Wei Le  Associate Professor CV

Computer Science, Iowa State University

Research interests: trustworthy software and AI

     



If you are interested in AI for software engineering, AI for medicine, or analyzing AI models and code, please submit your inquiries here: research track (Ph.D students) and engineering track.

Recent work

Testing GPU Numerics: Finding Numerical Differences Between NVIDIA and AMD GPUs, Super Computing (SC) HPCTESTS 2024

Towards Causal Deep Learning for Vulnerability Detection, ICSE 2024

Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection, ICSE 2024 (talk@TOOS, talk@Google Journal Club)

TRACED: Execution-aware Pre-training for Source Code, ICSE 2024

Research Interests

   

My research area is at the intersection of software engineering and machine learning. I am interested in advancing AI and making it safe and responsible to deploy. We are currently working on AI for software engineering, AI for medicine/health care, various challenges that bring AI to human level intelligence, and reliability of AI implementations. Our team: Program Analysis & AI Lab at Iowa State University

Selected Research Papers  

    [KDD2023] Towards Understanding and Enhancing Robustness of Deep Learning Models against Malicious Unlearning Attacks
    [ICSE23] An Empirical Study of Deep Learning Models for Vulnerability Detection [artifact]
    [ICSE23] GameRTS: A Regression Testing Framework for Video Games [artifact] [talk]
    [ASE22] FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures
    [ICSE22] DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs [artifact]
    [ICSE22] DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning [artifact]
    [ICML21] RNNRepair: Automatic RNN Repair via Model-based Analysis [artifact]
    [ISSTA21] Validating Static Warnings via Testing Code Fragments [artifact], [ISSTA21 talk]
    [ICSE21] DeepLocalize: Fault Localization for Deep Neural Networks [artifact]
    [TSE19] Specifying Callback Control Flow of Mobile Apps Using Finite Automata
    [TSE17] Automatic Loop Summarization via Path Dependency Analysis
    [FSE16] Proteus: Computing Disjunctive Loop Summary via Path Dependency Analysis (Distinguished Paper)
    [ICSE16] Generating Performance Distributions via Probabilistic Symbolic Execution
    [ICSE14] Patch Verification via Multiversion Interprocedural Control Flow Graphs [open source code]
    [ICSE13] Segmented Symbolic Analysis
    [TOSEM13] Marple: Detecting Faults in Path Segments Using Automatically Generated Analyses
    [FSE10] Path-Based Fault Correlations
    [FSE08] Marple, A Demand-Driven Path-Sensitive Buffer Overflow Detector (Best FSE presentation)

Teaching

    2016-2024 Fall/Spring: COM S 342 Principles of Programming Languages

    2016-2023 Fall/Spring: COM S 413, COM S/CPRE 513 Program Analysis

    2020 Spring: COM S 665A Advanced Topics -- the Intersection of Software Engineering and Machine Learning

Professional Activities

    Co-chairing: ASE 2024 Journal First Track, ISSTA 2021 Doctoral Symposium, ICSE 2020 Co-located Events, 2018 Midwest Big Data Summer School Software Analytics Track, ESEC/FSE 2017 Doctoral Symposium, ICST 2017 Workshop, ICST 2016 Testing Tool Demo

    PC/Journal Review: ICSE 2025 Area Chair, ICSE 2023/2022/2021/2019/2016, FSE 2024/2022/2020/2019/2018, ASE 2021/2018/2016, ISSTA 2024/2020/2015, SPLASH 2024, CAIN 2022, TSE 2021/2017/2016/2015, TOSEM 2019/2016/2015/2014, ICST 2016, ICSE 2015 Demonstration, ICPC 2014 ERA, MOBS 2013, WAM 2023

    Panelist: NSF 2023/2022/2019/2018/2016/2014/2013, DOE 2016/2015, NIH 2022

    STEM Volunteering: Instructor @ Software Carpentry and Data Carpentry 2017-2022, SIGSOFT CAPS coordinator: 2014-2015