I'm an experienced quantitative researcher with a focus on developing systematic trading strategies in global equities (primarily focused on the US). I have experience contributing to every aspect of the research process from idea generation to implementation and risk management. I have an extensive background in statistics, dating back to before the terms "data science" and "machine learning" were in common parlance. I completed my PhD in statistics at Cornell University. My research focused on the application of regularization methods to a wide variety of high-dimensional applied financial problems, with a particular emphasis on multivariate time series. I still actively engage in research whenever I have the time and in addition to regularization in time series, I have a particular interest in random forests and am always on the lookout for interesting and challenging problems. Over the course of my academic and industry career, I have developed novel, computationally efficient forecasting and prediction methods that result in substantial improvements in accuracy over conventional methods. I am an expert R programmer with considerable experience in both academic and industry settings. Many of my publicly available algorithms are aggregated in the R package BigVAR (available on cran: https://cran.r-project.org/package=BigVAR). A brief tutorial is posted on my website: http://www.wbnicholson.com/BigVAR.html.Links to my papers are available at my website: http://www.wbnicholson.com and additional information is available upon request.