Alexandra Koulouri

I hold a PhD degree in EEG Brain Imaging and Tomographic Imaging from Imperial College London, Dept. of Electrical and Electronic Engineering, UK. 

I am interested in new collaborations!

I really enjoy diving into new topics or expanding my horizons in topics related to neuroimaging, microscopy and climate or weather forecasting.

Please contact me either at alexandra.koulouri [at] tuni.fi or my personal email a.koulouri84 [at] gmail.com 

Currently, I am a post-doctoral Academy researcher. 

My research work is titled ''Superresolution in inverse problems with applications in NeuroImaging and Fluoresent Microscopy''.

I am interested in problems related to remote sensing, extraction information using limited data, real time tracking, data assimilation and forecasting (e.g. using Kalman and Bayesian filtering), convex/non-convex optimization algorithms using sparsity constraints, adaptive mesh approaches and uncertainty modelling in inverse problems.

The applications I am involved at the moment, are related to 

• (focal) brain source imaging using non-invasive EEG recording
• estimation of the skull conductivity and brain activity combining EEG and Electrical Impedance Tomography
• detection and visualization of single molecules in fluoresence microscopy, tracking of particles in microscopy
• dynamic imaging (an intereting application that I tried recently using my knowledge: imaging ionospheric S4 scintillation for GNSS users).

My expertise is in Bayesian inverse problems, optimization techniques, tomographic methods, imaging processing, super-resolution algorithms and machine learning approaches.

Academy of Finland, Post-doc research project, 2018-2021 (project brief description)

ATTRACT consortium, EC Horizon 2014-2020, 2019 – 2020 (project temporary webpage)

IKY Fellowship of excellence for postgraduate studies in Greece - Siemens program, 2016-2017

PhD grant by John S. Latsis Public Benefit Foundation, 2010-2013 (3 years)

Studentship EPSRC, UCL, 2008-2009

Studentship by John S. Latsis Public Benefit Foundation, 2008-2009

Participate in HYPERMATH project (2014-2016) funded by German BMBF for the development super-resolution algorithms in microscopy and Raman spectroscopy in Muenster University

Currently: Academy PostDoc Researcher

Previously: 

• Researcher in the Ionospheric imaging group, EEE Dept., University of Bath, May 2018- Oct. 2018

• Research fellow in the group of Bioelectromagentism, School of Physics, Aristotle University of Thessaloniki, Greece, Nov. 2016 – Oct. 2017

• Teacher in the Master Programme of Bioinformatics and Neuroinformatics, Ionian University, Dept. of Informatics, 7 Pl. Tsirigoti, 49100, Greece, Oct. 2016 – Jan. 2017

• Researcher, 1 Oct. 2014 – 31 July 2016, Imaging group, Institute for Computational and Applied Mathematics, University of Münster. Collaboration with the reproductive clinic of the University of Münster (super-resolution imaging strategies in Raman spectroscopy for DNA detection anomalies)

• PhD researcher, 2010 – 2014, Comm. And Sig. Proc. group, EEE department, Imperial College London, PhD studies and research on algorithms in electrical brain imaging

• A. Koulouri, P. Heins and M. Burger, Adaptive Superresolution in Deconvolution of Sparse Peaks, in IEEE Transactions on Signal Processing, vol. 69, pp. 165-178, 2021, doi: 10.1109/TSP.2020.3037373. (Matlab Codes) 

• V. Rimpiläinen, T. Samaras, A. Koulouri, Electrical Impedance Tomography with Box Constraint for Skull Conductivity Estimation. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_54 (arXiv:2001.11830)

• A. Koulouri, V. Rimpiläinen, Simultaneous Skull Conductivity and Focal Source Imaging from EEG Recordings with the Help of Bayesian Uncertainty Modelling. EMBEC 2020. IFMBE Proceedings, vol 80. Springer, Cham. https://doi.org/10.1007/978-3-030-64610-3_114 (arXiv:2002.00066), Finalist in the Young Investigator Competition 

• A. Rezaei, A. Koulouri & S. Pursiainen Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth. Brain Topogr 33, 161–175 (2020). https://doi.org/10.1007/s10548-020-00755-8 

• A. Koulouri, N. Smith, B. Vani, V. Rimpiläinen, A. Astin and B. Forte Methodology to estimate ionospheric scintillation risk maps and their contribution to position dilution of precision on the ground, J Geod 94, 22 (2020). https://doi.org/10.1007/s00190-020-01344-0 (Matlab Codes) 

•A. Koulouri, V. Rimpiläinen and N. Smith, Position Dilution of Precision and Bayesian Model of the Observation Error (2020arXiv:2001.02198)

• V. Rimpiläinen, A. Koulouri, F. Lucka, J.P. Kaipio, C.H. Wolters, Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity, NeuroImage, 188, 252-256, 2019. https://doi.org/10.1016/j.neuroimage.2018.11.058 

• A. Koulouri, V. Rimpiläinen, M. Brookes. J.P. Kaipio, Prior Variances and Depth Un-biased Estimators in EEG Focal source Imaging, EMBEC & NBC 2017, International Federation for Medical and Biological Engineering (IFMBE) Proceedings, 65, 33-36, 2017 (arXiv version :1703.09044) 

• V. Rimpiläinen, A. Koulouri, F. Lucka, J.P. Kaipio, C.H. Wolters, Bayesian Modelling of Skull Conductivity Uncertainties in EEG Source Imaging, EMBEC & NBC 2017, International Federation for Medical and Biological Engineering (IFMBE) Proceedings, 65, 892-895, 2017 (arXiv:1703.0903 )

• A. Koulouri, M. Brookes and V. Rimpilainen. Vector tomography for reconstructing electric field with non-zero divergence in bounded domains, Journal of Computational Physic ,Vol. 329, 15 January 2017, Pages 73–90. https://doi.org/10.1016/j.jcp.2016.10.037

• A. Koulouri, V. Rimpilainen, M. Brookes and J. P. Kaipio. Compensation of domain modelling errors in the inverse source problem of the Poisson equation: application in electroencephalographic imaging, Applied Numerical Mathematics, Vol. 106, Aug. 2016, P. 24-36. https://doi.org/10.1016/j.apnum.2016.01.005 

• A. Koulouri and M. Petrou: Vector Field Tomography: Reconstruction of an Irrotational Field in the Discrete Domain, Proceeding (778) Signal Processing, Pattern Recognition and Applications, 2012, DOI: 10.2316/P.2012.778-021

• Automatic segmentation of the abdominal Aorta from CT images: an initial approach towards the aortic Aneurysm detection. Authors: Alexandra Koulouri, Prof. Maria Petrou. Publisher: LAP LAMBERT Academic Publishing (22 May 2011).

Older thesis in CT segmentation:

Automatic segmentation of the thoracic organs for image registration and RT planning, UCL, October 2009, doi: 10.13140/RG.2.1.3136.1526 

Automatic Segmentation and 3D Reconstruction of the Abdominal Aorta from CT images, Imperial College, September 2008: full text