Reinforcement Learning

Control and Estimation Designs Using Model-Based and Model-Free Approaches for Water Quality Monitoring in Process Systems

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B1 L4 R4214

This dissertation develops and evaluates advanced control and estimation strategies to address complex dynamics and measurement limitations in water-related applications, specifically optimizing fish growth in aquaculture and estimating bacterial concentration in wastewater treatment plants.

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering
Kaiqiang Lin is currently a Postdoctoral Research Fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), working with Professor Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Kaiqiang Lin received his B.Eng. degree in Electronic and Information Engineering from Fujian Agriculture and Forestry University, Fuzhou, China, in 2018, and his Ph.D. degree with the College of Surveying and Geo-Informatics Tongji University, Shanghai, China, in 2024. From 2021 to

Mohammed Alwagait

Mohammed is a PhD Student in the Electrical and Computer Engineering program (ECE) at KAUST. He focuses on Reinforcement Learning Based Energy Saving Path Planning and Control for UAVs.

Aznaur Aliev

M.S. Student

Fahad Aljehani

Designing advanced control algorithms and estimation techniques to optimize water quality systems, specifically aquaculture and wastewater treatment, blending control theory with cutting-edge AI and machine learning to enhance performance, sustainability, and cost-efficiency.

We used Reinforcement Learning; but did it work?

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.

Inference for Longitudinal Data After Adaptive Sampling

Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

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B9 L2 H2

In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.

Thoughts About Machine Learning - Week 7

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B9 LH1 R2322

The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence.

Thoughts About Machine Learning - Week 8

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B9 H1 R 2322

The AI Initiative, Electrical, and Mathematical Sciences and Engineering (CEMSE) Division are delighted to announce the resumption of a series of seminars titled “Thoughts About Machine Learning". The seminars will be delivered in person by Jürgen Schmidhuber, Director of the KAUST AI Initiative, and will run weekly during the spring semester. The program will start with aspects of the theory of computation and delve into many topics not typically covered in a deep learning course. This is a truly unique opportunity to learn from one of the founders in artificial intelligence. Attendance is

Rihan Huang

M.S. Student, Computer Science

Mohammad Aljohany

Education 2021 - Present Ph.D. Student in Electrical and Computer Engineering, King Abdullah University of Science and Technology, KSA M.S. in Electrical and Computer Engineering , KAUST, KSA B.S. in Instrumentation and Control Engineering, Yanbu Industrial College, KSA Areas of Expertise and Research Interests Control Theory Intelligent Systems Automation Reinforcement Learning

Fares Fourati

Fares Fourati is a Ph.D. student at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Mohamed-Slim Alouini. Education Fares Fourati has studied mathematics and physics at the University of Sfax and has been among the top 1% in the Tunisian national contest of mathematics and physics in 2017. In 2020, he received his diplôme d’ingénieur with first-class honors in multidisciplinary engineering from Ecole Polytechnique de Tunisie. In 2022, he received his Master of Science in electrical and computer engineering from KAUST. Early Career Before

Deyao Zhu

Ph.D. Student, Computer Science
Deyao Zhu is a Computer Science Ph.D. student at the Visual Computing Center (VCC) in King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Mohamed Elhoseiny in the Vision-CAIR Group. Education and Early Career Deyao obtained his Master's degree in Electrical Engineering and Information Technology at Gottfried Wilhelm Leibniz Universität Hannover, Germany. His Master thesis is done in Max Planck Institute for Intelligent Systems which focuses on representation learning. Before that, he received his Bachelor’s degree in Mechatronics at Tongji University

Chang Shu

Education 2021.08 - Present M.S. in Electrical Engineering, KAUST, KSA 2017.09 - 2021.06 B.Eng in Electrical Engineering, UESTC, China 2017.09 - 2021.06 B.Eng in Electrical Engineering, University of Glasgow, UK Honors and Awards ​2020.11 China National Scholarship 2019.04 Meritorious Winner in the 2019 Interdisciplinary Contest in Modeling (ICM) Areas of Expertise and Research Interests Reinforcement Learning Robotic Assembly Manipulation Planning

Aniq Ur Rahman

Aniq is an MS/PhD student under the supervision of Prof. Mohamed-Slim Alouini at the Communication Theory Lab (CTL), KAUST. Education and Early Career Aniq obtained his bachelor’s degree in Electronics and Communication Engineering from National Institute of Technology Durgapur, India in 2019. Upon graduation, he joined Qualcomm as an Associate Systems Engineer. In 2018 summers, he participated in the ALICE experiment as a research intern at the European Organization for Nuclear Research (CERN), Switzerland. Prior to joining KAUST, he worked as a Project Assistant at IISc 5G Testbed, Indian