Regular biography
Dr. Sarat Moka is a researcher and academic with expertise in Applied Probability, Computational Statistics, Machine Learning, and Deep Learning. His work focuses on developing efficient algorithms for solving complex problems, often NP-hard, within these domains. He has made significant contributions to areas such as Model Selection, Best Subset Selection, Monte Carlo Simulation, Spatial Point Processes, Bayesian Inference, Perfect Sampling, Importance Sampling, Unbiased Estimation, Large Deviations Theory, Variance Reduction Techniques, and Queueing Theory. Dr. Moka is currently affiliated with the University of Melbourne, where he is a Senior Lecturer in the School of Mathematics and Statistics. He is also actively involved in industry collaborations, including with the Environmental Protection Authority (EPA) Victoria and CSIRO. His research has been published in top-tier journals and conferences, and he is known for his innovative approaches to statistical and probabilistic problems. Dr. Moka is also an active supervisor of research students and teaches a range of courses in statistics and data science.