Address

Chair of Computational Science
Clausiusstrasse 33
ETH-Zentrum, CLT E 12
CH-8092 Zürich

Contact Information

Email:

Georgios Arampatzis

Post-doctoral Fellow

Research and Interests

  • Bayesian Uncertainty Quantification
  • Hierarchical Bayesian Statistics
  • Optimal Sensor Placement

Education

  • Ph.D. Applied Mathematics, University of Crete, Greece. 2014
  • M.S. Applied Mathematics, University of Crete, Greece. 2011

2019

  • W. Byeon, M. Domínguez-Rodrigo, G. Arampatzis, E. Baquedano, J. Yravedra, M. A. Maté-González, and P. Koumoutsakos, “Automated identification and deep classification of cut marks on bones and its paleoanthropological implications,” Journal of computational science, vol. 32, pp. 36-43, 2019.
    [BibTeX] [Abstract] [PDF] [DOI]

    The identification of cut marks and other bone surface modifications (BSM) provides evidence for the emergence of meat-eating in human evolution. This most crucial part of taphonomic analysis of the archaeological human record has been controversial due to highly subjective interpretations of BSM. Here, we use a sample of 79 trampling and cut marks to compare the accuracy in mark identification on bones by human experts and computer trained algorithms. We demonstrate that deep convolutional neural networks (DCNN) and support vector machines (SVM) can recognize marks with accuracy that far exceeds that of human experts. Automated recognition and analysis of BSM using DCNN can achieve an accuracy of 91% of correct identification of cut and trampling marks versus a much lower accuracy rate (63%) obtained by trained human experts. This success underscores the capability of machine learning algorithms to help resolve controversies in taphonomic research and, more specifically, in the study of bone surface modifications. We envision that the proposed methods can help resolve on-going controversies on the earliest human meat-eating behaviors in Africa and other issues such as the earliest occupation of America.

    @article{byeon2019a,
    author = {Wonmin Byeon and Manuel Dom{\'{\i}}nguez-Rodrigo and Georgios Arampatzis and Enrique Baquedano and Jos{\'{e}} Yravedra and Miguel Angel Mat{\'{e}}-Gonz{\'{a}}lez and Petros Koumoutsakos},
    doi = {10.1016/j.jocs.2019.02.005},
    issn = {1877-7503},
    journal = {Journal of Computational Science},
    pages = {36 - 43},
    title = {Automated identification and deep classification of cut marks on bones and its paleoanthropological implications},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/byeon2019a.pdf},
    volume = {32},
    year = {2019}
    }

  • J. Zavadlav, G. Arampatzis, and P. Koumoutsakos, “Bayesian selection for coarse-grained models of liquid water,” Scientific reports, vol. 9, iss. 1, 2019.
    [BibTeX] [PDF] [DOI]
    @article{zavadlav2019a,
    author = {Julija Zavadlav and Georgios Arampatzis and Petros Koumoutsakos},
    doi = {10.1038/s41598-018-37471-0},
    journal = {Scientific Reports},
    month = {jan},
    number = {1},
    publisher = {Springer Nature},
    title = {Bayesian selection for coarse-grained models of liquid water},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/zavadlav2019a.pdf},
    volume = {9},
    year = {2019}
    }

  • G. Arampatzis, D. Wälchli, P. Weber, H. Rästas, and P. Koumoutsakos, “(μ,Λ)-ccma-es for constrained optimization with an application in pharmacodynamics,” in Proceedings of the platform for advanced scientific computing conference on – PASC ’19, 2019.
    [BibTeX] [PDF] [DOI]
    @inproceedings{arampatzis2019a,
    author = {Arampatzis, Georgios and W\"{a}lchli, Daniel and Weber, Pascal and R\"{a}stas, Henri and Koumoutsakos, Petros},
    booktitle = {Proceedings of the Platform for Advanced Scientific Computing Conference on - {PASC} {\textquotesingle}19},
    doi = {10.1145/3324989.3325725},
    keywords = {Stochastic optimization, constraint handling, covariance matrix adaptation, evolution strategy, pharmacodynamics, viability evolution},
    publisher = {{ACM} Press},
    title = {(\Μ,{\Lambda})-CCMA-ES for Constrained Optimization with an Application in Pharmacodynamics},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/arampatzis2019a.pdf},
    year = {2019}
    }

