Mohammad Hanafy

- Thesis Topic: Ghost Objects Detection Using Machine Learning for Autonomous Vehicles. - Grade: 1.0/1.0 (Sehr Gut) - Implemented and compared the results of seven classical ML models (including two ensemble tree models) and two different DL models (LSTM and ICL) to choose the best fit in detecting edge cases of ”ghost“ objects. - Deployed multiple models to detect ghost objects based on various features from different perception stack versions, achieving results of over 90%.

- Streamlined the manual scenario annotation and evaluation process., and aided the Scenario Identification team in analyzing real-world autonomous data to drive statistics and insights, performed manual annotations, pipeline executions, and comparing the outputs. - Co-developed several projects and dashboards using Python, AWS, SQL, and DASH.

- Labeling and creating a dataset for various customers to be used in the inspection of products.

- Analysed data from OEMs to identify anomalies and patterns before, during, and after breakdowns, aiming to reduce unplanned downtime.

- Organized and maintained the manufacturing and control equipment hall. - Designed support materials using SolidWorks.

Computer Hardware/ Software Engineering Specialization Grade: 1.5/1.0 (Sehr Gut)

The Artificial Intelligence Professional (AI-Pro) program is a 9-month post-graduate professional training program in Machine Learning and Artificial Intelligence. The program provides a unique learning experience that balances theoretical foundations, practical tools, and applications—enabling many job opportunities in the fastest-growing field of technology. The program was developed at the French Graduate School of Computer Science and Advanced Technologies (EPITA).

Grade: 1.89/0.7 (Very Good)