In WASP we are curious and experimenting with different types of scientific collaborations. We firmly believe that openness is a key in creating scientific excellence.
The research within WASP is conducted towards several different goals. Many of our research projects aim to build broad ground theory, some are narrow and breaching the very frontier of our knowledge, others are somewhere in between. Therefore, we are conducting some special research projects with different types of collaborations. On this page you can read more about them.
WASP Expedition Projects
A concept with high gain/high risk, targeted expeditions with a specific challenging goal – that is what the WASP Expedition Projects are about. Rather than developing long term thematic frameworks these two-year projects focuses on taking leaps in a specific area. The projects run across departments and universities and consists of two PIs and up to four Postdocs. The following projects are active.
PIs: Emil Björnson, LIU, Pontus Giselsson, LU
PIs: David Broman, KTH, Magnus O. Myreen, CTH
PIs: Iolanda Leite, KTH, Jana Tumova, KTH
PIs: Benoit Baudry, KTH, Erik Elmroth, UMU
PIs: Bo Bernhardsson, LU, Maria Sandsten, LU
PIs: Giuseppe Durisi, CTH, Katerina Mitrokotsa, CTH
PIs: Anders Hansson, LIU, Bo Wahlberg, KTH
NTU–WASP Collaboration Projects
One of our main international partners is Nanyang Technical University (NTU), Singapore. Together with some of their top researchers and doctoral students we have formed NTU-WASP Collaboration Projects.
Improve the security of existing machine learning algorithms against real-world attackers
Data poisoning attacks on optimization-based supervised learning algorithms and their defense.
- Understand vulnerabilities of supervised learning algorithms. We will find blind spots of a broad family of supervised learning algorithms.
- Design optimal defense strategies against data poisoning attacks. We will develop two defense frameworks: The detection framework aims to accurately and timely detect the occurrence of poisoning attacks and the mitigation framework aims to effectively reduce the influence of attacks on victim learning models.
- Improve solution robustness and evaluate proposed methods. We will refine the detection and mitigation frameworks and propose new algorithms to compute optimal defense. We will design evaluation frameworks and validate solutions on real-world data sets
Bo An (NTU), Chew Lock Yue (NTU), Christos Dimitrakakis (Chalmers), Devdatt Dubhashi (Chalmers)
To develop co-evolutionary algorithms for reinforcement learning in multi-agent systems
- Demonstrate the techniques on adaptive scheduling of an intelligent autonomous bus network at NTU campus
- Proof of concept for elevator scheduling (Figure 1)
- Data analytics on campus bus network (Figure 2)
- Preliminary results in simulations (Figure 3)
Chew Lock-Yue (NTU), Bo An (NTU), Mikael Johasson (KTH)
Interactive training of deep networks for vision-based autonomous systems. Reproducible machine learning of navigation and path following in autonomous vehicles such as cars and drones
Generate learning data in an interactive, dynamic, and adaptive way.
- Virtual paths, i.e., synthetic environments are projected onto the ground.
- Small-scale vehicles drive autonomously in these synthetic scenarios and acquire their training data in a dynamic environment that can adapt to the learning progress.
- Allowing for full control of all environmental parameters, for appropriate
- Generation of training data by photo realistic rendering of virtual images during real navigation and path following
Michael Felsberg (LiU), Kai-Kuang Ma (NTU)
Computing capacity in edge locations and the wireless access are managed separately. However, delivering the full potential of MECs requires that edge locations and wireless networks be managed in concert
To design new resource allocation methods to improve and unify the management of Mobile Edge Clouds (MECs).
- Game theory will be used to analyze the optimal strategy of each player, thus verifying that the goals of the devised mechanisms are met.
- A player interaction protocol will devised and validated using simulations.
- Development of mathematical models and algorithms to efficiently solve the network and computing resource allocation for MECs.
Erik Elmroth (UmU), Dusit Niyato (NTU)
Improved algorithms for sensor fusion, localization and coordination for search and rescue operations and surveillance applications
Centralized and distributed sensor fusion for target localization, reliable relative localization and collaborative control and task planning of the agents
- Environmental modelling of stationary and moving targets.
- Centralized and distributed fusion estimation methods for multiple robots.
- Reliable relative localization in GPS denied environments.
- Distributed optimization and hybrid control from local temporal logic specifications for multi-robot systems
Fredrik Gustafsson (LiU), Dimos Dimarogonas (KTH), Hu Guoqiang (NTU)
Develop advanced techniques and tools for visualizing different aspects of machine learning jointly in the same framework.
Visualization of the distributions of the input data with effective filters, development of new data structures for handling high dimensional and heterogeneous data and networks, and investigation of how to present and analyse the results with respect to the input data.
- Our approach is to supply a holistic view of a machine learning system and provide interactive visualization that facilitates human involvement and various stages of machine learning.
- We start with deep learning and computer vision tasks as the initial application domain in the area of facial reconstruction.
- We combine new and traditional data visualization methods and will develop a project demonstrator.
Anders Ynnerman (LiU), Jianmin Zheng (NTU)