Reinforcement Learning

Reinforcement Learning and Optimization in Large Action Spaces under Limited Feedback

Fares Fourati, Ph.D. Student, Electrical and Computer Engineering

Apr 29, 15:00 - 16:45

B2 R5209

Reinforcement Learning machine learning combinatorial multi-armed bandits large action spaces limited feedback efficient exploration submodular optimization black-box optimization global optimization

This dissertation develops theoretical foundations and scalable algorithms for reinforcement learning and optimization in large decision spaces under limited feedback.

Ansar Khangeldin

M.S. Student

Computer Vision Reinforcement Learning Multi-agent systems autonomous systems

Ms/PhD Electrical and Computer Engineering student. Have a background in Applied Mathematics, Control Theory, Machine Learning Research and Development, Multi-Agent Reinforcement Learning. Main research interests are application and enhancement of CV methods in autonomous systems (AVs, Robotics, UAVs).

Echo Ziyi Yang

Ph.D. Student

Autonomous Agent machine learning Reinforcement Learning

Kaiqiang Lin

Postdoctoral Research Fellow, Electrical and Computer Engineering

Wireless Underground Sensor Networks LoRAWAN Space-Air-Ground-Underground Integrated Networks Wireless Energy Transfer Reinforcement Learning

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

Ph.D. Student, Electrical and Computer Engineering

UAV adaptive control Reinforcement Learning Aerial Robotics

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

AI Deep learning Computer Vision Reinforcement Learning

Fares Fourati

Ph.D. Student, Electrical and Computer Engineering

machine learning Deep learning Reinforcement Learning artificial intelligence combinatorial multi-armed bandits intelligent systems black-box optimization

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.

Aniq Ur Rahman

M.S., Electrical and Computer Engineering

Stochastic Geometry Reinforcement Learning wireless network

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