Bill specializes in data science, algorithms and advanced analytics. He has over 20 years experience in the industry, government, and academia in the application of machine learning and stochastic modeling to real-world problems.
He has developed and deployed algorithmic solutions for industry, government and defense agencies enabling analytics to be applied at scale to open and closed-source data.
He has held teaching and visiting academic positions at research and academic entities including George Mason University, George Washington University, and Tokyo Institute of Technology. He has authored or co-authored over 20 academic publications on machine learning, stochastic modeling, and acoustics.
He has received awards for innovation, a best paper award from the international Speech Communication journal, and has notable performances in analytics competitions including runner-up in the Netflix movie recommendation competition.
Notable industry and government achievements include algorithms for voice recognition, sales performance estimation, opioid abuse detection, retail loss prevention estimation, and customer attrition reduction.
Bill has B.E. (Hons) and B.Sc.(hons) degrees from the University of Adelaide, Adelaide, Australia, and a Ph.D. degree from George Mason University, Fairfax VA.