2018

  • G. Arampatzis, D. Wälchli, P. Angelikopoulos, S. Wu, P. Hadjidoukas, and P. Koumoutsakos, “Langevin diffusion for population based sampling with an application in bayesian inference for pharmacodynamics,” SIAM journal on scientific computing, vol. 40, iss. 3, p. B788–B811, 2018.
    [BibTeX] [PDF] [DOI]
    @article{arampatzis2018a,
    author = {Georgios Arampatzis and Daniel W\"alchli and Panagiotis Angelikopoulos and Stephen Wu and Panagiotis Hadjidoukas and Petros Koumoutsakos},
    doi = {10.1137/16m1107401},
    journal = {{SIAM} Journal on Scientific Computing},
    month = {jan},
    number = {3},
    pages = {B788--B811},
    publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
    title = {Langevin Diffusion for Population Based Sampling with an Application in Bayesian Inference for Pharmacodynamics},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/arampatzis2018a.pdf},
    volume = {40},
    year = {2018}
    }

  • J. Lipková, G. Arampatzis, P. Chatelain, B. Menze, and P. Koumoutsakos, “S-leaping: an adaptive, accelerated stochastic simulation algorithm, bridging τ-leaping and r-leaping,” Bulletin of mathematical biology, 2018.
    [BibTeX] [PDF] [DOI]
    @article{lipkova2018a,
    author = {Jana Lipkov{\'{a}} and Georgios Arampatzis and Philippe Chatelain and Bjoern Menze and Petros Koumoutsakos},
    doi = {10.1007/s11538-018-0464-9},
    journal = {Bulletin of Mathematical Biology},
    month = {jul},
    publisher = {Springer Nature},
    title = {S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging \tau-Leaping and R-Leaping},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/lipkova2018a.pdf},
    year = {2018}
    }

2017

  • L. Kulakova, G. Arampatzis, P. Angelikopoulos, P. Hadjidoukas, C. Papadimitriou, and P. Koumoutsakos, “Data driven inference for the repulsive exponent of the Lennard-Jones potential in molecular dynamics simulations,” Scientific reports, vol. 7, iss. 1, p. 16576, 2017.
    [BibTeX] [Abstract] [PDF] [DOI]

    The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The LJ potential models atomistic attraction and repulsion with century old prescribed parameters (q=6, p=12 respectively), originally related by a factor of two for simplicity of calculations. We propose the inference of the repulsion exponent through Hierarchical Bayesian uncertainty quantification We use experimental data of the radial distribution function and dimer interaction energies from quantum mechanics simulations. We find that the repulsion exponent p\approx6.5 provides an excellent fit for the experimental data of liquid argon, for a range of thermodynamic conditions, as well as for saturated argon vapour. Calibration using the quantum simulation data did not provide a good fit in these cases. However, values p\approx12.7 obtained by dimer quantum simulations are preferred for the argon gas while lower values are promoted by experimental data. These results show that the proposed LJ 6-p potential applies to a wider range of thermodynamic conditions, than the classical LJ 6-12 potential. We suggest that calibration of the repulsive exponent in the LJ potential widens the range of applicability and accuracy of MD simulations.

    @article{kulakova2017a,
    author = {Kulakova, Lina and Arampatzis, Georgios and Angelikopoulos, Panagiotis and Hadjidoukas, Panagiotis and Papadimitriou, Costas and Koumoutsakos, Petros},
    doi = {10.1038/s41598-017-16314-4},
    issn = {2045-2322},
    journal = {Scientific Reports},
    number = {1},
    pages = {16576},
    title = {Data driven inference for the repulsive exponent of the {L}ennard-{J}ones potential in molecular dynamics simulations},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/kulakova2017a.pdf},
    volume = {7},
    year = {2017}
    }

  • B. Mosimann, G. Arampatzis, S. Amylidi-Mohr, A. Bessire, M. Spinelli, P. Koumoutsakos, D. Surbek, and L. Raio, “Reference ranges for fetal atrioventricular and ventriculoatrial time intervals and their ratios during normal pregnancy,” Fetal diagnosis and therapy, 2017.
    [BibTeX] [PDF] [DOI]
    @article{mosimann2017a,
    author = {Beatrice Mosimann and Georgios Arampatzis and Sofia Amylidi-Mohr and Anice Bessire and Marialuigia Spinelli and Petros Koumoutsakos and Daniel Surbek and Luigi Raio},
    doi = {10.1159/000481349},
    journal = {Fetal Diagnosis and Therapy},
    month = {oct},
    publisher = {S. Karger {AG}},
    title = {Reference Ranges for Fetal Atrioventricular and Ventriculoatrial Time Intervals and Their Ratios during Normal Pregnancy},
    url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/mosimann2017a.pdf},
    year = {2017}
    }