I'm an applied mathematician focusing on bayesian modeling and machine learning for real-time, production systems. I create custom models starting from general predictive/inferential requirements, to statistical models and their explicit implementations. Believe it or not, my research is in the same areas! Years of professional research experience focused on algorithmic and statistical programming. I've worked primarily in the Linux environment with languages such as C/C++, Java, R, and Python. Most of my development work involves programming for asynchronous processing (i.e. multi-threading and GPUs). I'm fond of open-source projects, and the idea of making sophisticated mathematical models accessible to the average developer. Otherwise, I like to imagine how great the world would be if HUDs could replace monitors.

Statistics/Data Science Lead @ Developed custom retail demand models. From January 2015 to July 2015 (7 months) Greater Chicago AreaFaculty Research Consultant @ Consultant to faculty on matters of research involving statistics, optimization, machine learning, and data manipulation and management. From August 2013 to January 2015 (1 year 6 months) Greater Chicago AreaMentor @ Mentor on the CTA-sim and Energywise projects. From June 2013 to September 2013 (4 months) Greater Chicago AreaStatistics Lead @ Statistics, real-time modeling for vehicle tracking and traffic inference. From August 2011 to June 2013 (1 year 11 months) Consulting Statistician/Developer @ Worked on sequential Bayesian models of market micro-structure for high-frequency market making. From August 2010 to August 2011 (1 year 1 month) Algorithmic Trading @ Designed, developed, and operated high-frequency trading algorithms. From April 2009 to August 2010 (1 year 5 months) Program Developer @ Responsible for developing, implementing and optimizing new code for trading, risk management, and analytics. From April 2008 to March 2009 (1 year) Applied Financial Mathematics Researcher @ Worked on a hybrid geometric Brownian motion switching model modulated by a finite-state Markov chain for option pricing. From October 2007 to April 2008 (7 months) Statistical Methods Researcher @ Wrote a critical analysis of particle physics data using a novel Goodness of Fit theory. Used Monte Carlo simulations to demonstrate efficacy of the theory, which showed a significant increase in bias rejection for certain cases. From September 2004 to September 2007 (3 years 1 month)

M.S., Statistics @ University of Chicago From 2008 to 2011 B.S. and Minor, Physics and Mathematics @ Wayne State University From 2003 to 2007 Certificates in Physics, Physik, Major GPA: 3.8 @ Ludwig-Maximilians Universität München From 2005 to 2006 Brandon Willard is skilled in: Applied Mathematics, Sequential MCMC, Mathematical Modeling, Statistical Data Analysis, Particle Filters, Monte Carlo Simulation, Markov Chain Monte Carlo, Real-time Data, Bayesian methods, Optimization, Bayesian statistics, Algorithms, Python, Statistics, R