WASP Clusters

WASP is at this stage divided into 12 clusters. 10 clusters mainly consist of PhD students from WASP-AS and two clusters consist of students from WASP-AI. Each cluster involves university, industrial, and affiliated PhD students. The clusters cover areas that are scientifically important, of high relevance, and which form a broad but well-connected research palette. On the software and computation side, one cluster will develop methodology for advanced industrial software development, one cluster focuses on software technology issues, one cluster concerns the security of autonomous systems, and one cluster concerns the rapidly emerging area of autonomy in cloud and communication network infrastructures. Two clusters consider the key aspect of cooperation between autonomous systems and humans, where one cluster tackles perception and robotics, and the other deals with new concepts in human-machine interaction such as cognitive digital companions. One cluster deals with the fundamental questions of localization, and one deals with scalability, both developing new techniques that are expected to be essential in many autonomous systems. The remaining clusters are aiming at the future automated transport systems and at fundamental questions related to artificial intelligence and machine learning.

The PhD student projects are gathered in clusters in order to allow for cooperation between related projects and to create network. Each PhD student belongs to one major cluster and may optionally also be a member of a second, minor, cluster.

The 12 WASP clusters are described in more detail below.

Software Engineering for Smart Systems

Smart and autonomous systems are dependent on software to realize their functionality, but the functionality of these systems must be able to evolve much more rapidly than is possible with classical software engineering approaches. This cluster will study data-driven methods for continuously evolving the functionality and performance of smart systems.


  • Jan Bosch (cluster coordinator) Chalmers
  • Patrizio Pelliccione, Chalmers
  • Per Runeson, Lund University
  • Benoit Baudry, KTH

Link to further information: Software Engineering for Smart Systems

Autonomous Clouds and Networks

This cluster will provide autonomy and predictability in the distributed cloud by developing dynamic, control-based resource management methods for deciding how much and what type of resources to allocate, and when and where to deploy them. The cluster also considers autonomy and analytics for communication networks including both the radio access network and the core network, with a special emphasis on 5G.


  • Erik Elmroth, (cluster coordinator), Umeå University
  • Karl-Erik Årzén, Lund University
  • Maria Kihl, Lund University
  • Dejan Kostic, KTH
  • Martina Maggio, Lund University
  • Philipp Leitner, Chalmers
  • Anton Cervin, Lund University
  • Marina Papatriantafilou, Chalmers
  • Johan Eker, Lund University

Link to further information: Autonomous Clouds and Networks

Perception and Learning in Interactive Autonomous Systems

The cluster will study perception methods based on fusion of multi-modal sensory information in combination with learning for autonomous systems.

  • Kalle Åström, (cluster coordinator), Lund University
  • Danica Kragic , KTH
  • Michael Felsberg, Linköping University
  • Alexandre Proutiere, KTH
  • Jonas Unger, Linköping University
  • Fredrik Kahl, Chalmers

Link to further information: Perception and Learning

Interaction and Communication with Sensor-Rich Autonomous Agents

This cluster will develop the next generation of decision support systems, so called cognitive companions, designed to adaptively reduce the cognitive load caused by the large and rapid information flows while ensuring mission-critical decision timescales.


  • Jonas Unger (cluster coordinator), Linköping University
  • Patrick Doherty, Linköping University
  • Morten Fjeld, Chalmers
  • Anders Ynnerman, Linköping University
  • Jacek Malec, Lund University
  • Jonas Löwgren, Linköping University
  • Petter Ögren, KTH
  • Ingrid Hotz, Linköping University

Link to further information: Interaction and Communication

Smart Localization Systems

Accurate localization anywhere and anytime – of vehicles, robots, humans, and gadgets in both the absolute and relative sense – is a fundamental Component in achieving high level of autonomy. The research challenge is to provide scalable, available and reliable smart localization technology needed to enable future intelligent and autonomous systems.


  • Fredrik Gustafsson, (cluster coordinator), Linköping University
  • Henk Wymeersch, Chalmers
  • Peter Händel, KTH
  • Joakim Jaldén, KTH
  • Patric Jensfelt, KTH
  • Isaac Skog, Linköping University
  • Gustaf Hendeby, Linköping University
  • Bo Bernhardsson, Lund University
  • Fredrik Tufvesson, Lund University
  • Kalle Åström, Lund University
  • Anders Robertsson, Lund University
  • Magnus Oscarsson, Lund University
  • Rickard Karlsson, Linköping University

Link to further information: Smart Localization Systems

Large Scale Optimization and Control

The cluster will develop basic theory and methodology for distributed optimization, learning and decision-making in large scale dynamic systems. This is essential to efficiently and reliably operate infrastructure networks for transportation, communications, data, electricity, heat and water, as well as smart cities and health care. The main research challenges are in the intersection between optimization, control, statistics, machine learning and economics.


