Topics

<> machine learning (25)

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Jonathan Klein

Research Scientist, Computer Science
My fields of research interests include computational simulation, plant simulation and modeling, synthetic training data, machine learning architectures, none-line-of-sight imaging, and computational photography.

Yibin Wang

Keep Moving forward

Andrea Vanessa Rocha

Research Specialist, Statistics
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

-

B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

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).

Waleed Abu Laban

Software Developer, Repli5
Waleed Abu Laban is a M.S./Ph.D. candidate at the KAUST Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil. Before joining KAUST, Waleed obtained a bachelor's degree in electrical engineering from The University of Jordan. Research Interests Waleed's research interests include signal processing and machine learning.

Kamilya Smagulova

Postdoctoral Research Fellow (former), Communication and Computing Systems Lab
Education and early career Kamilya received her bachelor degree in Radio Engineering and Telecommunications from Almaty University of Power Engineering and Telecommunications (2008) and masters degree in Nanotechnology from University College London (2011). In 2021 she obtained her PhD degree in Electrical and Computer Engineering from Nazarbayev University. Currently Kamilya is a postdoctoral fellow at Communication and Computing Systems Lab (CCSL) under supervision of Prof. Ahmed Eltawil. Her research interests lie primarily in the area of machine learning and resistive hardware accelerators

Ayan Shymyrbay

Ayan Shymyrbay is a Ph.D. candidate in the electrical and computer engineering program at the King Abdullah University of Science and Technology (KAUST).

Abdullah Adil Albakry

Senior Associate Consultant, Bain & Company
Education and early career: I graduated with my bachelor’s degree in Electrical and Computer engineering with a mathematics minor from North Carolina State University (NCSU) in the United States. Now, I am a MS/PhD student at KAUST supervised by Dr. Ahmed Eltawil. Research Interest: Machine Learning and Communication Education Profile B.Sc., Electrical and Computer Engineering, North Carolina State University, United States, 2020.
<> Deep learning (15)

Yimeng Chen

Postdoctoral Research Fellow, Computer Science
Yimeng Chen is a Postdoctoral Researcher in Prof. Jürgen Schmidhuber's research group at King Abdullah University of Science and Technology (KAUST).

Zakaria El Kassimi

KAUST offers a unique environment where applied mathematics and data science meet real-world impact, with exceptional resources and a supportive community for ambitious research.

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Juan Pablo Muñoz Díaz

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.

Mhd. Emad Alolaby

Sixiu Liu

Ph.D. degree in Earth science and engineering with the King Abdullah University of Science and Technology (KAUST)

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 (11)

Nanbo Li

Postdoctoral Research Fellow, Computer Science
Nanbo Li is a Postdoctoral Fellow working in Prof. Jürgen Schmidhuber research group at the King Abdullah University of Science and Technology (KAUST) Center of Excellence for Generative AI.

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
KAUST-CEMSE-CS-VISION-CAIR-Dalal-Aldossary

Dalal E. Aldossary

M.S. Student, Computer Science
Dalal is a Master’s student in Computer Science at KAUST. She is part of the VISION-CAIR and the Neuroscience & AI Lab (NAIL), where she works on developing machine learning models that interpret and learn EEG representations and behavior.

Adonai Vera

Machine Learning Engineer with 6+ years of experience specializing in computer vision, deep learning, and robotics. Led AI solutions for retail analytics, UAV systems, and edge devices. Author, educator, and TensorFlow Google Developer Expert.

Abdelrahman Eldesokey

Postdoctoral Research Fellow
Abdelrahman Eldesokey is a Postdoctoral Fellow at KAUST, working on Generative AI and Computer Vision. His research focuses on diffusion models, multimodal large language models, and vision foundation models, aiming to make generative systems more controllable, interpretable, and reliable.
Najmeddine Dhieb

Najmeddine Dhieb

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.

Aznaur Aliev

M.S. Student

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.

