Chunyang Chen
Scholar

Chunyang Chen

Google Scholar ID: 3tyGlPsAAAAJ
Professor at Department of Computer Science, Technical University of Munich
Software EngineeringDeep LearningHuman Computer InteractionLLM4SEGUI
Citations & Impact
All-time
Citations
6,577
 
H-index
40
 
i10-index
90
 
Publications
20
 
Co-authors
3
list available
Resume (English only)
Academic Achievements
  • Best Paper Honorable Mention at CHI 2024
  • Discovery Early Career Researcher Award (DECRA) from Australian Research Council
  • ACM SIGSOFT Early Career Researcher Award (2023)
  • ACM SIGSOFT Distinguished Paper Award at ICSE 2023
  • ACM SIGSOFT Distinguished Paper Award at ICSE 2021
  • Dean's Award for Research Impact (Economic and Social Impact), Faculty of IT, Monash University
  • Facebook Research Award in Probability and Programming (2020)
  • ACM SIGSOFT Distinguished Paper Award at ICSE 2020
  • ACM SIGSOFT Distinguished Paper Award at ASE 2018
  • Best Paper Award at SANER 2016
  • Best Tool Demo at ASE 2016
  • Extensive publications in top-tier venues including ICSE, FSE, ASE, CHI, CSCW, TSE, TOSEM, TDSC, TIFS, ISSTA, UIST, ACL, etc.
Background
  • Full Professor (Chair for Software Engineering & AI) at the Department of Computer Science, Technical University of Munich (TUM), Heilbronn, Germany
  • Core member of the Munich Data Science Institute
  • Board member of the Heilbronn Data Science Center
  • Fellow at Fortiss
  • Adjunct Professor at Monash University, Australia
  • Research focuses on Software Engineering, Deep Learning, and Human-Computer Interaction
  • Key research areas include: AI/LLM-assisted automated mobile app development (requirement elicitation, UI design, UI code generation, GUI testing, usability, accessibility testing, bug replay)
  • AI-powered mining of software repositories (library/API migration, collaborative editing, query reformulation, thesaurus construction)
  • Robustness and security of deep learning and mobile apps (on-device model attacks, backdoor attacks, phishing, defense mechanisms)
  • Applications of Large Language Models in software engineering and HCI (software testing, bug replay, accessibility, smart assistant development, dataset construction)