Pingchuan Ma

I am a final 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.


"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process
Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
On the Feasibility of Specialized Ability Extracting for Large Language Code Models


Enabling Runtime Verification of Causal Discovery Algorithms with Automated Conditional Independence Reasoning
Explain Any Concept: Segment Anything Meets Concept-Based Explanation
Causality-Aided Trade-off Analysis for Machine Learning Fairness
PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis
Towards Practical Federated Causal Structure Learning
XInsight: eXplainable Data Analysis Through The Lens of Causality
CC: Causality-Aware Coverage Criterion for Deep Neural Networks
sem2vec: Semantics-Aware Assembly Tracelet Embedding
Deceiving Deep Neural Networks-Based Binary Code Matching with Adversarial Programs
NoLeaks: Differentially Private Causal Discovery Under Functional Causal Model
ML4S: Learning Causal Skeleton from Vicinal Graphs
Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning
NeuralD: Detecting Indistinguishability Violations of Oblivious RAM with Neural Distinguishers
Enhancing DNN-Based Binary Code Function Search With Low-Cost Equivalence Checking
Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings
MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations
MetaInsight: Automatic Discovery of Structured Knowledge for Exploratory Data Analysis
Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models


  • 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.


  • 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


  • 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 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 International Journal of Computer Vision (IJCV), IEEE Signal Processing Letters (SPL)
  • 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)