Mojtaba Alshams

<> artificial intelligence (10)

Omar Ahmed

M.S. Student, Bioengineering
Omar Ahmed is an MS student in the Bioengineering department at KAUST. His research explores the mechanisms that enable brain signal generalisability across individuals, addressing how neural activity patterns can be reliably interpreted despite significant inter-subject and temporal variability. A major challenge in neuroscience is that brain activity varies substantially between people and even within the same person over time, which limits the development of universal diagnostic and therapeutic tools. Towards this goal, Omar is developing deep learning models that can identify consistent
Salem Alsaqaf

Salem Al-saqaf

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.
Najmeddine Dhieb

Najmeddine Dhieb

Mohamed Ads

Visiting Student, King Abdullah University of Science and Technology

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)

Jinane Bazzi

Asmaa Abdallah

<> Signal processing (10)

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon
Ahmad Dkhan

Ahmad Dkhan

Visiting Scholar (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology Present -Master's in Electrical and Computer Engineering, American University of Beirut, Lebanon

Saleh AlJazzar

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

Alaa Alameer Ahmad

Research visit to communication theory lab in computer, electrical and mathematical sciences and engineering. KAUST (King Abdullah University of Science and Technology) ,Worked with research groups supervised by Prof. Mohamed-Slim Alouini and Prof. Tareq AL-Naffouri.

Abdullatif Al-Rabah

MS Degree, Computer, Electrical and Mathematical Sciences and Engineering at King Abdullah University of Science and Technology (KAUST).

Waleed Abu Laban

Software Developer, Repli5
Waleed Abu Laban is a M.S./Ph.D. candidate at the KAUST Communication and Computing Systems Lab under the supervision of Professor Ahmed Eltawil. Before joining KAUST, Waleed obtained a bachelor's degree in electrical engineering from The University of Jordan. Research Interests Waleed's research interests include signal processing and machine learning.

Ahmed Eltawil

Professor Eltawil is an expert in advanced wireless communications and energy-efficient computing architectures, with a particular focus on low-power mobile systems, machine-learning platforms, sensor and body area networks. His work bridges academic research and industry, translating innovative ideas into impactful technologies and successful startups.
<> wireless communication (10)

Polar Codebook Design for Near-Field Communication

As sixth-generation (6G) wireless networks approach, leveraging the mmWave and THz bands' abundant spectrum becomes crucial, promising ultra-high data rates and enabling immersive communication experiences. This transformation, characterized by the integration of ultra-massive MIMO (UM-MIMO) systems, facilitates significant increases in throughput and capacity by utilizing densely packed antenna arrays. The resulting shift from traditional far-field communication, with its planar wavefronts, to near-field communication, where spherical wavefronts predominate, necessitates a reevaluation of
System overview of the RIS-aided indoor localization framework using RSSI and deep learning for precise positioning in NLoS environments.

RIS-Aided mmWave Indoor Localization

Deep Learning Framework for RSSI-Based Indoor Localization in RIS-Aided mmWave Systems. We present a novel deep learning framework for indoor localization in millimeter-wave (mmWave) environments using received signal strength indicator (RSSI) measurements from a reconfigurable intelligent surface (RIS)-aided system. Our dual-stream orientation-gated (DSOG) architecture addresses the critical challenges of non-line-of-sight (NLoS) conditions and orientation variability, achieving unprecedented decimeter-level accuracy with a median error of 0.19 m—outperforming classical methods by 74.7% and
ccsl-multi-target-detection-and-JVAR-estimation

Multi-Target Detection and JVAR Estimation

Integrated sensing and communication has been a key element towards the advancements of next generation wireless communication systems. Moreover, the convergence of radar and communication systems is rapidly gaining momentum as we advance towards beyond fifth-generation (5G) networks. A promising approach involves the utilization of existing 5G new radio 5G-NR control signals for sensing purposes. This approach exploits the periodic nature of such signals to collect environmental data without degrading the communication performance. Synchronization signal block (SSB) is the most popular
Testbed

