Bio

I'm a seasoned NLP and Recommendation data scientist with several years of research and engineering in the Natural Language Processing and Recommendation domains. I've had experience doing professional research with professors and colleagues in the ML space and have credentialed publications to my name and credit. I'm also highly in demand for conference presentations, book editing and book reviews. Whether it is researching document summarization techniques, turns of phrases in dialogue analysis, personality analysis through written text, recommendation systems using natural language queries, question/answering systems, chatbots and information retrieval, I make it my utmost effort to keep on top of the latest research and state of the art techniques. I do this not only for professional reasons, but also from general curiosity. With the 4th industrial revolution utterly transforming our society and the world, we are now doing the impossible: slowly beginning to mimic aspects of humans long thought impossible. Perhaps the Singularity long espoused by Ray Kurzweil and dismissed for a long time may turn out to be feasible after all. I consider myself not only an expert in the various technical domains needed for ML/NLP (hello dual Math/Computer Engineering masters), but also a domain expert in the intangible domains used for NLP including linguistics and psychology. As a dual language speaker, I am familiar with the subtle nuances and communication styles afforded in other languages. I have always been long interested in psychology growing up. Before settling on the tech field, I was considered the empathetic person people needed in times of trouble. Friends and acquaintances had always thought I would have become a psychologist before settling on the tech field. Such fields as you know are great domains to help get an intuitive feel for NLP as one tries to parse the correct sentences and intentions in a natural language text. Research Interests: • Natural Language Processing for the Financial/Healthcare Field • Algebraic topology for topological data analysis of news articles. • Graph theory for Graphical Neural Network analysis. • Financial Markets and Algorithmic Trading (Time Series analysis) • State of the art deep learning for tweet analysis of cryptocurrency. • Generating mathematical models and Monte-Carlo simulations for Bayesian analysis of linguistic structures. • Philosophy/psychology/cognitive science. • Actively learning new technologies In my spare time, I'm learning new CS and math concepts to be on top of cognitive science.