doc. RNDr. Denis Horváth, PhD.


MSc. Solid-state physics, Dept. of Experimental physics, Faculty of Science,
UPJŠ, Košice, Slovakia

PhD. (CSc.) Solid-state physics, Dept. of Magnetism, Institute of Experimental Physics,
Slovak Academy of Sciences, Košice, Slovakia


1989–1997 Institute of Experimental Physics, Slovak Academy of Sciences, scientific fellow, senior scientist;

  • stage (1/2 year) Bogoliubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, Dubna, Russian Federation;
  • stage Dept. Physics, Helsinky University, Finland;

1997–2002 Faculty of Natural Science, Šafárik University, Košice, Slovakia, associated professor, lecturing experience;

  • stage Dept. of Solid State Physics, University of Lodz, Poland;
  • stage International Centre for Theoretical Physics, Trieste, Italy;

2008–2009 stage „le STUDIUM“ institute for advanced studies (International research center and hosting foreign associate researchers, organized into a network of European laboratories, based on the Région Centre), CBM (Center de Biophysique Moleculaire Orleans France) CNRS (Centre National de la Recherche Scientifique), group of Theoretical Biophysics, Molecular Simulation, and Scientific Computing;

2009–2011 SORS research joint stock company, Košice, Slovakia, data scientist and analyst;

2011–2012 Technical University of Košice, Faculty of Economics, Dept. Finance, position of scientist and lecturer;

2012–2015 Center of Interdisciplinary bioscience (CIB), UPJŠ Košice, Slovakia

Research interests, expertise relevant to the project:

  • continuous models, newtonian and non-newtonian complex fluids
  • theory of randomly stirred incompressible fluids
  • renormalization group field-theoretical approaches
  • computational and statistical physics
  • discrete media models in solid state (condensed matter) physics
  • mean field theoretical methodologies
  • Monte Carlo simulation technique, adaptive Monte Carlo schemes
  • molecular dynamics technique
  • nonlinear ordinary differential equations solvers and sensitivity analysis for time-dependent systems
  • coarse-grained and multi-scale computational models, nanostructures
  • time-series data statistical analysis, autoregressive models
  • clustering algorithms, distance-based methodologies
  • agent-based and multi-agent simulations
  • neural networks and related tools of artificial intelligence, information processing of system configurations and reconstructed experimental images
  • continuous-time deterministic models based on the ordinary differential equations
  • large-scale models of entropy-controlled hypercycles and biological structures
  • computational models of the growth dynamics of multi-cellular tumor spheroids
  • presently: acquiring theoretical background for biostatistics and sensitivity analysis,

Computational skills:
Linux operation system, elementary Bash-Scripting
C and C++ programming skills
R programming and statistical computing
Mathematica – advanced symbolic computing
Condor distributed computing practice

List of selected publications related to the project specifications and requirements:

topic: renormalization group, models of fluids
Buša J., Hnatich M., Honkonen J., Horváth D.: “Stability of Kolmogorov scaling in anisotropically forced turbulence”, Phys. Rev. E, 55 (1997) 381–394.

topic: molecular dynamics simulation technique
Kašpárková M., Horváth D., “On positional ordering in a 2-dimensional system of parallel dipoles”, J. Phys.D: Appl.Phys. 25 (1992) 1522–1527.

topic: Monte Carlo, tuning of simulation outputs to experiment
Horváth D., Orendáčová A., Orendáč M., Jaščur M., Brutovský B., Feher A.: “Ker(MoO4)_2: A quasi one-dimensional S=3/2 Blume-Capel system”, Phys. Rev. B 60 (1999) 1167–1174.

topic: neural networks, adaptivity and artificial intelligence in nanostructures and solid state physics
Horváth D., Gmitra M., Vávra I.: “Neural network approach to magnetic dot arrays”, J. Magn. Magn. Mater. 231 (2001) 273–286.

