Technical Skills

Machine Learning: neural network classification, clustering, dimensions reduction

Statistical Methods: regression models, Bayesian classification, hypothesis testing, confidence intervals

Mathematical Methods: gradient descent, Lagrangian optimization, interpolation, euler's method, and iterative methods to matrix equations

Software and Programming Languages:

• Matlab [4+ years experience] - Algorithms I've written in my thesis are in Matlab. In addition, I've used Matlab toolboxes, such as the image processing toolbox. 

• LaTeX [4+ years experience] - I wrote my entire dissertation in LaTeX; papers I've submitted or prepared are also in this language; and my presentations are in Beamer.

• R Programming [4+ years experience] - I integrate R-basics into my courses (Introduction to Statistics, Introduction to Data Science, and Machine Learning & Neural Networks).  I am comfortable with reading data into R, manipulating data, analyzing data via statistical techniques.  That is,

  • Read data from SPSS, STATA, Excel

  • Read data delimited by tabs, commas

  • Analyze data using five-number summary, t-tests, ANOVA

  • Analyze data using Machine Learning techniques such as Neural Networks, K-means, Linear Regression

  • Visualize data with standard histograms, stemplots, boxplots, scatterplots; with ggplot2 package

  • Create formal reports using R Markdown

• Python [4+ years experience] - I integrated Python into my courses (Linear Algebra and Machine Learning & Neural Networks).  I am comfortable with the numpy, scipy, sympy, scikit-learn, pandas, and keras packages.  I have experience with object-oriented programming in Python.

Miscellaneous: Java, C, Julia, CUDA C, HTML, Maple