Slava Vaisman

Radislav Vaisman’s research interests lie at the intersection of applied probability and computer science where he has made key contributions to the theory and the practical usage of Sequential Monte Carlo methods. Specifically, his work led to the publication of a book by John Wiley & Sons: Fast Sequential Monte Carlo Methods for Counting and Optimization, which covers the state-of-the-art of modern simulation techniques for counting and optimization. In addition, his contribution to the field of System Reliability resulted in the book: Ternary Networks: Reliability and Monte Carlo, by Springer. In 2019, Radislav coauthored the book: Data Science and Machine Learning: Mathematical and Statistical Methods, which was published by CRC Press. Dr. Vaisman has published in top-ranking journals such as Statistics and Computing, INFORMS, Journal on Computing, Structural Safety, Networks, and IEEE Transactions on Reliability.

Radislav Vaisman's research in the field of Sequential Monte Carlo led to the development of the Stochastic Enumeration method for estimating the size of backtrack trees. The proposed method tackles this very general but difficult problem in computational sciences. Dr. Vaisman also developed a rigorous analysis of the Stochastic Enumeration procedure and showed that it results in significant variance reduction as compared to available alternatives. In addition, he applied the multilevel splitting ideas to many practical applications, such as optimization, counting, and network studies. Dr. Vaisman has produced insightful work in the field of systems reliability, both in theory and practice. In particular, he has developed Sequential Monte Carlo methods for estimating failure probability in highly reliable structures and new sampling plans for estimating network reliability based on a network’s structural invariants. This contribution has been recognized by top scientific journals in this field, namely Structural Safety and IEEE Transactions on Reliability.