c— title: “Vadim Sokolov” —
Nguyen Engineering Building MS 4A6
Office: 2242
Fairfax, VA, 22030
Phone: 703 993 4533
vsokolov@gmu.edu
Book on Bayes AI
Discover the intersection of Bayesian statistics, artificial intelligence, and deep learning in our new book, Bayes, AI and Deep Learning: Foundations of Data Science. Co-authored by Nick Polson and Vadim Sokolov, this book offers an accessible yet rigorous journey through the core ideas shaping modern data science. Drawing on years of teaching experience with both business and engineering audiences, we blend intuitive explanations, real-world case studies, and hands-on exercises to bridge theory and practice. Whether you’re a manager seeking to leverage AI for strategic advantage or an engineer building intelligent systems, you’ll find practical insights into topics ranging from probability and Bayesian inference to neural networks and large language models. Join us as we explore how these transformative tools are revolutionizing industries—from personalized medicine to urban systems—and learn how to harness their power for your own projects. Read more and explore the full table of contents.
Current Teaching
- AI 600: Foundations of AI (page)
Areas of Expertise
- Data science: Bayesian statistics, deep learning, reinforcement learning
- Complex Systems: Agent-based models, Bayesian optimization
- Applications: Urban systems modeling, digital twins
Bio
Vadim Sokolov is an associate professor in the Systems Engineering and Operations Research Department at George Mason University. He works on building robust solutions for large scale complex system analysis, at the interface of simulation-based modeling and statistics. This involves, developing new methodologies that rely on deep learning, Bayesian analysis of time series data, design of computational experiments and development of open-source software that implements those methodologies. Inspired by an interest in urban systems he co-developed mobility simulator called Polaris that is currently used for large scale transportation networks analysis by both local and federal governments. Prior to joining GMU he was a principal computational scientist at Argonne National Laboratory, a fellow at the Computation Institute at the University of Chicago and lecturer at the Master of Science in Analytics program at the University of Chicago.
He has published in such leading statistics, mathematics and engineering journals, as the Annals of Applied Statistics, Transportation Research Part C, Linear Algebra and Its Applications as well as in Mechanical Systems and Signal Processing. He holds a PhD in computational mathematics from Northern Illinois University, and pursued graduate studies in statistics at the University of Chicago, while working at Argonne.