Photo of Daniel de Leng

Daniel de Leng


I am a postdoc and deputy lab leader for the Reasoning and Learning Lab (ReaL).

Applied Artificial Intelligence

I am a postdoc and deputy lab leader for the Reasoning and Learning Lab (ReaL).

AI and Autonomous Systems

Safely deploying autonomous systems in the real world is challenging. These systems often rely on sensors that produce incomplete and uncertain observations, whereas humans tend to reason in terms of more abstract objects and relations. To complicate things further, the rules dictating what behaviour is allowed are often written using these high-level representations. This highlights the need for the ability to combine reasoning and learning.

My research interests revolve around AI and autonomous systems, particularly the combination of symbolic and subsymbolic AI. During my PhD I investigated different ways to robustly monitor safe system behaviour in the context of autonomous systems by performing logic-based stream reasoning. As part of this work, I developed the DyKnow-ROS stream reasoning framework built on the Robot Operating System (ROS). Much of my work was inspired by the pioneering results from the WITAS project performed at the AIICS division and has subsequently been improved on. After my PhD I spent a few years at Saab Aeronautics where I helped initiate and acquire resources for several AI and autonomy initiatives becoming the first Point of Contact for Artificial Intelligence.

ReaL Stellar

When I was a research engineer at ReaL, I develop and maintain Stellar, which is our AIOps environment for AI research and development. It is designed to speed up research by making it easier to test different solutions, and is intended to be used in conjunction with Berzelius. Stellar consists of a network of AI machines running AI development software and can be used as a small cluster for running experiments. Both AI Academy and ReaL make use of Stellar, and the environment has previously been used for course projects involving autonomous systems.

CV in brief

  • PhD Computer Science, Dec 2019, Linköping University, Sweden.
  • Lic Computer Science, Oct 2017, Linköping University, Sweden.
  • MSc Computer Science, Nov 2013, Utrecht University, the Netherlands.
  • BSc Computer Science, Jul 2011, Utrecht University, the Netherlands.
  • Saab Aeronautics' first Point of Contact for Artificial Intelligence until 2022.
  • Part of the AIOps ELLIIT Infrastructure Initiative.
  • Part of the AI Academy management team.



Licentiate and PhD Theses


Ella Olsson, Mikael Nilsson, Kristoffer Bergman, Daniel de Leng, Stefan Carlén, Emil Karlsson, Bo Granbom (2023) Urdarbrunnen: Towards an AI-enabled mission system for Combat Search and Rescue operations Proceedings of the 35th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS 2023), p. 38-45 Continue to DOI
Mattias Tiger, David Bergström, Simon Wijk Stranius, Evelina Holmgren, Daniel de Leng, Fredrik Heintz (2023) On-Demand Multi-Agent Basket Picking for Shopping Stores 2023 IEEE International Conference on Robotics and Automation (ICRA), p. 5793-5799 Continue to DOI


Daniel de Leng (2019) Robust Stream Reasoning Under Uncertainty
Daniel de Leng, Fredrik Heintz (2019) Approximate Stream Reasoning with Metric Temporal Logic under Uncertainty Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), p. 2760-2767 Continue to DOI


Daniel de Leng, Fredrik Heintz (2018) Partial-State Progression for Stream Reasoning with Metric Temporal Logic SIXTEENTH INTERNATIONAL CONFERENCE ON PRINCIPLES OF KNOWLEDGE REPRESENTATION AND REASONING, p. 633-634

AI Academy

About the division

Colleagues at AIICS

About the department