Ahmed Hendawy

Ahmed Hendawy joined the Intelligent Autonomous Systems Group at TU Darmstadt as a Ph.D. Student in April 2022. He is working on Multi-Task and Continual Reinforcement Learning, supervised by Dr. Carlo D'Eramo (as part of the LiteRL research group).

Ahmed Hendawy has a master's degree in Information Technology from the University of Stuttgart, with a specialization in Computer Engineering. In 2019, Ahmed graduated from the German University in Cairo (GUC) with a bachelor's degree in Mechatronics Engineering.


CORL, AISTATS (Top Reviewer).

Teaching Assistant

Reinforcement LearningSS 2022, SS 2023

His master's thesis, “Constraint-based Optimization Approach for Generalized Few-Shot Object Detection”, was awarded the Sony Award for best master’s thesis at the University of Stuttgart. In addition, his work, "CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection", was recognized by the Best Paper Runner-up Award in the CVPR 2022 Workshop on Limited Labelled Data for Image and Video Understanding (L3D-IVU).

Ahmed Hendawy has served as a reviewer for the Conference on Robot Learning (CoRL) 2023 and AISTATS 2023. His effort was recognized by the Top Reviewer Award at AISTATS 2023. Ahmed Hendawy takes great pleasure in instructing students as a teaching assistant in the Reinforcement Learning course (in the summer semester of 2023 and 2022), in addition to research project supervision of integrated projects (e.g. Latent Generative Replay in Continual Learning, and Memory-free Continual Reinforcement Learning).


  •       Bib
    Guirguis, K.; Abdelsamad, M.; Eskandar, G.; Hendawy, A.; Kayser, M.; Yang, B.; Beyerer, J. (2023). Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors, Winter Conference on Applications of Computer Vision (WACV) 2023.
  •     Bib
    Guirguis, K.; Hendawy, A.;Eskandar, G.;Abdelsamad, M.;Kayser, M.;Beyerer, J. (2022). CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection, Workshop on Learning with Limited Labelled Data for Image and Video Understanding (L3D-IVU).