Ahmad Khalil, M.Sc.

Ahmad Khalil

Short Bio

Ahmad Khalil is a highly dedicated doctoral researcher with a keen interest in Adaptive Perception Mechanisms in Vehicular Systems. Currently pursuing his doctorate at the KOM department, Ahmad is under the supervision of Prof. Steinmetz. He earned his master's degree in Distributed Software Systems from TUDa in 2021, with a thesis titled "Realization of Multi-tree Federated Learning".

As a committed researcher, Ahmad is an integral part of the "MAKI– Multi-Mechanisms Adaptation for the Future Internet" project in sub-project B1. His research focuses on developing cutting-edge solutions that enhance the perception mechanisms of vehicular systems, making them more adaptive and efficient.

Research Interests

My research interests are primarily focused on the development of advanced solutions that enhance the perception mechanisms of vehicular systems, resulting in greater adaptivity and efficiency. My work is grounded in a deep understanding of the following areas:

  • Adaptivity
  • Vehicular Perception
  • Vehicular Networks
  • Federated Learning

Supervised Theses

 
Student Topic Thesis type Semester Status
Kristina Raysbikh Adaptive Object Detection Model Compression with Contextual Knowledge Integration Master WS2324 Ongoing
Hani Aldebes Efficient Ground Truth Extraction for Federated Object Detection in Vehicular Networks Master WS2324 Ongoing
Ha Giang Hoang Tran Resource-Aware Active Learning for Object Detection Master SS2023 Completed
Tizian Dege Heterogeneous Data Handling for Robust Vehicular Collective Perception Master SS2023 Completed
Malaz Mursi Adaptive Noise Removal Pipeline to Enhance Vehicular Perception in Adverse Weather Conditions Master WS2223 Completed
Yulian Sun Federated Transfer Learning with Multimodal Data Master SS2022 Completed

Supervised Seminars/Labs

 Student Topic Type Semester Status
Weiliang Wang
Srividya Ravikumar		 Transformers for Real-time Object Detection MMC seminar WS2324 Completed
Samuel Loka
Daniel Jonatan 
Raynard Widjaja		 Integrating YOLO with Transformers in Federated Learning Framework MMC Lab WS2324 Completed
Radwan Alzoubi, Hani Aldebes Implementing a simulation for collective perception in vehicular networks (Federated learning approach) MMC Lab SS2023 Completed
Oemer Yilmaz, Ardit Leskaj Power consumption of GPU cards/chips utilized in electric vehicles MMC seminar SS2023 Completed
Minh Hieu Le, Alexander Stichling Sensor Data Denoising in Vehicular Perception Applications  MMC seminar WS2223 Completed
Tizian Dege Enabling adaptive perception model training MMC Lab (Project) WS2223 Completed
Deveena Jain, Kiran Shinde Machine Learning to Increase the Detection Ability of the Vehicles MMC seminar SS2022 Completed
Ha Giang Hoang Tran, Radwan Alzoubi, Felix Kuennecke  Collective Perception in Vehicular Networks MMC seminar SS2022 Completed
Sepehr Baharloo Implementing a simulation for collective perception in vehicular networks (Federated learning approach) MMC Lab SS2022 Completed
Rostyslav Olshevskyi, Malaz Mursi Federated Learning (FL) for Point Clouds Classification in Vehicular Networks MMC Lab WS2122 Completed

HIWI Students

 
Student From To
Rostyslav Olshevskyi 01.04.2022 31.03.2023

 

 

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