Biografie

Julian Kooij (1982) is an Associate Professor at the Intelligent Vehicles group, performing research on multi-sensor vehicle perception for autonomous driving. The group is part of the Cognitive Robotics (CoR) department of the 3ME Faculty.
His research interests include computer vision, 3D object detection by vision/lidar/radar and acoustic, visual localization, semantic environment understanding, and trajectory forecasting for Vulnerable Road User (VRU) behavior. His team develops novel techniques using deep learning, including representation learning and self-supervised approaches, statistical machine learning and probabilistic inference.
A main focus is to address these tasks in the urban environment, where traffic is highly dynamic and interactions with VRUs are frequent, the visibility is limited, and GPS/GNSS reception unreliable.

He is a member of the Delft unit of the European Laboratory for Learning and Intelligent Systems ( ELLIS). Together with dr. Liangliang Nan from the Architecture & Built Environment (ABE) faculty, he co-founded the cross-faculty 3D Urban Understanding (3DUU) Delft AI Lab. Before joining 3ME, he was a PostDoc at the pattern recognition and computer vision lab of the EWI Faculty of TU Delft. In this period, he collaborated with Leiden University Medical Hospital, and developed new signal processing techniques to detect subtle tremors in patients. He completed his PhD in 2015 at the University of Amsterdam on the topic of automated analysis of pedestrian tracks, using probabilistic graphical models for unsupervised learning and online predictive Bayesian inference. In 2013 he interned at Daimler AG in Ulm, Germany. There, he worked on improved pedestrian path prediction for intelligent vehicles, exploiting various contextual cues such as pedestrian head orientation and location relative to the road.

Links

Personal website, Google Scholar, ResearchGate, IV Group website

Projecten

• 3D Urban Understanding (3DUU) Delft AI Lab
  
• EU Epistemic AI (E-Pi)
  
• STW Perspectief - Efficient Deep Learning (EDL)
  
• EU PROSPECT
  
• STW SafeVRU
  
• Technology in Motion (TIM)
  
• EU ADABTS
• NWO CASSANDRA

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