Stochastic Optimal Control

Illustration weak approximation SDE

AMCS 336 Numerical Methods for Stochastic Differential Equations with connections to Machine Learning

stochastic differential equations Ito integral Monte Carlo Multilevel Monte Carlo Importance sampling Variance Reduction Kolmogorov Backward Equation Fokker-Planck equations Hamilton-Jabobi-Bellman Stochastic Optimal Control

The goal of this course is to give basic knowledge of stochastic differential equations and their numerical solution, useful for scientific and engineering modeling, guided by some problems in applications in financial mathematics, material science, geophysical flow problems, turbulent diffusion, control theory, and Monte Carlo methods.

Species count mRNA, proteins, dimers - jump processes

AMCS 308 Stochastic Numerics with Application in Simulation and Data Science

stochastic algorithms Stochastic Methods Stochastic Modeling Stochastic Optimal Control Stochastic processes Filtering theory data assimilation Monte carlo methods Variance Reduction Importance sampling

Monte Carlo methods. Simulation, estimation, data assimilation, and optimal control for time-discrete and time-continuous Markov chains

KAUST alumnus Dr. Abdul-Lateef Haji-Ali receives prestigious Humboldt Research Fellowship for Experienced Researchers

2 min read · Mon, Sep 29 2025

uncertainty quantification stochastic differential equations Stochastic Optimal Control

KAUST alumnus Abdul-Lateef Haji-Ali receives Humboldt Fellowship for advances in stochastic modeling

Renzo Caballero

Ph.D. Student, Applied Mathematics and Computational Science

Self-Replicating Robots Stochastic Optimal Control robotics Numerical Optimization

Education 2022.05.24 Ph.D. Research Proposal: "Theory and Dynamics of Self-Repair Systems" Committee Members: Prof. Eric Feron, Prof. Shinkyu Park, Prof. Diogo Gomez, Prof. David Keyes, Prof. Taous-Meriem Laleg 2019.06 - 2023.12 Ph.D. in Applied Mathematics and Computational Sciences, KAUST, KSA 2017.08 - 2019.06 M.Sc. in Applied Mathematics and Computational Sciences, KAUST, KSA 2010.02 - 2016.06 B.Sc. in Electrical Engineering, Universidad de la Republica, Montevideo, Uruguay Professional Profile 2021.08 - 2021.12 Teaching Assistant, CEMSE, KAUST, KSA 2017.01 - 2017.04 Models Analyst, UTE

Erik von Schwerin

Research Scientist, Stochastic Numerics Research Group

uncertainty quantification numerical analysis stochastic differential equations Stochastic Optimal Control

Given a mathematical model of a system with uncertainties, a computational method of approximation of that model, and a specific Quantity of Interest (QoI) of the system, how can we, with as small computational effort as possible, approximate the QoI within a user-specified accuracy? Most of Erik von Schwerin's research relates in some way or another to this question.