Projects per year
My research focuses on problems in Statistics, Data Science, Monte Carlo Simulation, and Computer Science. In particular, I consider hard problems in these areas and develop efficient computer algorithms to tackle those problems.
Some of the keywords associated with my research are Model Selection, Best Subset Selection, Deep Learning, MCMC Methods, Spatial Point Processes, Bayesian Inference, Perfect Sampling, Importance Sampling, Unbiased Estimation, Large Deviations Theory, Variance Reduction Techniques, and Queueing Theory.
Currently writing a book on The Mathematical Engineering of Deep Learning jointly with Prof. Benoit Liquet (MQ) and A/Prof. Yoni Nazarathy (UQ). Completed chapters are freely available at https://deeplearningmath.org/.
The following are several research projects that are currently taking most of my time. If you are interested in discussing them, feel free to drop an email.
COMBSS: Continuous Optimization towards Best Subset Selection: Recent rapid developments in information technology have enabled the collection of high-dimensional complex data, including in engineering, economics, finance, biology, and health sciences. High-dimensional means that the number of features is large and often far higher than the number of collected data samples. In many of these applications, it is desirable to find a small best subset of predictors so that the resulting model has desirable prediction accuracy. This is a hard problem (NP-hard). In this project, we recast the challenge of best subset selection in linear regression as well as non-linear regression as a continuous optimization problem. We show that this reframing has enormous potential and substantially advances research into larger dimensional and exhaustive feature selection in regression, making available technology that can reliably select significant variables when the number of features is well in excess of 1000s.
The first paper (preprint) is available at https://arxiv.org/abs/2205.02617.
Partial Rejection Sampling for Markov Random Fields: With the rapid acceleration of computational power over the last half-century, sampling techniques have become ubiquitous in engineering and scientific disciplines and financial and industrial applications. When attempting to sample from a probability distribution, these Monte Carlo techniques fall into two main categories: approximate methods and exact or perfect sampling techniques. A typical approximate approach is to construct a Markov chain whose distribution asymptotically converges to the target distribution. Two key drawbacks of Markov chain methods are that they are sequentially and it is hard to bound the approximation error. In this project, we develop partial rejection sampling methods for Markov random fields. These methods are exact and parallelizable. Some of the applications we focus on are Gibbs point processes, graph colouring, and sampling of solutions of stochastic differential equations.
Primary work from this project is published in Bernoulli (click here).
- Moka, S. B., Juneja, S. and Mandjes, M. R. H.  “Rejection and Importance Sampling based Perfect Simulation for Gibbs Hard-Spheres Processes”, Advances in Applied Probability. [Link]
- Hirsch, C., Moka, S. B., Taimre, T. and Kroese, D.  “Rare Events in Random Geometric Graphs”, Methodology and Computing in Applied Probability. [Link]
- Moka, S. B., Juneja, S. and Mandjes, M. R. H. 2018. “Analysis of Perfect Sampling Methods for Hard-sphere Models”, SIGMETRICS Perform. Eval. Rev. 45(2) [Link]
- Foss, S., Juneja, S., Mandjes, M. R. H. and Moka, S. B. 2015. “Spatial Loss Systems: Exact Simulation and Rare Event Behavior”, SIGMETRICS Perform. Eval. Rev. 43(2) [Link]
- Statistical Inference (STAT3110/6110) . . . . . . . . . . . . . . . Semester 2, 2022 Macquarie University, Australia.
- The Mathematical Engineering of Deep Learning . . . . . . AMSI Summer School, 2021, Adelaide. [Link]
- Problems & Applications in Modern Statistics (STAT3500/7500). Semester 2, 2020, The University of Queensland, Brisbane, Australia.
- Problems & Applications in Modern Statistics (STAT3500/7500). Semester 2, 2019, The University of Queensland, Brisbane, Australia.
Research student supervision
- Vindya Warnakulasooriya (MRes + PhD), School of Mathematical and Physical Sciences, MQ. Jointly with Prof. Samuel Muller.
- Anant Mathur (PhD), School of Mathematics and Statistics, UNSW. Jointly with Dr Zdravko Botev.
Applied Probability, PhD, Tata Institute of Fundamental Research
Award Date: 5 Jul 2017
Telecommunications, Master of Engineering, Indian Institute of Science
Award Date: 1 Apr 2008
Electronics and Communications Engineering, Bachelor of Engineering
Award Date: 1 Apr 2005
Postdoctoiral Research Fellow, The University of Queensland
18 Jul 2017 → 28 Feb 2021
Scientist-SC, Indian Space Research Organization
5 Sep 2008 → 25 May 2010
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Collaborations and top research areas from the last five years
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Moka, S., Liquet, B., Zhu, H. & Muller, S., 5 May 2022, (Submitted) arXiv.org, (arXiv).
Research output: Working paper › Preprint
Mathur, A., Moka, S. & Botev, Z., Oct 2022, In: Algorithms. 15, 10, p. 1-20 20 p., 354.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile5 Downloads (Pure)
Hirsch, C., Moka, S. B., Taimre, T. & Kroese, D. P., Sep 2022, In: Methodology and Computing in Applied Probability. 24, 3, p. 1367-1383 17 p.
Research output: Contribution to journal › Article › peer-reviewOpen AccessFile
Moka, S. B., Nazarathy, Y. & Scheinhardt, W., 22 Nov 2021, (Submitted) arXiv.org, (arXiv).
Research output: Working paper › Preprint
Moka, S., Juneja, S. & Mandjes, M., Sep 2021, In: Advances in Applied Probability. 53, 3, p. 839-885 47 p.
Research output: Contribution to journal › Article › peer-review