Ahmed Abdelhadi

• Train state-of-the-art Deep Neural Networks and evaluate different approaches for intention recognition and motion prediction. • Keeping up-to-date with new research in the fields of deep reinforcement learning and optimal control. • Deploy data pipelines in a research environment. • Implement data augmentation methods in addition to configuring vehicle simulators to gather synthetic data. • Collaborate with other team experts in perception, tracking and various autonomous driving disciplines.

• Worked at the intersection of sensors and algorithms, specifically for radar signal processing. • Contributed to all phases of the software development cycle including prototyping, requirements capture, design, implementation, integration and validation in an Agile development environment. • Developed ECU diagnostic and network communication tools to verify and troubleshoot distributed vehicle systems. • Supported Peer Review of work products like Design Documents, Code, Test Cases in manner.

Artificial Intelligence, Machine Learning, Computer Vision, Mobile Robotics, Deep Learning and Reinforcement Learning. KEY PROJECT: Implemented reinforcement learning methods (i.e. DQN, DDQN, Soft Actor-Critic) on a simulated environment for autonomous vehicle control in highway scenarios.

Data Structures and Algorithms, Computer Organization, Digital Logic Design, Machine Design, Mechanics, Electric Machines, Power Electronics, Sensors and Robotics.