Alex Shestopaloff

I am a Fellow of the Alan Turing Institute, mentored by Arnaud Doucet and Ruth King. My interests are in developing efficient Monte Carlo methods for statistical inference problems.

I graduated with a PhD in Statistics from the Department of Statistical Sciences, University of Toronto in June 2016, where my supervisor was Radford M. Neal.

E-mail: alexander@utstat.toronto.edu

Preprints:

  • A.Y. Shestopaloff and R. M. Neal. MCMC for non-linear state space models using ensembles of latent sequences. Also available at arxiv.org
  • A.Y. Shestopaloff and R. M. Neal. On Bayesian inference for the M/G/1 queue with efficient MCMC sampling. Also available at arxiv.org
  • A.Y. Shestopaloff and R. M. Neal. Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods. Also available at arxiv.org

    Articles:

  • A.Y. Shestopaloff and R. M. Neal (2018). Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method. Bayesian Analysis. Available here. This is a revised version of the following technical report.

  • Miasnikof, P., Shestopaloff, A., Bonner, A. J. and Lawryshyn, Y. (2018). A Statistical Performance Analysis of Graph Clustering Algorithms. In A. Bonato, P. Pralat and A. Raigorodskii (Eds.), Algorithms and Models for the Web Graph: 15th International Workshop, WAW 2018, Moscow, Russia, May 17-18, 2018, Proceedings (Lecture Notes in Computer Science; Vol. 10836). Springer Nature.

  • Miasnikof, P., Giannakeas, V., Gomes, M., Aleksandrowicz, L., Shestopaloff, A. Y., Alam, D., Tollman, S., Samarikhalaj, A. and Jha, P. (2015) Naive Bayes classiers for verbal autopsies: comparison to physician-based classication for 21,000 child and adult deaths, BMC Medicine, 13:286.

    Previously, I also worked on problems in investment performance measurement.

  • Y. Shestopaloff and A. Shestopaloff. Choosing the Right Solution of IRR Equation to Measure Investment Success. The Journal of Performance Measurement. Fall 2013.