Contact information Lawrence Livermore National Laboratory 7000 East Avenue, L-367 Livermore, CA 94550 email: georgouli1@llnl.gov

Education

Ph.D., Biological Sciences, 2018 
Institute for Global Food Security, School of Biological Sciences, Queen's University Belfast, Northern Ireland, UK

M.S., Science and Technology in Computer and Communication Engineering, 2011
Department of Electrical and Computer Engineering, University of Thessaly, Greece

B.S., Informatics and Communications Engineer, 2005
Department of Informatics Engineering, Technological Educational Institute of Serres, Greece

Research

During my PhD studies, I investigated and developed new chemometric methods for vibrational spectroscopic data in the field of food authenticity as well as introduced new advances in the field of pattern recognition applied to food science problems.  Since joining LLNL in 2020, I have worked on two projects. Firstly, I worked on the development and implementation of a general purpose, high-performance, multi-scale, parallel simulation framework that was optimized to execute a variety of modeling algorithms in biological simulation. Currently, my research focuses on developing new deep learning techniques for identifying protein conformations and lipid fingerprints.

Publications

        Google Scholar page

1. Georgouli, K., Ingólfsson, H.I., Aydin, F., Heimann, M., Lightstone, F.C., Bremer, P.T. and Bhatia, H., 2022. Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints. arXiv preprint arXiv:2207.04333.
2. Aydin, F., Georgouli, K., Dharuman, G., Glosli, J.N., Lightstone, F.C., Ingólfsson, H.I., Bremer, P.T. and Bhatia, H., 2022. Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning. arXiv preprint arXiv:2207.06630.
3. Yeom, J.S., Georgouli, K., Blake, R. and Navid, A., 2021, August. Towards dynamic simulation of a whole cell model. In Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 1-10).
4. Georgouli, K., Carrasco, B., Vincke, D., Del Rincon, J.M., Koidis, A., Baeten, V. and Pierna, J.A.F., 2020. Continuous statistical modelling in characterisation of complex hydrocolloid mixtures using near infrared spectroscopy. Chemometrics and Intelligent Laboratory Systems, 196, p.103910.
5. Monteiro, P.I., Santos, J.S., Brizola, V.R.A., Deolindo, C.T.P., Koot, A., Boerrigter-Eenling, R., van Ruth, S., Georgouli, K., Koidis, A. and Granato, D., 2018. Comparison between proton transfer reaction mass spectrometry and near infrared spectroscopy for the authentication of Brazilian coffee: A preliminary chemometric study. Food Control, 91, pp.276-283.
6. Georgouli, K., Osorio, M.T., Martinez Del Rincon, J. and Koidis, A., 2018. Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement. Journal of Chemometrics, 32(6), p.e3004.
7. Diaz-Chito, K., Georgouli, K., Koidis, A. and del Rincon, J.M., 2017. Incremental model learning for spectroscopy-based food analysis. Chemometrics and Intelligent Laboratory Systems, 167, pp.123-131.
8. Georgouli, K., Del Rincon, J.M. and Koidis, A., 2017. Continuous statistical modelling for rapid detection of adulteration of extra virgin olive oil using mid infrared and Raman spectroscopic data. Food Chemistry, 217, pp.735-742.