Welcome to my personal homepage! I am currently working as a PhD Scholar in the Future Everyday group of the department of Industrial Design of TU Eindhoven. My research focuses on leveraging connected data from autonomous vehicles and road users to improve road safety. With a strong multidisciplinary background, I integrates advanced machine learning, computer vision, and human-vehicle interaction technologies into his work.
I have completed his Master of Science from the Indian Institute of Technology (IIT) Madras, where my thesis, titled Data-Driven Control for Marine Vehicle Maneuvering, explored innovative applications of deep reinforcement learning and control strategies for marine vessels. During my time at IIT Madras, I was actively contributed to several cutting-edge projects and honed his expertise in advanced algorithms, trajectory prediction, and reinforcement learning. My undergraduate degree is a Bachelor of Technology in Mechanical Engineering from Jamia Millia Islamia, where I developed a product prototype for Inventory Management 4.0, demonstrating my early interest in applying engineering solutions to real-world challenges.
Throughout his academic and professional journey, I have cultivated a robust skill set, including proficiency in Python, C++, C, and emerging programming languages such as Rust. My technical expertise encompasses computer vision, machine learning frameworks (TensorFlow, PyTorch), and state-of-the-art technologies such as Transformers and Graph Neural Networks. Additionally, his experience in cloud computing platforms like AWS, Google Cloud Platform, and Microsoft Azure positions him as an adept practitioner in deploying scalable and efficient machine learning systems.
My research has been disseminated through various publications in esteemed journals and conferences. I have authored papers on topics such as pedestrian behaviour evaluation using dashcam footage, trajectory forecasting with generative adversarial networks (GANs), and deep reinforcement learning applications in autonomous systems. In particular, his recent work includes analysing user-centric interfaces for autonomous shuttle buses and applying deep learning models to generate realistic traffic scenarios. His contributions to these fields have been recognised in journals like Ocean Engineering, Journal of Marine Engineering anf Technology and conferences such as AutomotiveUI, IHIET-AI, ICICT and ICOE.
In addition to my research achievements, I have also demonstrated leadership in academic service. I have serving as the web chair for the IEEE RO-MAN 2025 conference and as a placement coordinator during his tenure at IIT Madras, fostering collaborative environments for student success. I was also appointed reviewer for prominent journals, including NeurIPS, Ocean Engineering, Journal of Intelligent & Robotic Systems and Engineering Applications of Artificial Intelligence, reflecting my deep engagement with the academic community.
My work is distinguished by its practical focus and innovative applications. My current Ph.D. research aims to design a two-way communication framework between road users and connected vehicles using computer vision and vehicle-to-pedestrian (V2P) communication technologies. This work promises to address critical gaps in road safety, especially in ensuring that communication between vehicles and road users is effective and verified.
Beyond academia, I have a strong foundation in industrial collaboration. His internships include developing machine learning models for premium user protection at Goalwit Technologies and inventory management systems at Guinea Motors Pvt. Ltd. These experiences underscore his ability to translate research into practical solutions. With his multidisciplinary expertise, Shadab Alam is poised to make significant contributions to the fields of autonomous systems and road safety technology.
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