Skilled data scientist, statistician and machine learner with a broad applied experience and deep mathematical expertise.  Demonstrated interdisciplinary practice in fields:

• Materials Science

• Physics

• Chemistry

• Metrology, method development and validation, GUM uncertainty methodology

• Laboratory quality control and quality assurance according to international standards (e.g. ISO)

• Managing, analyzing, and reporting the results of interlaboratory comparisons and proficiency tests

• Remote Sensing and image analysis

• Nuclear engineering, international safeguards, and various applications

Versatile data-science programming capabilities:

• High level proficiency in languages: Julia, R (base and tidyverse), MATLAB, Mathematica, Sage, LaTex, R-markdown

• Basic proficiency in: SAS, Minitab, Python

• Proficiency in all Microsoft office and google office products

Deep knowledge and proficiency with the following statistics and machine learning sub-disciplines:

• Response Surface Methodology (product and process optimization via designed experiments)

• Design of Experiments (DoE). Well versed in classical (catalogue) designs, optimal DoE in the hypercube, hypersphere, and standard n-simplex (i.e. mixture experiments).

• Statistical quality and process control, including capability and tolerance analysis

• Advanced mathematical-statistics training, advanced matrix algebra (e.g. vector spaces and projection operators), advanced matrix calculus (e.g. via Kronecker product), advanced inference theorems

• Statistical modeling and inference including: linear and non-linear modeling and analysis, generalized linear models, mixed-effects models, and general likelihood based inferential procedures.

• Statistical sample plan design

• Robust statistical estimation methods, including estimation of Gaussian parameters, robust estimation of linear and mixed-effects model parameters, robust multivariate analysis methods, including characterization of multivariate Gaussian distributions, robust estimation of covariance matrices, and robust K-means type clustering algorithms. (outliers are a disaster to your data analysis, btw ;-) ) 

• Multiple multivariate analysis techniques (e.g. PCA; clustering; factor analysis; MANOVA; discrimination and classification techniques such as LDA/QDA, SVMs, and mixture-distribution modeling)

• Exploratory data analysis and graphical analysis

• Spatial data analysis. General time-series analysis and state-space models

• Bayesian modeling, sampling, inference, and data-analysis; Bayesian model averaging; Bayesian Networks

• Trained in Biostatistics

• Missing data analysis including Expectation-Maximization and Bayesian Data Augmentation

• Multiple optimization techniques and algorithms including: gradient search with boosting, constrained optimization, Newton-Raphson, Fisher Scoring, Gauss-Newton, and of course meta-heuristics including the Particle Swarm Optimization