Sequential Decision Making – Information

Fenghui Yu

Personal website

Fenghui Yu is an Assistant Professor in Stochastic and Mathematical Finance at TU Delft, where she has been since 2022. Prior to that, she was a postdoctoral researcher at RiskLab and the Department of Mathematics at ETH Zürich. She obtained her PhD in Financial Mathematics from the University of Hong Kong. Her research interests lie broadly in stochastic modeling, optimal control, and risk management, with applications in finance. Specific areas of recent interest include algorithmic trading, market microstructure, and data-driven approaches, such as reinforcement learning, for sequential decision-making in financial systems.

Lecture 1 Lecture 2

Xiaodong Cheng

Personal website

I am an assistant professor at the Mathematical and Statistical Methods (Biometris), Wageningen University & Research (WUR). My main research interests cover various topics in control systems, optimization and machine learning. I obtained my Ph.D. degree with honors (cum laude) from the University of Groningen, the Netherlands, and before joining WUR, I was appointed as a research associate in the Department of Engineering at the University of Cambridge from 2020 to 2022 and a postdoctoral researcher in the Department of Electrical Engineering at the Eindhoven University of Technology from 2019 to 2020. I am the recipient of the Paper Prize Award from the IFAC Journal Automatica in the triennium 2017–2019 and the Outstanding Paper Award from IEEE Transactions on Control Systems Technology in 2020.

Lecture 1 Lecture 2

Abstracts

Fenghui Yu

Lecture 1: Foundations of Stochastic Optimal Control and Connections to Reinforcement Learning

This lecture introduces core concepts of stochastic optimal control, with a focus on the dynamic programming principle and the associated Hamilton-Jacobi-Bellman (HJB) equation. We will then explore how these concepts connect to reinforcement learning, and how both frameworks can be applied to sequential decision-making problems in finance.

Lecture 2: Applications in Finance – From Stochastic Control to Algorithmic Trading

This session builds on the theoretical groundwork of the first lecture, and focuses on applications in quantitative finance. Through examples from algorithmic trading, we will illustrate how HJB-based methods and data-driven control approaches can be used to solve real-world financial decision problems.

Xiaodong Cheng

Lecture 1: Introduction to Model Predictive Control with Greenhouse Application

This lecture will provide an introduction to control theory and Model Predictive Control (MPC) with a practical application to greenhouse climate control. The session is divided into two parts: Part 1 begins with an overview of control theory, introducing key concepts such as feedback, stability, and system dynamics, using a greenhouse as an illustrative example to contextualize these principles. This will be followed by an introduction to optimal control, focusing on the formulation and objectives of optimizing system performance, from there, we will look into MPC, starting with linear systems to explain the core methodology, including prediction models, cost functions, and constraints. A brief discussion on nonlinear MPC will highlight its relevance and challenges without excessive technical detail, ensuring accessibility for students new to the topic.

Lecture 2: Introduction to Model Predictive Control with Greenhouse Application

Part 2 is about greenhouse control problem with hands-on practice. The control objectives and constraints are defined, followed by a step-by-step formulation of an MPC problem tailored to greenhouse control. An introduction to the software tools used for implementation will be provided, where students can use to apply MPC to a simple greenhouse model and analyze simulate results.