IESL-FORTH
Published on IESL-FORTH (http://139.91.197.33)


Researcher ID [1]
Orc ID [2]
Scholar ID [3]
Office Phone: (+30) 2810 39 4263
Email: barmparis@iesl.forth.gr
Dr. Barmparis Georgios D.
PostDoctoral Fellow
  • About
  • Selected Publications
  • Research Groups

Education

  • 2012, Ph.D. in Materials Science and Technology, Dept. of Materials Science and Technology, University of Crete, Greece.
  • 2008, M.Sc. in Computational Physics, Dept. of Physics, University of Crete, Greece.
  • 2005, B.Sc. in Physics with major in Computational Physics, Dept. of Physics, University of Crete, Greece.

Career

  • 2023 – present, Research Associate. Institute of Electronic Structure and Laser, Foundation of Research and Technology-Hellas, Greece.
  • 2021 – present, Member of the Collaborating Educational Staff of the Hellenic Open University, Postgraduate program: Data Science and Machine Learning, Greece.
  • 2022 – 2023, Senior Data Scientist - VODA.ai, USA (remotely).
  • 2020 – 2021, Temporal teaching personnel in the frame of the National-EU program Gaining Academic Teaching experience, Department of Physics, University of Crete, Greece.
  • 2019 – 2023, Research Associate. Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Greece.
  • 11/2019 – 02/2020, Visiting researcher. Institute for Advanced Computational Science (IACS), John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University, MA, USA.
  • 2018 – 2019, Research Associate. Institute of Electronic Structure and Laser, Foundation of Research and Technology-Hellas, Greece.
  • 2017 – 2019, Research Associate. Department of Physics, University of Crete, Greece.
  • 2015 – 2016, Research Associate. Crete Center for Quantum Complexity and Nanotechnology. Department of Physics, University of Crete, Greece.
  • 2012 – 2015, Postdoctoral scholar. Department of Physics and Astronomy, Vanderbilt University, TN, USA and Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, TN, USA.

Interests

  • Physics Informed Machine Learning
  • Machine Learning in Complex Systems and Medicine
  • Computational Materials and Condensed Matter Physics
  • Inverse Problems
  • Development of scientific software

Awards/Prizes/Distinctions

  • 2011, “Maria Michail Manassaki" award as the top graduate student of the Department of Materials Science and Technology of the University of Crete, Greece.
  • 2010, Individual HPC-Europa2 grant for a visit in Finland and access to one of Europe’s largest computers (150000 cpu hours).
Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG‐based approach.
Eleni Angelaki, Maria E. Marketou, Georgios D. Barmparis, Alexandros Patrianakos, Panos E. Vardas, Fragiskos Parthenakis, Giorgos P. Tsironis
J Clin Hypertens, Volume:00, Page:1–11, Year:2021, DOI:doi.org/10.1111/jch.14200 [4]
Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach.
Georgios D. Barmparis, Georgios P. Tsironis
Chaos Soliton Fract, Volume:135, Page:109842, Year:2020, DOI:doi.org/10.1016/j.chaos.2020.109842 [5]
Robust prediction of complex spatiotemporal states through machine learning with sparse sensing.
G. D. Barmparis, G. Neofotistos, M. Mattheakis, J. Hizanidis, G. P. Tsironis, E. Kaxiras
Phys. Lett. A, Volume:384, Page:126300, Year:2020, DOI:doi.org/10.1016/j.physleta.2020.126300 [6]
Machine Learning With Observers Predicts Complex Spatiotemporal Behavior.
George Neofotistos, Marios Mattheakis, Georgios D. Barmparis, Johanne Hizanidis, Giorgos P. Tsironis, Efthimios Kaxiras
Front. Phys., Volume:7, Page:24, Year:2019, DOI:doi.org/10.3389/fphy.2019.00024 [7]
  • Nonlinear and Statistical Physics [8]
  • Nonlinear Lithography group [9]

Links
[1] http://www.researcherid.com/rid/V-1899-2018 [2] https://orcid.org/0000-0002-5906-9868 [3] https://scholar.google.com/citations?user=rEKUBXEAAAAJ [4] https://doi.org/10.1111/jch.14200 [5] https://doi.org/10.1016/j.chaos.2020.109842 [6] https://doi.org/10.1016/j.physleta.2020.126300 [7] https://doi.org/10.3389/fphy.2019.00024 [8] http://139.91.197.33/en/research/Nlin_Stat_Phys [9] http://139.91.197.33/en/research/NLL_Group