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Daniel de Leng is an Assistant Professor at Linköping University, where he focuses on applied human-centered artificial intelligence (AI) within the Division of Human-Centered Systems. His research interests lie in the realm of explainable human-centered AI autonomous systems, particularly investigating the combination of symbolic and subsymbolic AI methodologies. Daniel's PhD work centered around the safe behavioral monitoring of autonomous systems, with a particular focus on logic-based stream reasoning. He contributed to the development of the DyKnow-ROS framework within the Robot Operating System (ROS), motivated by significant outcomes from the Wallenberg Information Technology Autonomous Systems (WITAS) project. His experience includes a tenure at Saab Aeronautics, where he played a crucial role in initiating AI autonomy initiatives. Additionally, Daniel served as a research engineer at the Reasoning Learning Lab (ReaL) in the AIICS division, where he developed and maintained the Stellar at the AI Academy, enhancing research efficiency and supporting course projects involving autonomous systems. His academic credentials include a PhD in Computer Science from Linköping University, awarded in December 2019, and a Licentiate in Computer Science from the same institution in October 2017.
Reasoning Learning Lab (ReaL) • Linköping University
Developed and maintained the Stellar environment for AI research and development.
Linköping University • Linköping, Sweden
Teaching and conducting research in the field of applied human-centered AI.
Requirements are standardized across the Faculty of Science and Engineering (Institute of Technology) for international Master's programs.