Applied Machine Learning

Deep learning accelerates research on early pregnancies

1 min read ·
KAUST researchers have created deepBlastoid, a deep learning tool that surpasses scientists at evaluating laboratory models of the human embryo before pregnancy

Multilevel Bayesian Neural Networks

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B9 L2 R2322

Abstract In this talk we consider the application of multilevel Monte Carlo for Bayesian computation tasks in machine learning. There has recently been a synergy of statisticsand machine learning, promoting the application and development of new methodologies.Based on this we promote the use of multilevel Monte Carlo, which is a technique used toreduce the cost to attain a particular order of MSE with trace-class neural network priors. We provide some theoretical insights, and demonstrate the performance of our methodologyon different model problems such as classification and reinforcement

Numerical Methods for Energy Minimisation: Application to Machine Learning Algorithms

Prof. Charalambos Makridakis, Director of the Institute of Applied and Computational Mathematics of FORTH, Greece

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B2 L5 R5220

In this talk, we discuss problems and numerical methods arising in the calculus of variations and energy minimization. Among numerous applications, energy minimization is a core element of Machine Learning algorithms. Within the field of nonlinear PDEs, the calculus of variations has received a lot of attention from the analysis point of view. Although quite interesting and challenging, the numerical analysis of these problems is much less developed.

Mean Field Games price formation models

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B1 L4 R4214

This thesis studies mean-field games (MFGs) models of price formation. The thesis focuses explicitly on a MFGs price formation model proposed by Gomes and Saúde. The thesis is divided into two parts. The first part examines the deterministic supply case, while the second part extends the model to incorporate a stochastic supply function. We explore different approaches, such as Aubry-Mather theory, to study the properties of the MFGs price formation model and alternative formulations using a convex variational problem with constraints. We propose machine-learning-based numerical methods to approximate the solution of the MFGs price formation model in the deterministic and stochastic setting.

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

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B3 L5 R5220

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Rudi Coppola

Rudi Coppola is a Master's student under the supervision of Professor Mohamed-Slim Alouini at Communication Theory Lab (CTL) at King Abdullah University of Science and Technology (KAUST). Education and Early Career Rudi completed his Bachelor’s Degree in Electronic Engineering from the University of Pisa in 2019. He has also collaborated with 'e-team', the university’s racing team, under the electronic division. In the same year, he was admitted to KAUST as a Master's student. In 2021 he earned a Master's degree in Electrical Engineering at KAUST, presenting his thesis on radar-based road

Malik Shahid Sultan

Ph.D. Student, Statistics
Malik Shahid Sultan is a Ph.D. student in Bio-Engineering ( Bioinformatics & Machine Learning) at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Hernando Ombao in the Biostatistics group. Education and Early Career Malik Shahid Sultan hold BSc. in Industrial Engineering with a Gold Medal from the University of Engineering and Technology Taxila (Pakistan). Malik holds M.Sc. from King Abdullah University of Science and Technology (KAUST) (Saudi Arabia) in Bio-Engineering (Bioinformatics & Machine Learning). During the Fall of 2021

Julian David Gutierrez Pineda

Julian received both B.S. in Mathematics and B.S. in Engineering from Escuela Colombiana de Ingenieria, Colombia. After receiving his M.Sc. in Mathematical Sciences from Universidad Nacional Autónoma de México, México, he joined Professor Diogo Gomes as a Ph.D. student in applied mathematics at King Abdullah University of Science and Technology, Saudi Arabia. His research interests are primarily optimal control, optimal transport, calculus of variations, partial differential equations, and mean-field games.

Juan Manuel Marin Mosquera

Ph.D. Candidate (former), Electrical and Computer Engineering
Early stage researcher focused on laying the foundations for the emerging field of Integrated Sensing and Communications (ISAC). His work aims to incorporate smart sensing solutions leveraging machine learning-aided Distributed Fiber Optic Sensing (DFOS) into the next generation of fiber-based telecommunication networks, as well as their integration into the realm of the Internet of Things (IoT).

Christos Sapsanis

Education Profile : - Ph.D. Candidate, Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA. - M.Sc., Electrical Engineering (Electrophysics), King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, 2015. - B.Sc., Electrical and Computer Engineering, University of Patras, Greece, 2013. Introduction: Christos Sapsanis obtained his M.Sc. Degree in the Electrical Engineering (EE) Program from King Abdullah University of Science and Technology, under the supervision of Prof. Khaled Nabil Salama. Before joining KAUST, he earned his B.Sc. in Electrical