, Chaozheng Wang, Pingchuan Ma, Daoyuan Wu, Tianxiang Li, Shuai Wang, Cuiyun Gao, and Yang Liu
arXiv
"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process
Pingchuan Ma, Zongjie Li, Ao Sun, and Shuai Wang
arXiv

† means corresponding author.

Award

  • Overseas Research Award, HKUST Fok Ying Tung Graduate School, 2024
  • UGC Research Travel Grant, 2023-24 Academic Year.
  • Future of Life Institute Travel Support, 2024.
  • UGC Research Travel Grant, 2022-23 Academic Year.
  • SIGMOD Student Travel Award, April 2023.
  • 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

  • Visiting Scholar, Sky Lab, UC Berkeley, hosted by Prof. Alvin Cheung, 2024,4 - present
  • 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

  • Elevating Exploratory Data Analysis in The Era of Large Language Model, Huawei, 15 Dec, 2023.
  • 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 ECML/PKDD '24, KDD '24, SDM '24, NeurIPS Ethics Review '23, SIGMOD ARI '23, Queer in AI @ ACL '23, ECML/PKDD '23, 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 Harvard Data Science Review, Scientific Reports, Journal of Systems & Software (JSS), International Journal of Computer Vision (IJCV), IEEE Signal Processing Letters (SPL)
  • Sub-Reviewer of ISSTA '24, FSE '24, 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)