Tristan Gottwald studied Computer Engineering at Leibniz University Hannover, graduating in 2024 with a master's thesis on "Safe Reinforcement Learning for Autonomous Vehicles with few Hard Resets". Since October 2024, he has been working towards his Dr.-Ing. at the Institut für Informationsverarbeitung (TNT).

His research focuses on reinforcement learning under real-world constraints, including safe and resetless training and learning control policies directly from event camera data. He also works on deep learning for medical imaging, particularly the reconstruction of highly accelerated 4D flow MRI. A shared thread across this work is adaptive sensing: deciding how much data to acquire, and how to process it efficiently, so that models get the information they need at lower cost.