Topics

<> machine learning (10)

Hayssam Dahrouj

Senior Research Scientist at KAUST (King Abdullah University of Science and Technology)

Karen Sanchez

Postdoctoral Research Fellow
Karen Sanchez is a Postdoctoral Researcher in the IVUL lab at KAUST, specializing in deep learning, machine learning, and artificial intelligence (AI) for healthcare applications. Her research focuses on video understanding, domain adaptation, generative AI, and methods for preserving patient privacy.

Hani A. Al Majed

M.S. Student
Electrical and Computer Engineering Master's student at KAUST under the supervision of Professor Bernard Ghanem, with expertise in machine learning workflow automation, physics-informed neural operators, and AI for Science, particularly in chemistry applications. Passionate about leveraging AI to solve complex scientific problems.

Ahmad Sait

M.S. Student, Computer Science
Ahmad Sait is a Master's student in the Computer Science program at KAUST. Before joining KAUST, Ahmad obtained his Bachelor's degree in Computer Science from King Abdulaziz University, Saudi Arabia. Ahmad is also a KAUST Academy graduate from the AI track, having been under the supervision of Prof. Naeemullah Khan. The Academy provided him with seven opportunities to assist in teaching students enrolled in subsequent cohorts.

Saleh AlJazzar

Research Fellow at King Abdullah University of Science and Technology (KAUST) .

Muhammad Esmat

Visiting student at, King Abdullah University of Science and Technology (KAUST).

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Abdulkarem Badiea Alshaibani

Visiting Student at the Information Science Lab at the King Abdullah University of Science and Technology (KAUST).
<> mean-field games (7)

Stationary Mean-Field Games with Congestion

-

B2 L5 R5220

Mean-field games MFG are models of large populations of rational agents who seek to optimize an objective function that takes into account their state variables and the distribution of the state variable of the remaining agents. MFG with congestion model problems where the agents’ motion is hampered in high-density regions. First, we study radial solutions for first- and second-order stationary MFG with congestion on R^d. Next, we consider second-order stationary MFG with congestion and prove the existence of stationary solutions. Additionally, we study first-order stationary MFG with congestion with quadratic or power-like Hamiltonians.

On mean-field game price models

-

B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

Two-Scale Homogenization and Numerical Methods for Stationary Mean-Field Games

-

KAUST

Mean-field games (MFGs) study the behavior of rational and indistinguishable agents in a large population. Agents seek to minimize their cost based upon statistical information on the population's distribution. In this dissertation, we study the homogenization of a stationary first-order MFG and seek to find a numerical method to solve the homogenized problem. More precisely, we characterize the asymptotic behavior of a first-order stationary MFG with a periodically oscillating potential. Our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems. Moreover, we prove the existence and uniqueness of the solution to these limit problems. Next, we notice that the homogenized problem resembles the problem involving effective Hamiltonians and Mather measures, which arise in several problems, including homogenization of Hamilton--Jacobi equations, nonlinear control systems, and Aubry--Mather theory. Thus, we develop algorithms to solve the homogenized problem, effective Hamiltonians, and Mather measures.

KAUST Ph.D. students win best paper awards at mean-field games conferences

1 min read ·
By Sonia Turosienski KAUST Ph.D. students David Evangelista and Xianjin Yang, who are supervised by Diogo Gomes, professor of applied mathematics and computational science, won best paper awards at conferences this summer for their work in mean-field game (MFG) theory. Mean-field games model and seek to explain the behavior of rational agents in a competitive environment and have been used in diverse areas of research, including studies on non-renewable resources and mining models; opinion dynamics; price impact and order book modeling; and networks and energy management. Evangelista was

Xianjin Yang

Xianjin Yang's research interests fall on mean-field games, which is an active research area in differential game theories. In his PhD period, Xianjin focuses on the homogenization and numerical methods for stationary mean-field games. Education and Early Career Xianjin Yang got his bachelor's degree in Software Engineering from Chongqing University of China in 2011. Then, he obtained his Master with majors in Computer Science from Zhejiang University of China in 2014. Later on, Xianjin Yang received his second Master's degree in Applied Mathematics from KAUST in 2016. He continues to study at

Diogo Gomes

Professor Diogo Gomes, a distinguished expert in Applied Mathematics and Computational Science at KAUST, leverages advanced mathematics – including partial differential equations, numerical methods and mean-field game models – to solve complex problems in social sciences, economics and finance

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research
<> deep learning (6) <> artificial intelligence (5)

Najmeddine Dhieb

Yasmeen Alsaedy

M.S. Student, Computer Science
She is an AI researcher specializing in generative AI and deep learning. With a strong academic foundation and hands-on experience in machine learning, data science and artificial intelligence, she is passionate about advancing AI applications in real-world scenarios. She has contributed to cutting-edge projects, published research, and collaborated on innovative solutions that bridge the gap between AI theory and practice. Her expertise includes designing and implementing advanced data models, AI-driven visual/text computing, and deep learning architectures.

