I have been working in applied mathematics and physics for more than ten years. I have acquired this background through my experience in research fields such as, for example, astrophysics, signal processing and parameter estimation methods for time series. My programming experience spans algorithm development and test, and implementing numerical methods using physics principles in different programming languages as, for instance, C, C++, Fortran, Matlab and Maple.
· C language: Since 1998 I have acquired knowledge and experience with C through some Bachelor and PhD degree subjects and research (in Physics and Engineering).
· Fortran77: From 2000 until 2008 I have worked with Fortran 77 for numerical computation in order to solve differential equations, linear equation systems, matrix inversion problems and apply Bayesian statistics for modeling regression.
· Bash: In order to install and use different software in different computers and clusters, I have got experience with Bash scripts.
· Matlab: Since 2009 I also work with Matlab for modeling regression, numerical calculus, Bayesian statistics, MCMC simulations, data analysis and matrix calculus. Namely, I have developed and used algorithms for estimating parameters with Monte Carlo Markov Chain methods, linear regression
modeling, spectral analysis of stochastic time series, matrix inversion, several matrix calculus algorithms such as, for example, Cholesky and iterative algorithms (e.g. conjugate gradient) and random number generators.
· Others: Latex, Word, Excel, Power-Point, Maple.
· OS: Linux (since 2000) and Windows (since 1995).
Senior Researcher, University of Luxembourg
· I have worked on parameter estimation in stochastic time series, e.g. GPS position time series. The goal was to simultaneously estimate model and noise parameters, including the spectral index of the spectrum of the noise, which, so far, has been avoided by the geodesy and geophysics communities. With this more comprehensive parameter estimation method (using Bayesian Monte Carlo Markov Chain) uncertainties of estimated parameters are more robust and up to 40% times larger than in previous works. This research ended up with two publications in peer-reviewed journals and a thesis (PhD in Engineering).
· I have worked as co-responsible of homework supervision of Lectures in Statistics in the Bachelor degree in Civil Engineering, and as supporting professor of Physics Laboratory in the Bachelor degree in Life Sciences.
Supporting Professor, Universitat Politècnica de Catalunya
· Responsible for the module applied in mathematics of the first course in the Bachelor degree in Economics.
Teaching Assistant and Researcher, Universitat Autònoma de Barcelona
· Taught courses at various levels in: general physics, linear algebra, real analysis, computational methods for physics (C language).
· In 2007 I defended a thesis in Physics which was recognized by the University with an Excellency Award, prize given by the University to fewer than 20% of all theses graded with the highest mark 'Cum Laude'.
· During this period I gave some talks and presented results of my research in different international conferences and seminars.
Doctorate in Engineering, University of Luxembourg
Thesis: “Analysis and parameter estimation of geophysical time series by means of Monte Carlo Markov Chains”
The main goal was to develop a Monte Carlo Markov Chains (MCMC) method that simultaneously estimates all parameters of GPS position time series.
With this more comprehensive parameter estimation MCMC method, uncertainties of estimated parameters, e.g. the velocity, are more robust and up to 40% times larger than methods that do not estimate the uncertainty of the esti mated spectral index.
For shorter time series and time series with low levels of noise it was shown that the white noise amplitude estimated with the MCMC method was more robust and less biased than with the Maximum Likelihood Estimation method.
Numerical methods used included: Bayesian statistics for data analysis, Monte Carlo Markov Chain simulation, Linear Algebra, Matrix Inversion, Time Series Analysis.
Software was developed using: Matlab, C, Shell script and OS Linux.
Doctorate in Physics, Universitat Autònoma de Barcelona
Thesis: “Constraining interacting cosmological models with observational data”, recognized by the University with an Excellency Award, prize given by the University to fewer than 20% of all theses graded with the highest mark 'Cum Laude'.
WMAP, SDSS, 2dF and Supernova Ia observational data were used, by means of Bayesian Statistics analysis and Monte Carlo Markov Chains simulations, to constrain the cosmological parameters of theoretical models.
Numerical methods used included: Runge-Kutta method to solve differential equations, numerical integration methods (Monte Carlo, Romberg), interpolation methods (splines), Bayesian statistics for data analysis, Monte Carlo Markov chain simulation.
The main goal was to study the effects of dark matter-dark energy interaction on the cosmological expansion rate, Cosmic Microwave Background perturbations spectrum, Integrated Sachs Wolfe effect, and Large Scale Structure distribution.
Software was developed using: FORTRAN 77, C, C++, Maple, Shell script, IDL and OS Linux.