  • Mikael Johansson, (cluster coordinator), KTH
  • Anders Rantzer , Lund University
  • Anders Hansson, Linköping University
  • Daniel Axehill, Linköping University
  • Bengt Lennartsson, Chalmers
  • Samuel Qing-Shan, Chalmers
  • Bo Bernhardsson, Lund University

Link to further information: Large-Scale Optimization and Control

Automated Transport Systems

Automated transport systems will revolutionize the efficiency of transportation of people and goods, and at the same time dramatically reduce environmental impact. This cluster concerns optimization of the overall transport performance by taking advantage of new possibilities for efficient communication, accurate position estimation, and smart decision systems.


  • Bo Wahlberg (cluster coordinator), KTH
  • Karl Henrik Johansson, KTH
  • Lars Nielsen, Linköping University
  • Jonas Sjöberg, Chalmers
  • Fredrik Tufvesson, Lund University
  • Henk Wymeersch, Chalmers
  • Jonas Fredriksson, Chalmers
  • Krister Wolff, Chalmers
  • Jonas Mårtensson, KTH
  • Paolo Falcone, Chalmers
  • Lennart Svensson, Chalmers
  • Martin Törngren, KTH

Link to further information: Automated Transport Systems

Security for Autonomous Systems

Autonomous systems need to be designed with security and privacy in mind in order to be trustworthy. In this cluster, we investigate security and privacy research challenges that arise from advances in autonomous systems, and, conversely, how advances in security and privacy research can be used to make autonomous systems safer.


  • Sonja Buchegger, (cluster coordinator), KTH
  • Aikaterini Mitrokotsa, Chalmers
  • Thomas Johansson, Lund University
  • Martin Hell, Lund University
  • Andrei Sabelfeld, Chalmers
  • Mads Dam, KTH
  • Andrei Gurtov, Linköping University
  • Panagiotis Papadimitratos, KTH
  • Morten Fjeld, Chalmers
  • Jeff Yan, Linköping University

Link to further information: Security for Autonomous Systems

Software Technology for Autonomous Systems


  • Martin Monperrus, (cluster coordinator), KTH
  • Benoit Baudry, KTH
  • Görel Hedin, Lund University
  • Christian Schulte, KTH
  • Michael Doggett, Lund University
  • Simin Nadjm-Tehrani, Linköping University
  • Martin Fabian, Chalmers
  • Mary Sheeran, Chalmers
  • Christoph Reichenbach, Lund University
  • Patrizio Pellizione, Chalmers

Link to further information: Software Technology for Autonomous Systems

AI and Machine Learning for Autonomous Systems

A heuristic definition of AI is the development of agents that perceive the world and use these observations to learn about the world, plan and reason. This is tightly connected to Machine Learning, where agents are given the ability to learn from data examples rather than being explicitly programmed. Common to the AI and Machine Learning projects in this cluster are that AI and Machine Learning are used in different ways to make autonomous systems smarter and more capable.


  • Helena Lindgren, (cluster coordinator), Umeå University
  • Hedvig Kjellström, KTH
  • Mattias Villani, Linköping University
  • Martin Rosvall, Umeå University
  • Cristian Sminchisescu, Lund University
  • Erik Frisk, Linköping University
  • Alexandre Proutiere, KTH
  • Fredrik Heintz, Linköping University
  • Christos Dimitrakis, Chalmers
  • Christian Berger, Chalmers
  • Joakim Jaldén, KTH
  • Seif Haridi, KTH
  • Håkan Hjalmarsson, KTH
  • Kalle Åström, Lund University
  • Patrick Doherty, Linköping University
  • Dimos Dimarogonas, KTH

Link to further information: AI and Machine Learning

Machine Learning, Deep Learning and other AI

The focus of this clusters is Machine Learning (ML), Deep Learning (DL) and other AI, where the latter in particular includes eXplainable AI (XAI), i.e., the capacity for an AI system to  answer questions on how it came to its response, to motivate its response and, perhaps, to generalize its response. The projects in this cluster are typically of a more fundamental nature than the projects in the cluster “AI and Machine Learning for Autonomous Systems”.

Program Management group:

  • Danica Kragic, (cluster coordinator), KTH
  • Fredrik Heintz, Linköping University
  • Amy Loutfy, Örebro University
  • Thomas Schön, Uppsala University
  • Helena Lindgren, Umeå University
  • Fredrik Kahl, Chalmers

Link to further information: Machine Learning, Deep Learning and other AI

Mathematical Foundations of AI

The second major focus is mathematics to attack the theoretical basic questions of AI in the broadest sense. As in the case of ML and DL, recent years have emerged new mathematics, which is of great importance both for understanding and applications.

Program management group:

  • Johan Håstad, (cluster coordinator), KTH
  • Sandra di Rocco, KTH
  • Tobias Ekholm, Uppsala University
  • Anders Rantzer, Lund University
  • Holger Rootzén, Chalmers

Link to further information: Mathematical Foundations of AI