Vagelis Harmandaris

Vagelis Harmandaris

Vagelis Harmandaris is Professor at the Department of Mathematics and Applied Mathematics, University of Crete, and affiliated researcher at the Institute of Applied and Computational Mathematics, at the Foundation for Research and Technology – Hellas (IACM/FORTH). He is also Professor and ERA-Chair at “The Cyprus Institute” (CyI) in “Modelling and Simulation for Engineering Applications”. He got his diploma and PhD from the Chemical Engineering department in the University of Patras, Greece. Then, he was post-doctoral fellow and research associate at the Max Planck Institute for Polymer Research, Mainz, Germany. In 2009 he joined, as Assistant Professor, the Department of Applied Mathematics, University of Crete, where he is now full Professor. Since 2010 he is also collaborating researcher in the “Institute of Applied and Computational Mathematics”, Foundation for Research and Technology – Hellas.
His research interests concern on the development of mathematical and computational multi-scale methodologies for complex molecular systems and materials of scientific and technological interest. He leads the group of “Mathematical and Computational Modeling of Complex Molecular Systems” (MACOMMS) at the University of Crete/FORTH, and the SimEA group at the CyI.
He has published more than 125 papers in refereed journals, 4 in books, more than 30 in refereed conference proceedings, and about 80 in non-refereed conference proceedings. He has given more than 150 (about 80 invited) presentations at international conferences and academic and industrial institutions. His h-index is 37 in Scopus and 42 in Google Scholar. He has been a Reviewer for a large number of International Journals, for the European Union, and for various institutions among which the National Science Foundation (USA), the EU HORIZON, the Israeli Science Foundation (ISF), the National Research Council of Romania, and the Greek General Secretariat for Research and Technology. He has also organized and co-organized about 30 International workshops and conferences.
Vagelis
Harmandaris
...
Department of Mathematics and Applied Mathematics, University of Crete, GR-70013, Voutes, Heraklion, Greece
+302810393735
A. F. Behbahani, et al “Dynamics and Rheology of Polymer Melts via Hierarchical Atomistic, Coarse-grained, and Slip-spring Simulations” Macromolecules, 2021, 54, 6, 2740–2762, http://dx.doi.org/10.1021/acs.macromol.0c02583 (Chosen as back-cover).

E. Gkolfi, P. Bačová, and V. Harmandaris, “Size and shape characteristics of polystyrene and poly(ethylene oxide) star-shaped melts studied by atomistic simulations”, Macrom. Theor. Simul., 2021, 30(1), 2000067, https://doi.org/10.1002/mats.202000067 (Chosen as front-cover).

T. Jin, A. Chazirakis, E. Kalligiannaki, V. Harmandaris, M. Katsoulakis, “Data-driven uncertainty quantification for systematic coarse-grained models” Soft Materials, 2020, 18:2-3, 348-368, https://doi.org/10.1080/1539445X.2020.1765803

P. Bačová, E. Glynos, S. Anastasiadis and V. Harmandaris, “Nanostructuring single-molecule polymeric nanoparticles via macromolecular architecture host”, ACS Nano, 2019, 13, 2, 2439-2449, https://doi.org/10.1021/acsnano.8b09374

E . Kalligiannaki, A. Chazirakis, A. Tsourtis, M. Katsoulakis, P. Plechac, V. Harmandaris, “Parametrizing coarse grained models for molecular systems at equilibrium”, Europ. Phys. J. Special Topics, 2016, 225, 1347–1372. doi: http://dx.doi.org/10.1140/epjst/e2016-60145-x
Mathematical Modeling and Scientific Machine Learning: Mathematical/Big-data coarse-graining, stochastic processes, Bayesian statistics, applied probability, optimization algorithms, non-equilibrium methods, information metrics, statistical methods for big data analysis, and inverse problems;

Computational Sciences & Engineering: Modeling, High Performance Computing and large-scale Simulations of physical systems with important technological applications;

Multi-scale simulations and structure-property relations for complex materials, including polymers, biomolecular systems, and multi-phase polymer nanocomposites;

Statistical mechanics-based approaches for quantum, microscopic, and mesoscopic simulations;

Computational methods for developing coarse-grained models and for re-introducing atomistic detail;