Tamoghna Das is an engineering leader with more than 20 years of experience in autonomous driving simulation, artificial intelligence, control systems, and automotive software engineering. He currently serves as Product Owner for Autonomous Driving and ADAS Virtual Simulation Platform at Aptiv, where he leads the architecture and development of end-to-end closed-loop simulation platforms that integrate autonomous driving software stacks with high-fidelity sensor models, virtual ECUs, and industry-standard simulation frameworks.
His expertise spans autonomous driving simulation, AI-assisted scenario generation, sim-to-real technologies, neural scene reconstruction, and machine learning applications for virtual validation. He has extensive experience with ASAM standards, including Open Simulation Interface (OSI), OpenSCENARIO, and OpenDRIVE, and has led the development of interoperable simulation platforms that support advanced driver assistance systems such as Automatic Emergency Braking, Adaptive Cruise Control, and automated parking. His work also includes applying NVIDIA COSMOS, Neural Radiance Fields (NeRF), and Neural Reconstruction (NuRec) to bridge the gap between simulation and real-world driving environments.
Before Aptiv, Tamoghna held engineering leadership and technical roles at Valeo, Magneti Marelli, Denso International, KPIT Cummins Infosystems, and the Indian Space Research Organisation (ISRO), where he contributed to functional safety, hardware-in-the-loop validation, automotive control systems, and mission-critical aerospace control technologies.
Tamoghna holds a Master of Science (Research) in Control and Automation from the Indian Institute of Technology (IIT) Delhi and a Bachelor of Engineering in Electrical Engineering from Bengal Engineering College. He has also completed advanced nanodegree programs in Deep Reinforcement Learning, Self-Driving Car Engineering, and Deep Learning, along with professional certifications in Machine Learning and Self-Driving Cars. He is the author of multiple peer-reviewed publications in adaptive control systems, fuzzy logic, and robotics, reflecting his long-standing contributions to intelligent control and autonomous systems research.