Wei Le  Associate Professor CV

Computer Science, Iowa State University

Research interests: trustworthy software and AI

     



If you are interested in machine learning for code, machine learning for medicine and/or analyzing deep learning systems, please send your CV to weile@iastate.edu. I am mostly looking for PhD students, but exceptional master and undergraduate students will also be considered.

Recent Work and News

Congratulations, Ellie Kloberdanz, for the accepted ICSE22 paper: DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning

Congratulations, Mohammad Wardat, for the accepted ICSE22 paper: DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs

Please check out our open source tool: Hydrogen is a program analysis tool that can compare and analyze software changes and versions. It has been used in our program analysis class

Research Interests

   

I am interested in trustworthy software and AI engineering, program and AI model analysis, and the intersection of software engineering and machine learning. I work with Program Analysis Lab at Iowa State University on machine learning for code, machine learning for medicine, software and AI model analysis and debugging, and automating software engineering tasks of bug detection, testing, debugging and repair.

Selected Research Papers  

    [ICSE22] DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs [code and data]
    [ICSE22] DeepStability: A Study of Unstable Numerical Methods and Their Solutions in Deep Learning
    [ICML21] RNNRepair: Automatic RNN Repair via Model-based Analysis [code and data]
    [ISSTA21] Validating Static Warnings via Testing Code Fragments [data], [talk]
    [ICSE21] DeepLocalize: Fault Localization for Deep Neural Networks [code and data]
    [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
    [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

    2016-2021 Fall/Spring: COM S 342 Principles of Programming Languages

    2016-2021 Spring: COM S 413, COM S/CPRE 513 Foundations and Applications of Program Analysis

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

Professional Activities

    Co-chairing: 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 2023/2022/2021/2019/2016, FSE 2022/2020/2019/2018, ASE 2021/2018/2016, ISSTA 2020/2015, CAIN 2022, TSE 2021/2017/2016/2015, TOSEM 2019/2016/2015/2014, ICST 2016, ICSE 2015 Demonstration, ICPC 2014 ERA, MOBS 2013

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

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