Yasser Jaber

Visiting Student Researcher ,Machine Learning Engineer at KAUST (King Abdullah University of Science and Technology).
Syed Abidullah Fareed

Syed Abidullah Fareed

Visiting Student (former), Information Science Lab

Ahmed Youssef Ragab Radwan

Visiting Student (former), Information Science Lab
Visiting student at the Information Science Lab, in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST)
<> Computer Vision (4) <> Signal processing (4) <> AI (3) <> MATLAB (3) <> AI4Science (2) <> Asymptotic Performance Analysis (2) <> gamma-convergence (2) <> Generative AI and LLMs (2) <> healthcare (2) <> IoT (2) <> Localization (2) <> modeling (2) <> Network Design (2) <> numerical analysis (2) <> Partial Differential Equations (2)

Mean-field Games and Nonlinear PDE Research Group

Professor Diogo Gomes leads the so-called MFG research group, which stands for Mean-field Games and Nonlinear PDE Research group areas of expertise and current scientific interests: partial differential equations, mean-field models, numerical analysis Professor Gomes’s research interests are in partial differential equations (PDE), namely on viscosity solutions of elliptic, parabolic and Hamilton-Jacobi equations as well as in related mean-field models. This area includes a large class of PDEs and examples, ranging from classical linear equations to highly nonlinear PDEs, including the Monge

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research
<> price formation (2)

On mean-field game price models

-

B9 L2 H1 R2322

Abstract In this talk, we discuss a mean-field game price formation model. This model describes a large number of rational agents that can trade a commodity with an exogenous supply. The price is determined by a balance condition between supply and demand. We discuss the well-posedness of the model, the uniqueness and regularity of the price function. Then, we examine two explicit models - the linear-quadratic problem and a model with finitely many agents. Time permitting, we will examine the connections between this problem and optimal transport with constraints. Brief Biography Diogo Gomes

David Evangelista

David Evangelista is a Ph.D. Candidate at King Abdullah University of Science and Technology (KAUST), working in the areas of partial differential equations, mean-field games (MFG), and market microstructure. He is expect to receive a Ph.D. from KAUST in June 2019, under the direction of Professor Diogo A. Gomes. David's research interests are in optimal control theory, nonlinear partial differential equations, MFG, and market microstructure. MFG is a framework to study interactions of a large number of indistinguishable players that play differential games. MFG has become an active research
<> Programming language (2) <> 5G networks (1) <> 6g wireless systems (1) <> Action Recognition (1) <> Action understanding (1) <> AI for healthcare (1) <> applied mathematics (1) <> Applied Partial Differential Equations (1) <> beamforming (1) <> biomechanics (1) <> Biomedical Signal Processing (1) <> bioscience (1) <> biotechnology (1) <> BLE communication (1) <> Cellular Networks (1) <> cloud- and fog-radio access networks (1) <> communication (1) <> communication networks (1) <> communication systems (1) <> Compressed Sensing Approach (1) <> computational methods (1) <> computer networks (1) <> Continual Learning (1) <> Cooperative networks (1) <> cross-layer optimization (1) <> data analysis (1) <> Data Analyst (1) <> Deep generative models (1) <> development (1) <> differential game theories (1) <> Digital Communications (1) <> Digital signal processing (1) <> Dynamic games (1) <> Dynamical Systems (1) <> Electron Transport (1) <> embedded systems (1) <> Embedded systems. (1) <> energy efficiency (1) <> fluid dynamics (1) <> free boundary problems (1) <> game theory (1) <> GSM (1) <> Hamilton-Jacobi equations (1) <> haps (1) <> interference management (1) <> intrusion detection (1) <> linear-quadratic problem (1) <> Liquid Crystals (1) <> living systems (1) <> market microstructure (1) <> Mathematical modeling (1) <> mathematics (1) <> mean-field models (1) <> Medical imaging (1) <> microelectronics (1) <> Microgrids (1) <> Multi-carrier Systems (1) <> Multimodal learning (1) <> neural network (1) <> Neural Networks (1) <> non-linear partial differential equations (1) <> Non-Terrestrial Networks (1) <> nonlinear PDEs (1) <> nr-u (1) <> Optical communications (1) <> optical fiber (1) <> optical frequency combs (1) <> optimal control theory (1) <> optimal execution (1) <> optimal transport with constraints (1) <> optimization (1) <> Power electronics (1) <> prototyping (1) <> Python (1) <> quantum-dot lasers (1) <> Reinforcement Learning (1) <> Renewable integrations (1) <> Renewable-based power systems (1) <> scientific computing (1) <> seismic processing (1)