Monte carlo methods

PhD Student Abdul Lateef Haji Ali to Present 'Monte Carlo Methods' Talk at University of Basel

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KAUST

In this talk, I explore the application of single level and multilevel Monte Carlo methods for computing key quantities in a stochastic particle system within the mean-field framework.

Sequential Monte Carlo Methods By Prof. Ajay Jasra (National University of Singapore)

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B1 R4214

This talk will provide a tutorial style introduction to sequential Monte Carlo methods, attempting to start from a basic introduction, to a coverage of some of the latest research developments.

Multilevel Approximations of Markovian Jump Processes with Applications in Communication Networks By Pedro Vilanova (PhD Student of Prof. Raul Tempone, KAUST)

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Al Marsa Yacht Club

This thesis focuses on the development and analysis of efficient simulation and inference techniques for Markovian pure jump processes with a view towards applications in dense communication networks. The first part of this work proposes novel numerical methods to estimate, in an efficient and accurate way, observables from realizations of Markovian jump processes.

Learning for Visual Data Synthesis and Analysis

Dr. Timo Ropinski, Professor, Visual Computing, Ulm University, Germany

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B9 L2 H1 R2322

Abstract In this talk, I will present our recent advances in deep learning to synthesize and analyze visual data, such as images, volumes, and point clouds. The presented approaches are loosely aligned along the classical computer graphics rendering pipeline, whereby both structured and unstructured data are handled. I will first present concepts for learning in object space, i.e., directly on the data to be rendered. To realize different visual tasks, such as normal estimation and segmentation, I will discuss how Monte Carlo integration can be used to realize convolutions on point cloud data

Coupled Sampling Methods for Filtering

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KAUST

This thesis focuses on the use of multilevel Monte Carlo methods to achieve optimal error versus cost performance for statistical computations in hidden Markov models as well as for unbiased estimation under four cases: nonlinear filtering, unbiased filtering, unbiased estimation of hessian, continuous linear Gaussian filtering.

Prof. Tempone is plenary speaker at MCQMC2014

1 min read ·
Prof. Tempone is a plenary speaker at the 11th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing.

PhD Student Abdul Lateef Haji Ali to Present 'Monte Carlo Methods' Talk at University of Basel

1 min read ·
I discuss using single level and multilevel Monte Carlo methods to compute quantities of interests of a stochastic particle system in the mean-field. In this context, the stochastic particles follow a coupled system of Ito stochastic differential equations (SDEs).

Prof. Raul Tempone and Dr. Chiheb Ben Hammouda are organizing a minisymposia "Hierarchical Methods for Variance Reduction" at the MCQMC Conference, 10-14 August 2020, University of Oxford

1 min read ·
​Prof. Raul Tempone and Dr. Chiheb Ben Hammouda are organizing a mini-symposia "Hierarchical Methods for Variance Reduction" at the Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing Conference, 10-14 August 2020, University of Oxford.​​​

Miguel Angel Alvarez Ballesteros

Miguel Angel Alvarez Ballesteros is a M.S./Ph.D. candidate in the KAUSTStochastic Numerics research group under the supervision of Professor Raul Tempone. Before joining KAUST, Miguel obtained a bachelor's degree in physics from the Universidad Industrial de Santander, Bucaramanga, Colombia. Research Interests Miguel's research interests include Monte Carlo methods, Bayesian Statistics, Particle filtering and Machine learning. Education Profile Bachelor's degree in Physics from the Universidad Industrial de Santander (UIS), Bucaramanga, Colombia, in 2019. Publications Álvarez, M., Orjuela