Research Interests

Konstantia Georgouli is a computational scientist in the Biosciences & Biotechnology Division’s Biochemical and Biophysical Systems Group. She joined LLNL in 2020 as a postdoctoral researcher and transitioned to a staff position in 2023. Konstantia earned her Bachelor of Science in Computer Science from the International Hellenic University, Greece (formerly Technological Educational Institute of Serres), and a Master of Science in Computer Science from the University of Thessaly, Greece. She received her Ph.D. in Biological Sciences from the Queen's University Belfast, Northern Ireland, UK, in 2018 where she investigated and developed novel 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.

As a postdoc at LLNL, Konstantia was involved in the High-performance Parallel Simulations for Whole-cell Modeling Project, where she contributed to the development and implementation of a general-purpose, high-performance, multi-scale, parallel simulation framework. She also played a key role in designing and implementing a novel machine learning-based capability aimed at generating protein structures, with the ultimate goal of accelerating scientific discovery by aiding in the identification of transition pathways.

Her current research focuses on developing new deep learning techniques to understand the molecular mechanisms underlying signaling pathways of biological processes.

 

Computational Scientist, Oct 2023 - Present, Lawrence Livermore National Laboratory, Livermore, CA

Postdoctoral Researcher, May 2020 - Sep 2023, Lawrence Livermore National Laboratory, Livermore, CA

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 Computer, Informatics and Telecommunications Engineering, International Hellenic University, Greece (formerly Technological Educational Institute of Serres)

Google Scholar page

1. Georgouli, K., Yeom, J.S., Blake, R.C. and Navid, A., 2023. Multi-scale models of whole cells: progress and challenges. Frontiers in Cell and Developmental Biology, 11.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.