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Daniel de Leng

Assistant Professor

I am an assistant professor with the division for Human-Centered Systems, working on applied human-centered AI.

Applied Artificial Intelligence

I am an assistant professor with the division for Human-Centered Systems and part of the Semantic Web group.


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. Certifying these systems requires the ability to show adherence to these rules using explanations that can be understood by humans. This highlights the need for the ability to combine reasoning and learning.

My research interests revolve around explainable and human-centered 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 (Wallenberg Information Technology and Autonomous Systems) project performed at the AI and Integrated Computer Systems (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 the Reasoning and Learning Lab (ReaL) at AIICS, I developed and maintained Stellar, which is an 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 (see below) 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.

 

Publications

Licentiate and PhD Theses

2024

Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz (2024) FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability Proceedings of the 2nd Workshop on Fairness and Bias in AI, co-located with 27th European Conference on Artificial Intelligence (ECAI 2024), Article 16 (Conference paper)
Daniel de Leng, Pieter Bonte (2024) Last Night in Sweden: A Vision for Resource-Intelligent Stream Reasoning PROCEEDINGS OF THE 18TH ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED AND EVENT-BASED SYSTEMS, DEBS 2024, p. 103-109 (Conference paper) Continue to DOI
Daniel de Leng, Pieter Bonte (2024) Last Night in Sweden: A Vision for Resource-Intelligent Stream Reasoning Proceedings of the 18th ACM International Conference on Distributed and Event-based Systems (DEBS ’24) (Conference paper)
Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, Giacomo Ziffer (2024) Grounding Stream Reasoning Research Transactions on Graph Data and Knowledge (TGDK), Vol. 2, p. 1-47, Article 2 (Article in journal) Continue to DOI

2023

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 (Conference paper) Continue to DOI

Research

AI Academy

About the division

Colleagues at HCS

About the department