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Larry Fulton is an Associate Professor of Health Administration at Texas State University, San Marcos. He earned his Doctorate of Philosophy / Masters of Science in Statistics from the University of Texas at Austin, his Master of Health Administration from Baylor, and three other graduate degrees. Dr. Fulton is a Fellow of the American College of Healthcare Executives (FACHE) and maintains the credentials of Chartered Scientist and Chartered Statistician (CStat CSci) as a Fellow in the Royal Statistical Society. He is a Certified Analytics Professional (CAP) of the Institute for Operations Research & Management Science, a Certified Quality Engineer and Certified Six Sigma Black Belt (CQE CSSBB) of the American Society for Quality and a Professional Statistician (PStat) of the American Statistical Association.

Christopher Musco is an Assistant Professor in the Computer Science and Engineering department at NYU鈥檚 Tandon School of Engineering. Christopher鈥檚 research focuses on the algorithmic foundations of data science and machine learning. He studies methods for efficiently processing and understanding data, often working at the intersection of theoretical computer science, numerical linear algebra, and optimization.

Christopher received his Ph.D. in Computer Science from the Massachusetts Institute of Technology and B.S. degrees in Applied Mathematics and Computer Science from Yale University. 

Research Interests: Scalable machine learning, foundations of data science, numerical linear algebra, theory of algorithms, randomized algorithms, sketching and streaming

Woodstock teaches applied mathematics courses, with an emphasis on how class material is used in everyday life. He specializes in optimization, and how it arises within machine learning tasks. 
His research focuses on two areas, developing new algorithms to solve modern challenges in data science and mathematically proving that these new algorithms are guaranteed to do their job. His work has been used for image reconstruction, audio de-noising and change detection from bitemporal satellite imagery.
 
 A goal of providing these mathematical guarantees is to contribute theoretically-sound alternatives to the theoretically unfounded ad-hoc techniques (e.g., neural network training with ReLU activation and algorithmic differentiation) that are rapidly being adopted in critical infrastructure. 
Woodstock earned a bachelor's degree in mathematics at JMU, a master's degree in applied mathematics at North Carolina State University and a doctorate in mathematics at North Carolina State University. Before joining JMU as faculty, he was a postdoctoral staff scientist at the Interactive Optimization and Learning Laboratory based in Technische Universität Berlin and the Zuse Institute Berlin.