Master Degree in Space Science and Technology, Universitat Politècnica de Catalunya
Applicable courses covered: : Astrodynamics, Radionavigation, Analog and Digital Signal Processing, Broadening of Fundamentals in Aerospace Science and Technology, Numerical Methods for Aerospace Engineering Systems (embracing the study of general algorithms – regula Falsi, Newton-Raphson – and interpolation schemes - splines -, plus methods to solve ordinary and partial differential equations – Runge-Kutta, Finite Element Method), Space Systems Engineering, Test and Instrumentation Systems in Aerospace Applications.
Thesis: “Selection of first principle constraints to improve the ionospheric tomography with GNSS” (in progress)
Bachelor and Master Degrees in Physics, Universitat Autònoma de Barcelona
Applicable courses covered: General Physics (120 hours), Physics Laboratory (Mechanics, Electromagnetism, Thermodynamics – 300 hours) Classical Mechanics (180 hours), General Relativity (60 hours), Optics (120 hours), Quantum Optics (60 hours), Optics Laboratory (120 hours), Electronics (120 hours).
Spanish (mother tongue)
Catalan (mother tongue)
Olivares, G., Teferle, F.N. , “A Monte Carlo Markov Chain Method for Parameter Estimation of Fractional Differenced Gaussian Processes”, IEEE Transactions on Signal Processing, vol. 61, no. 9, pp. 2405-2412, (2013). Article with 4 cites.
Gazeaux, J., Williams, S., Matt, K., Bos, M., Dach, R., Deo, M., Moore, A.W., Ostini, L., Petrie, E., Roggero, M., Teferle, F.N., Olivares, G., Webb, F.H., “Detecting offsets in GPS time series: First results from the detection of offsets in GPS experiment”, Journal of Geophysical Research: Solid Earth, vol. 118, pp. 111, (2013).
Olivares, G., Atrio-Barandela, F., Pavón, D., “The Integrated Sachs-Wolfe Effect in Interacting Dark Energy Models”, Phys.Rev. D77, 103520, (2008). Article with 16 cites.
Olivares, G., Atrio-Barandela, F., Pavón, D., “Dynamics of Interacting Quintessence Models: Observational Constraints”, Phys.Rev. D77, 063513, (2008). Article with 39 cites.
Olivares, G., Atrio-Barandela, F., Pavón, D., “Constraining dark energy interacting models with WMAP”, AIP Proceedings of the XXVIII Spanish Relativity Meeting. Pp. 550-553, (2006), ISBN 0-7354-0333-3.
Olivares, G., Atrio-Barandela, F., Pavón, D., “Matter density perturbations in interacting quintessence models”, Phys.Rev. D74, 043521, (2006). Article with 63 cites.
Campo, S. del, Herrera, R., Olivares, G., Pavón, D., “Interacting models of soft coincidence”, Phys.Rev. D74, 023501, (2006). Article with 36 cites.
Olivares, G., Atrio-Barandela, F., Pavón, D., “Observational constraints on interacting quintessence models”, Phys. Rev. D71, 063523, (2005). Article with 89 cites.
Olivares, G., Teferle, F.N., “A Comparison of Monte Carlo Markov Chain and Maximum Likelihood Estimation Methods for the Statistical Analysis of Geodetic Time Series”, AGU Fall Meeting 2013, San Francisco, USA, 9-13 December 2013 (Poster).
Olivares, G., Teferle, F.N., “A Bayesian Monte Carlo Markov Chain Method for the Statistical Analysis of Geodetic Time Series, IAG 2013, Potsdam, Germany, 01-06 September 2013 (Oral).
Olivares, G., Teferle, F.N., “A Bayesian Monte Carlo Markov Chain Method for the Analysis of GPS Position Time Series”, EGU General Assembly 2013, Vienna, Austria, 07-12 April 2013 (Oral).
Olivares, G., Atrio-Barandela, F., Pavón, D., “Interacting Quintessence Models”, Ibéricos 2008. IIIrd Iberian Cosmology Meeting, Lisboa, Portugal., 6-7 March 2008 (Oral).
Olivares, G., Atrio-Barandela, F., Pavón, D., “The Coincidence Problem in Cosmology”, XXX Spanish Relativity Meeting (E.R.E. 2007). EAS Publication Series, Volume 30, 2008. Spanish Relativity Meeting. Encuentros Relativistas Españoles ERE2007. Relativistic Astrophysics and Cosmology. Edited A. Oscoz, E. Mediavilla, M. Serra-Ricart. EDP Sciences (Les Ulis, Francia). ISBN 978-2-7598-0062-9, e-ISBN 1638-1963, Puerto de la Cruz, Tenerife, Spain, 10-14 September 2007 (Oral).
Olivares, G., Atrio-Barandela, F., Pavón, D., “Observational tests for constraining Interacting Quintessence Models”, Seminar of the Dept. of Astronomy and Astrophysics, University of Pennsylvania, Philadelphia, USA, September 2006 (Oral).