Multimodal Sensing for Indoor RIS-Aided Beam Selection

In the 5G standard, beam sweeping is typically periodically executed through exhaustive search methods to maintain continuous alignment of user equipment (UE) and base station (BS) beamformers. In a reconfigurable intelligent surface (RIS)-aided massive MIMO (mMIMO) system, beam training is a daunting challenge as RIS codebook sizes are significantly larger compared to regular transceivers. To address the beam-training overhead, deep learning-based solutions have attracted great interest, enabling learning from data and adapting to dynamic conditions. The integration of multimodal sensing

Digital Twin For RIS-Aided Systems

Beyond 5G networks are expected to deliver ultra-reliable, low-latency, and high-capacity connectivity. One of the key enabling technologies for achieving these goals is a reconfigurable intelligent surface (RIS), which is a programmable metasurface composed of many passive elements, each capable of adjusting the phase of incident signals. By intelligently controlling these elements, RIS can manipulate wireless propagation environment to enhance coverage, increase capacity, and improve energy efficiency. To accurately model the behavior of RIS within complex and dynamic environments, digital
digital-twin-for-outdoor-scenario

Digital Twin For Outdoor Scenario

In outdoor scenarios, DTs provide large-scale virtual replicas of urban or rural networks to optimize base station placement, manage mobility, and enhance vehicular or drone communications. These advantages make DTs a powerful tool for improving reliability, efficiency, and adaptability across diverse wireless environments. At CCSL, we investigate two specific DT scenarios for outdoor wireless cellular networks: digital twin–aided beamforming design and digital twin–aided blockage prediction/detection for MIMO systems. Experiment Description Digital Twin (DT) technology enables the accurate
digital-twin-for-indoor-scenario

Digital Twin For Indoor Scenario

In indoor wireless scenarios, Digital Twin enables the accurate modeling of complex environments, such as offices, factories, and smart homes, allowing for the real-time optimization of coverage, interference management, and seamless connectivity for IoT devices. IT supports applications like intelligent building management, immersive AR/VR, and industrial automation. At CCSL, we investigate two specific indoor DT scenarios: digital twin–aided beamforming design and digital twin–aided blockage prediction/detection for MIMO systems. Experiment Description Digital Twin (DT) technology enables
Chengjie zhao

Chengjie Zhao

Visiting Researcher, Information Science Lab
Visiting Student, King Abdullah University of Science and Technology
Almutasem Bellah Enad

Almutasem Bellah Enad

Visiting Researcher (former), Information Science Lab
Visiting Student, King Abdullah University of Science and Technology, Present M.Eng in Electrical and Computer Engineering, American University of Beirut, Lebanon

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

-

B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.
<> cybersecurity (8) <> body area networks (7)
Wearable health monitoring system with edge analytics for real-time diagnostics, emergency alerts, and secure cloud reporting.

On-Body HBC Networking

To advance innovations in next-generation Internet of Bodies (IoB) networks, there is a growing need for miniature, ultra-low-power devices capable of continuous and reliable operation. This need has led to a surge of interest in Human Body Communication (HBC), where nodes exchange messages directly through the human body as a secure and energy-efficient medium. With evidence showing that HBC can achieve up to 100× lower power consumption compared to conventional RF technologies, our research focuses on developing novel communication and networking techniques that facilitate connectivity over

AgriLink: Internet of Plants for Smart Agriculture

Dense vegetation severely attenuates 2.4–5 GHz RF links (additional foliage loss of order 1–3 dB/m, and often >30 dB across canopies), forcing higher transmit power or more relays in plant/greenhouse settings. In contrast, Plant-Body Communication (PBC) utilizes signals within the stem, where ionic pathways and distributed capacitances form a guided, low-radiation medium, thereby reducing path loss and environmental variability. We target two practical outcomes: 1. quantify and model the intra-plant channel across coupling modes (galvanic vs. capacitive), species (herbaceous Dieffenbachia vs
Close-up view of a microfabricated sensor chip used in advanced biomedical and communication systems.

Ultra-Low-Power HBC Transceiver ICs

The CCSL advances next-generation Human Body Communication (HBC) through innovative ASIC implementations that enable ultra-low power, secure, and efficient data transmission using the human body as a communication medium. Our research focuses on silicon-proven solutions that bridge the gap between theoretical HBC concepts and practical wearable/implantable applications. We develop mixed-signal transceivers that support multiple communication modes, adaptive signal processing, and AI-enhanced performance optimization. The lab's ASIC implementations demonstrate >100x energy efficiency
Overview of wearable sensing technologies for comprehensive human health monitoring.

