Position:
Research Assistant

Phone:
+49-40-42878-2180

Room:
4.058

Email: daniel.rashedi(at)tuhh.de

Bio

Daniel Rashedi is a research associate at the Institute for Software Systems (STS) at Hamburg University of Technology and an associate fellow of the Research Training Group CAUSE. He received his B.Sc. (2020) and M.Sc. (2022) in Computer Science, with theses in hardware verification and formal verification. His research focuses on software engineering for AI, in particular on diagnostic signals for neural networks and how to compute them efficiently.

Teaching (Teaching Assistant)

• Functional Programming (Bachelor)
• Software Testing (Master)

Conference Papers

• Conference Paper (TFPiE 2026)
  Automatically Generating Programming Exercises with Open-Source LLMs: Integrating Lecture Slides and Learning Objectives
  M. B. Hoffmann, D. Rashedi, S. Schupp
  15th International Workshop on Trends in Functional Programming in Education, Odense, Dänemark, 2026
   
• Conference Paper (ICSOFT 2025)
  Efficient Hit-Spectrum-Guided Fast Gradient Sign Method: An Adjustable Approach with Memory and Runtime Optimizations
  D. Rashedi, S. Schupp
  20th International Conference on Software Technologies, Bilbao, Spanien, 2025
  Nominiert für den Best Paper Award
  DOI: 10.5220/0013463100003964
   
• Conference Paper (SEAI 2024)
  Repairing Neural Networks for Image Classification Problems Using Spectrum-Based Fault Localization
  D. Rashedi, S. Schupp
  4th IEEE International Conference on Software Engineering and Artificial Intelligence, Xiamen, China, 2024
  DOI: 10.1109/SEAI62072.2024.10674058
   

Journal Articles

• Journal Article (CCIS 2025)**
  Efficient Adversarial Generation through Selective SBFL Guidance and Automated Sub-Model Strategy*
  D. Rashedi, S. Schupp
  Communications in Computer and Information Science, 2025
  DOI: 10.1007/978-3-032-37353-3_1