Dr Radislav Vaisman
Lecturer, Associate Investigator
I am a Lecturer in Data Science in the School of Mathematics and Physics in the University of Queensland. My research lies in the field of applied probability, stochastic simulation and machine learning. In particular, I am interested in the rare-events. These events are of great interest in many areas such as theoretical computer science, optimization and counting
Research Interests:
Advanced Monte Carlo Methods for rare event estimation
Combinatorial Optimization and Counting
Data Science
Design analysis and implementation of algorithms
Evolutionary computation
Machine Learning
Network reliability
Stochastic Simulation and Applied Probability
Qualifications:
PhD
Projects
Publications
Books
(2019). Data Science and Machine Learning: Mathematical and Statistical Methods.
Machine Learning & Pattern Recognition. 510.
Book Chapters
(2018). Reliability of a Network with Heterogeneous Components.
(Lisnianski, A., Frenkel I., & Karagrigoriou A., Ed.).Recent Advances in Multi-state Systems Reliability. 3 - 18. doi: 10.1007/978-3-319-63423-4_1
Invited talks, refereed proceedings and other conference outputs
(2016). New Sampling Plans for Estimating Residual Connectedness Reliability.
Annual International Conference on Operations Research and Statistics ( ORS 2016 ). doi: 10.5176/2251-1938_ORS16.18
(2016). Estimating the number of vertices in convex polytopes.
4th Annual International Conference on Operations Research and Statistics (ORS 2016). doi: 10.5176/2251-1938_ORS16.25
Journal Articles
(2018). On the analysis of independent sets via multilevel splitting.
Networks. 71(3), 281 - 301. doi: 10.1002/net.21805
(2017). Stochastic Enumeration Method for Counting Trees.
Methodology and Computing in Applied Probability. 19(1), 31 - 73. doi: 10.1007/s11009-015-9457-4
(2017). The Multilevel Splitting algorithm for graph colouring with application to the Potts model.
Philosophical Magazine. 97(19), 1646 - 1673. doi: 10.1080/14786435.2017.1312023
(2016). Improved Sampling Plans for Combinatorial Invariants of Coherent Systems.
IEEE Transactions on Reliability. 65(1), 410 - 424. doi: 10.1109/TR.2015.2446471
(2016). Splitting sequential Monte Carlo for efficient unreliability estimation of highly reliable networks.
Structural Safety. 63, 1 - 10. doi: 10.1016/j.strusafe.2016.07.001
(2015). Model Counting of Monotone Conjunctive Normal Form Formulas with Spectra.
INFORMS Journal on Computing. 27(2), 406-415. doi: 10.1287/ijoc.2014.0633
(2014). Monte Carlo for Estimating Exponential Convolution.
Communications in Statistics - Simulation and Computation. 44(10), 2696-2704. doi: 10.1080/03610918.2013.842591
Publicly available softwares
(2019). DSML-book.
Data Science and Machine Learning: Mathematical and Statistical Methods.