topic: multi-scale approach, solid state physics and artificial intelligence
Horváth D., Gmitra M.:“The neural network two-scale model of the magnetic dot array”, J. Magn.Magn.Mater. 263 (2003) 195–213.

topic: inverse problem within the Monte Carlo technique
Horváth D., Gmitra M., Kuscsik Z.: “The self-organized Monte Carlo localization of the critical point via linear filtering”, Czech. J Phys 54 (2004) 921– 926.

topic: multi-scale simulations and Monte Carlo method
Horváth D., Gmitra M.: “The self-organized multi-lattice Monte Carlo simulation”, Int. J. Mod. Phys. C 15 (2004) 1249–1268.

topic: multicanonical Monte Carlo method, coarse-grained simulations
Reitzner D., Horváth D.: “Multicanonical sampling of vortex states in magnetic nanoelements”,
Physica A 379 (2007) 587–594.

topic: biological systems, chemical kinetics, simulations
Horváth D., Kneller G.R.: “A least-constraint principle for population dynamics and reaction kinetics,
Modeling entropy-controlled chemical hypercycles”, J. Chem. Phys., 131:171101, 2009.

topic: inverse problems in the simulations in the biological systems
Horváth D., Brutovský B., Kočišová J., Šprinc S.: “Manipulation with heterogeneity within a species population formulated as an inverse problem”, Physica A, 389 (2010) 5028–5036.

topic: coarse-grained models of biological systems, optimization
Brutovský B., Horváth D.: „The optimization aspects of carcinogenesis“,
Medical Hypotheses (2009), doi:10.1016/ j.mehy.2009.10.019; [see also arXiv:0907.2004v3 (q-bio.PE)].

B. Brutovsky, Horvath D. “Towards inverse modeling of intratumor heterogeneity”, Open Physics, 13 (2015) 232–241.

topic: coarse-grained models of biological systems, inverse approach
Brutovský B., Horváth D., Lisý V.: “Inverse geometric approach for the simulation of close-to-circular growth. The case of multicellular tumor spheroids”, Physica A 387 (2008) 839–850.

topic: data mining, manifold learning, advanced hybrid optimization
Horvath D., Ulicny J., Brutovsky B.: “Self-organised manifold learning and heuristic charting via adaptive metrics”, Connection Science 28 (1), (2016) 1–26.

topic- modeling of compartmental intracellular transport
Verebova V., Belej D., Joniova J. , Jurasekova Z., Miskovsky P., Kozar T., Horvath D. , Stanicova J., Huntosova V.: “Deeper insights into the drug defense of glioma cells against hydrophobic molecules”, International Journal of Pharmaceutics, 503 (1–2) (2016) 56–67.

topic – biostatistics and phenomenological modeling
Misuth M., Joniova J., Belej D., Hrivnak S., Horvath D., Huntosova V.: “Estimation of PKCδ autophosphorylation in U87 MG glioma cells: combination of experimental, conceptual and numerical approaches”, Journal of biophotonics (2016).

topic – advanced biostatistics
Huntosova V., Gerelli E., Horvath D., Wagnieres G.: “Measurement of pO2 by luminescence lifetime spectroscopy: A comparative study of the phototoxicity and sensitivity of [Ru(Phen)3]2+ and PdTCPP in vivo”, Journal of Biophotonics, online : 2 SEP 2016, DOI: 10.1002/jbio.201600127

topic – Monte Carlo simulation of evolutionary phenomena on the level of cellular populations
Horvath D., Brutovsky B.: “Etiology of phenotype switching strategy in time varying stochastic environment, Physica A: Statistical Mechanics and its Applications, 462 (2016) 455–468.

topic – Monte Carlo simulation of evolutionary phenomena on the level of cellular population
Horvath D., Brutovsky B.: “Study of selected phenotype switching strategies in time varying environment”, Physics Letters A, 380, (13) (2016) 1267–1278.