Machine Learning and Artificial Intelligence Engineer with Full Stack experience. Presented original research on subvocal recognition using multilayer perceptrons at ICTAI 2017 in November. Experienced with bespoke machine learning solutions in several contexts (NLP, sequence to sequence, vision, stochastic regression, etc.) and algorithmic optimization for cost effective pipeline scaling. Machine Learning and AI experience began with Udacity's MLND (2016) and AIND (2017), projects at Syntasa, and continuing with independent ventures. See my GitHub for examples. • I have over a decade (10+ years) of formal programming experience, beginning with Java in High School in 2006, then Python at Drexel University in 2007 and University of Maryland, Baltimore County in 2009, and continuing with both at University of the People at present. Along the way, I’ve also picked up Javascript through Udacity, Scala at Syntasa, and R for UoPeople. • I also have over a decade (10+ years) of formal experience with applied mathematics, including over 10 years in each of statistics, calculus, differential equations, and linear algebra. Languages: Python (14 years), R (1 year), Java (15 years), Scala, SQL (5 years), Javascript (6 years), HTML (6 years), CSS (6 years), Matlab (14 years)Key Technologies: Keras (5 years), TensorFlow (5 years), Scikit-Learn (6 years), HMMLearn, Pip, Jupyter, Pandas, NumPy, SciPy, Spark, HadoopTechnologies: AWS, GCP, EC2, Atom.io, Eclipse, Notepad++, Git, Subversion, Linux, Windows, AJAX, JSON, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Cross Validation, Bower, Grunt, Jquery, Knockout.js, Backbone.js, Angular.js, Node.js, Bootstrap, Yeoman, NPM, Express, MongoDB, AutoCAD, FreeCAD, MATLAB, GiD.
Listed skills include Machine Learning, Deep Learning, Scikit Learn, Javascript, and 15 others.
