3 years of experience in conducting Financial, Biological, Geophysical, and Atmospherical data mining, data modeling, data extraction, data preparation,
cleaning, manipulation, variable and model selection, parameter estimation, and model validation using R
Performed advanced level statistical analysis, namely, Stochastic Modeling, Machine Learning:
Principal Component Analysis, Random Forest, Logistic regression,
Dynamic Fourier transform, and error analysis of forecasting the geophysical
seismic data and high-frequency financial data.
Have operational skills in experimental designing, probability and prediction analysis for
univariate, multivariate and exploratory data using R program & Minitab software.
My current researchs are focused on the statistical and computational aspects of geophysical and financial time series analysis.
An important contribution of my work is the establishment of a new perspective to the analysis of data where the measurements of a sequence of
geophysics and finance are assumed to be stochastically dependent on specific time, as observed at discrete time points with measurement error.
This perspective helps to effectively determine the time-varying parameters in order to forecast the time series.
As for finance, I study stock markets data such as daily returns, high frequency returns, and daily closing prices
from emergent as well as developed markets. I am particularly interested in designing some stochastic methods and in showing their
statistical efficiency by using tools from Newton-Raphson algorithm of Maximum Likelihood Estimation.
In geophysics, I study a sequence of mining explosions and a large number of aftershocks of the magnitude M=5.2 earthquake located near
the Clifton, Arizona. Owing to the dynamic behavior of these sequences, I use dynamic Fourier analysis in stationary environment in
order to capture the spectral behavior of the signal as it evolves over time.