BioLink: Internet of Body via HBC for Healthcare

Human Body Communication (HBC) is an innovative technology that uses the human body itself as a transmission medium for data exchange. This approach enables secure, energy-efficient, and reliable communication between smart health sensors, wearable devices, and medical implants in and around the body. Unlike traditional wireless methods that rely on radio frequency signals prone to interference and privacy risks, HBC offers low power consumption, reduced signal loss, and enhanced data privacy by transmitting signals through direct skin or body tissue contact. This project focuses on advancing

Qi Huang

Education and early career: Qi (Dave) Huang received his Bachelor of Engineering degree in Southwest Petroleum University of China. He is now a master student in Communication and Computing Systems Lab, CEMSE, King Abdullah University of Science and Technology. Before joining KUAST, he was a visiting student of Communication and Computing Systems Lab from June 2021 to August 2021. Research interests: Circuit and system design of body area network, body channel characterization Awards and Distinctions Undergraduate national scholarship of China; Excellent graduate of Sichuan Province; Second

David Lago Cachón

Postdoctoral Research Fellow (former), Communication and Computing Systems Lab
Biography David Lago Cachón received his B.Sc. in Physics, specialization in Electronic, Automatism and Signal Processing in 2008 and his M.Sc. in Science and Technology of Materials in 2010, both from University of Oviedo. He obtained his PhD in Applied Physics in 2016, with qualification Excellent cum laude and proposed to Extraordinary doctorate award. At the same time, he has been highly implicated in Science Communication, given talks in schools, pubs and even a TEDx. Also, he has been interviewed and consulted by local and regional media. With some friends he created an astronomy club

Abdelhay Ali

Postdoctoral Research Fellow, Electrical and Computer Engineering
<> Digital signal processing (7)

Hassan Hizeh

Hassan is an MS/PhD electrical engineering student. He is currently based in the CEMSE division at KAUST, working under the supervision of Prof. Tareq Al-Naffouri.

Harnessing Multi-modal AI and Machine Learning for Next-Generation 6G Networks

-

B9 L2 R2325

This seminar explores how multi-modal AI and large language models can optimize future 6G wireless networks by integrating diverse data sources to enhance reliability, efficiency, and overall performance.

Ahsan Mehmood

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

Mohammad F. Hashem

Senior Consultant, Oliver Wyman

Asmaa Abdallah

Ahmed Nasser Ismail

Postdoctoral Research Fellow, Electrical and Computer Engineering

Ahmed Hussain

<> IoT (7)
Salem Alsaqaf

Salem Al-saqaf

Salem Al-Saqaf is an Electrical and Computer Engineer specializing in semiconductors, electronics and AI. He combines strong academic training with practical industry experience in AI-driven solutions, data science, and system optimization. His work spans applied research and real-world innovation, aiming to advance intelligent systems, enhance semiconductor applications, and develop technologies that bridge academic discovery with industrial impact.

Serine Medjber

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri

Mohammed Amine Labbas

Visiting student at King Abdullah University of Science and Technology (KAUST) under the supervision of Mr. Tareq Al-Naffouri
Ruochen Zhu

Ruochen Zhu

Mohamed Ads

Visiting Student, 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).
<> ISAC (7)
kaust-cemse-ece-ccsl-changhao-he

Changhao He

kaust-cemse-ece-ccsl-yousef-awad

Yousef Awad

kaust-cemse-ece-ccsl-bumin-kagan-yildirim

Bumin Kagan Yildirim

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang 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 Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022

Ahmed Nasser Ismail

Postdoctoral Research Fellow, Electrical and Computer Engineering

Ahmed Hussain

<> Cyber Security (5) <> integrated photonics (5)

