Photo of Daniel de Leng

Daniel de Leng

Assistant Professor

Artificial intelligence can help us make sense of a world that is increasingly observed in real time. I study machine reasoning techniques that turn continuous streams of observations into a clearer understanding of that world.

Artificial intelligence for democracy

AI empowers those who wield it, so it matters a great deal who that is. As an AI researcher, I work on techniques that are transparent, trustworthy, and for everyone.

Making sense of a streaming world

Much of today's information is generated continuously. Traffic cameras track how busy the roads are, and drones overhead emit telemetry, much as servers on the ground do. Emerging technologies such as collaborative autonomous systems both produce and consume information, drawing on external streams to carry out their own tasks. These stream-generating resources are increasingly heterogeneous, geographically distributed, and unreliable, with no centralised ownership. Yet the streams they produce are critical to a better understanding of the world—an understanding that in turn strengthens democracy, encourages innovation, and creates value. No single person can keep track of what information exists, let alone how to access it. And because the landscape of streams is constantly shifting, even maintaining the streams needed to answer a single question is difficult. I develop artificial intelligence techniques to address these problems. My research centres on expressing spatio-temporal questions, finding the information needed to answer them, negotiating access across organisations, and robustly orchestrating the resulting processing—all while keeping auditability in mind.

Applied AI education

AI is a fast-moving field: data-driven approaches have produced major advances in areas such as computer vision, reinforcement learning, and natural language processing. Agentic large language models add powerful new tools, full of promise but also raising fresh challenges—including for education itself. I am involved in several initiatives to build on the strong AI education already offered at Linköping University. I helped establish AI Academy, where I also provide technical support; it sets practical AI challenges for talented students across all programmes and runs upskilling courses for industry partners and government organisations. I am also responsible for developing an arena for applied AI education, offering courses that combine data-driven AI, its regional and organisational context, and the hands-on use of AI tools.

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.
  • SAIS Master's Thesis Award recipient 2014.
  • Local organising committee SR2019 workshop
  • Part of the Stellar ad-hoc AI cluster initiative.
  • Former member of the AI Academy management team.
  • Organiser for the Semantic Web PhD student colloquia.

 

Publications

Licentiate and PhD Theses

2026

Eva Blomqvist, Daniel de Leng, Robin Keskisärkkä, Oskar Storm, Christina Stålhandske, Karin Wannerberg, Mikael Lindecrantz (2026) Ontology-based Semantic Interoperability for the Circular Economy: The Case of Flat Glass Proceedings of the 4th International Workshop on Knowledge Graphs for Sustainability (KG4S) (Conference paper)
Amath Sow, Mauricio Rodriguez, Fabíola M. C. de Oliveira, Mariusz Wzorek, Daniel de Leng, Mattias Tiger, Fredrik Heintz, Christian Rothenberg (2026) Multi UAVs Preflight Planning in a Shared and Dynamic Airspace Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026) (Conference paper) https://dx.doi.org/10.65109/KPWJ5508
Daniel de Leng, Aya Rizk (2026) Applied AI Compass: A decision-support method and toolkit for developing applied AI education (Report)

2025

Aya Rizk, Daniel de Leng (2025) Applied AI: An analysis and recommendations for education (Report)
Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz (2025) Promoting Intersectional Fairness through Knowledge Distillation (Conference paper) https://dx.doi.org/10.3233/FAIA251214

Research

Teaching

About the division

Colleagues at HCS

About the department