WISENET Collaborations
and Co-creation

The Center also interacts and collaborates in actual research projects with different types of actors at the regional, national and international levels, with a clear mentality of co-creation, including several institutions from Europe and United States, as well as institutions from Asia, such as the Indian Institute of Science – IISc.

The following Figure shows, respectively, the international, national and regional institutions that collaborate with WISENET in several funded research projects.

These external institutions include both public and private institutions, cooperating with the WISENET Lab in several ways: a) research visits, b) co-supervision of PhD students, c) actual work in funded research projects where they participate together with the WISENET Center, d) advising the Lab in terms of future directions.  They also cover expertise in the three fronts where the Center operates, namely, Theory, Algorithms and Applications.

In addition other synergies are being built also in the contexts of e-health (future I4Health Institute), Future Robotics, Digital Factories, EYDE, as well as the School of Business and Law, or the Faculty of Health and Sport Sciences, at UiA.

The members of the WISENET Center have collaborated and continue collaborating with international institutions from Europe, United States and Asia. The main external collaborators are the following ones (only paper co-authors and/or project collaborators):

External PhD students/Postdocs

  • Greg Tzagkatakis, Dept. of Computer Science, Signal Processing Laboratory, FORTH-ICS, University of Crete. Project: Matrix completion, low-rank data modeling and sparse sampling.
  • Xabier Insausti, Electronics and Communications Department, CEIT, Universidad de Navarra. Project: Gossip Algorithms for Subspace Projection using Computational Codes in Wireless Sensor Networks.
  • Georgios Tzagarakis, Dept. of Computer Science, Telecommunications and Networks Laboratory, University of Crete. Project: Rotation-Invariant Image Classification based on sub-gaussian probabilistic modeling.