Naiqin Bu

KAUST-CEMSE-ECE-IPL-Nikita-Kondratyev

Nikita Kondratyev

Mikhail Mishevskii

Mikhail received the B.S. degree in applied mathematics and physics from Novosibirsk State, Novosibirsk, Russia and received a master's degree with honors from the Skolkovo Institute of Science and Technology (Skoltech). His research interests include: nonlinear optics, integrated photonics, ultrafast lasers, frequency comb generation. During his time at Skoltech participated in the Skoltech Translational Research and Innovation Program industrial project to create a pulsed submicron laser for pumping a single-photon quantum light source. Mikhail awarded with Scholarship of the President of

Aleksandr Movsisian

Aleksandr received his bachelor’s degree in 2023 in Physics from Moscow State University (MSU, ranked first in Russia) and completed a master’s degree in Physics with honors at the same institution. He has over two years of research experience in quantum optics, optomechanics, and quantum measurement theory as part of the LIGO group at MSU. His work has focused on squeezed states of light, measurement topologies, and methods to overcome the Standard Quantum Limit, resulting in two first-author publications in JOSA B and Optics Letters. Aleksandr also gained hands-on experimental experience at

Aram Mkrtchyan

Postdoctoral Research Fellow, Electrical and Computer Engineering
<> AI (4) <> embedded systems (4) <> human body communication (4) <> Photonics and optoelectronics (4)
Mohammed Alotaibi

Mohammed Obidallah Alotaibi

Ph.D. Student, Photonics Laboratory
I am Mohammed Obidallah Alotaibi, joining the KAUST Photonics Lab. I hold an M.S. in Electrical Engineering from the University of Central Florida and a B.S. from Umm Al-Qura University. Previously, I served as an Instrument Engineer at Aramco and currently lecture at Taibah University. I am passionate about advancing photonic technologies in energy and sustainability applications, and I look forward to contributing to the lab’s research in optoelectronics and integrated photonic systems. Previous Work Experience: Instrument Engineer, Saudi Aramco (2010–2012) Teaching Assistant & Lecturer

Yan Wang

Ph.D. Student, Photonics Laboratory
M.S./Ph.D. in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Aug. 2024- present BEng. in Communication Engineering at University of Electronic Science and Technology of China (UESTC), Chengdu, China, Sept. 2019 - Jun. 2024​
Jiahoa Wei profile picture

Jiahao Wei

M.S. Student, Photonics Laboratory
Education Jan 2025 - present, M.S./Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Sep 2020 - Jun 2024, B.S. in Optoelectronic Information Science and Engineering at Southern University of Science and Technology (SUSTech), Shenzhen, China.
Zinan Xiao profile picture

Zinan Xiao

Ph.D. Student, Photonics Laboratory
Education Aug 2024 - present, Ph.D. in Electrical Engineering at KAUST, Thuwal, Saudi Arabia. Oct 2020 - Oct 2021, M.S. in Optics and photonics at Imperial College London, London, UK. Sep 2016 - Jun 2020, B.S. in Optoelectronic information engineering at Chongqing University, Chongqing, China.
<> Python (4) <> 5G and beyond (3) <> haps (3)

Bang Huang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Bang Huang works as a postdoctoral fellow at the Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Mohamed-Slim Alouini in the Communication Theory Lab (CTL). Education and Early Career Bang Huang received the M.S. degree (with honors) in electrical engineering and Ph.D degree in information and communication engineering from the School of Information and Communication Engineering (SICE), University of Electronic Science and Technology of China (UESTC), Chengdu, China, in

Xiaowei Pang

Postdoctoral Research Fellow, Electrical and Computer Engineering
Xiaowei Pang 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 Xiaowei Pang received her B.S. degree in Telecommunication Engineering from Northwestern Polytechnical University, Xi'an, China, in 2018, and her Ph.D. degree in Information and Communication Engineering from Dalian University of Technology, Dalian, China, in 2024. From 2021 to 2022
H

Hussein Al-Shatri

I chose KAUST because of its reputation for excellence in research and its recent emphasis on applied research. With my extensive background in both academic research and industrial development, I am excited to contribute my experience and ambition. My goal is to foster new ideas and develop innovative prototypes in the field of wireless communications, which will undoubtedly enhance people's lives.
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