Profiles

Lucas graduated in Electrical Engineering at the Federal University of Rio Grande do Norte (UFRN), Natal, Brazil, on December 2019. From January to July 2019, he was a visiting student at the Information Systems Lab/KAUST, when he worked on indoor localization systems using acoustic waves. In the following year, he joined KAUST as a MSc. student under the supervision of Dr. Tareq Al-Naffouri. He successfully obtained his MSc. degree in December 2021. His Master's thesis is titled A Bayesian Approach to D2D Proximity Estimation using Radio CSI Measurements, and the continuation of this work led to a journal publication at the IEEE Open Journal of the Communications Society. He is currently a Ph.D. student at the Distributed Systems and Autonomy Group/KAUST, under the supervision of Dr. Shinkyu Park.

Lucas' research interests are multi-agent systems, robotics, and deep learning (especially Multi-Agent Reinforcement Learning).

Luis Vazquez obtained a Bachelor Degree on Robotics and Telecommunication from Universidad de las Americas Puebla, his final thesis work was a review on autonomous control and Natural Language Processing for the Robotics human-machine collaboration and interconnection between digital and physical components.

My research is focused on the effects of physical attacks to a robot sensor in the digital processing of the autonomous process more focused on Autonomous control algorithms for 2D vehicles and drones.

Madi Makin (Graduate Student Member, IEEE) received both his B.Sc. and M.Sc. degrees in Electrical and Computer Engineering from Nazarbayev University, Astana, Kazakhstan. He is currently pursuing a Ph.D. degree in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.

His research interests include wireless communication systems, with a particular focus on reconfigurable intelligent surfaces (RIS), non-orthogonal multiple-access (NOMA) networks, and physical-layer security.

Madi's research interests include wireless communication systems, with a particular focus on reconfigurable intelligent surfaces, non-orthogonal multiple-access (NOMA) networks, and physical-layer security.

Manal A. Alshehri is a Doctoral Candidate in Computer Science at King Abdullah University of Science and Technology (KAUST). She holds a B.S. and M.S. degree in Computer Science from King Abdulaziz University, where she also serves as a lecturer. Her research has been published in leading international conferences, including IEEE Big Data and CIKM.

Her research spans a broad range of artificial intelligence applications, with a focus on enhancing recommendation systems and advancing text mining techniques. She employs cutting-edge AI methodologies to address key challenges such as cold-start problems, user privacy, diversity, and filter bubbles. She is also interested in analyzing user behavior across digital platforms and in leveraging generative large language models to create realistic simulations and automate labor-intensive tasks.

Mario Soto Martinez earned his Bachelor’s degree in Nanotechnology Engineering from ITESO, Guadalajara, Mexico, in 2020. During his studies, he completed an internship at CIIDEP, ITESM Monterrey (2018), where he worked on smart materials for environmental applications. His undergraduate research focused on the fabrication of nanostructured electrodes for biosensing. He later obtained his M.Sc. in Bioengineering from King Abdullah University of Science and Technology (KAUST), Saudi Arabia, in 2022, where he specialized in the microfabrication of thin-film transistors for biosensing applications. Mario’s interdisciplinary background in nanotechnology, materials engineering, and bioelectronics underpins his current research on multimodal sensing systems for point-of-care diagnostics

Mario’s research focuses on the development of multimodal sensing systems for point-of-care disease screening. His approach combines electrochemical and gas sensors to detect biomarkers that indicate disease presence or stage. He integrates materials engineering, microfabrication, electronics, and prototyping to design and optimize these platforms for practical implementation, aiming to advance accessible and reliable healthcare technologies.

Mattia Soldan is a final-year Ph.D. candidate in Electrical and Computer Engineering at KAUST, where he is advised by Prof. Bernard Ghanem. His research lies at the intersection of computer vision and natural language processing, with a focus on scalable and efficient algorithms for semantic video understanding and retrieval. His work spans task-specific deep learning architectures, dataset creation, and efficient visual encoding pipelines. Mattia is passionate about building intelligent systems that connect visual content with language and advancing research that bridges fundamental understanding with practical impact.

Mattia's research focuses on video and language understanding and especially on how to leverage mutual information to solve specific tasks as Single Video Moment Retrieval and Video Corpus Moment Retrieval.

Małgorzata Forystek is a Statistics Master's student at the Bayesian Computational Statistics & Modeling research group  (BAYESCOMP) under the supervision of Professor Håvard Rue. She received her B.Sc. degree in Applied Mathematics from AGH University of Science and Technology in Kraków, Poland, in 2023.

Her research interest is mainly in Bayesian and computational Statistics and Data Science.

Merey Ramazanova is a Ph.D. candidate in computer vision at KAUST, supervised by Professor Bernard Ghanem, with whom she also completed her Master’s degree. Her research focuses on egocentric video understanding, including multimodal modeling, action localization, and test-time adaptation under missing modality conditions. She also completed a research internship at Adobe.

Merey research focuses on data science, computer vision, and machine learning.

Michał Forystek received his B.Sc. degree in Information and Communication Technology from AGH University of Science and Technology in Kraków, Poland, in 2023.

He has a 2 years of experience as a Java Developer working for IG Group in Kraków.

Currently he is a Computer Science Master's student at the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou.

Michał's research involves using Load Altering Attacks to exploit the Load Frequency Control in Power Systems and developing the appropriate countermeasures.

Mohammed is currently a PhD student in Electrical and Computer Engineering program (ECE) at KAUST. He received his Master degree in Advanced Control and Systems Engineering with Distinction from UOM, Manchester. He has experience in academia working as Lecturer and worked as Head of the committee of the Engineering Training Program. He also worked as Project Engineer in Saudi Electrical Company in the power plant project PP13 carried by SEC.

His research interests span various topics in systems and control engineering, Robotics, and fault detection and diagnosis.

Mojtaba AlShams is a Ph.D. student in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST), specializing in distributed machine learning and hardware-aware inference. He holds a Master’s degree from KAUST and a Bachelor’s degree from King Fahd University of Petroleum and Minerals (KFUPM).

Mojtaba has a strong academic foundation in computing systems, embedded design, and machine learning, complemented by hands-on experience in electrical engineering roles. He has also demonstrated impactful leadership through national educational initiatives, effectively combining deep technical expertise with strategic leadership capabilities.

His research interests focus on edge computing paradigms and distributed learning, with an emphasis on hardware-aware, energy-efficient inference across heterogeneous edge devices.

Mritunjay Kumar is a Ph.D. candidate in Electrical Engineering at King Abdullah University of Science and Technology (KAUST), Saudi Arabia, under the supervision of Prof. Xiaohang Li. Before joining KAUST, he earned his Master of Technology from the Indian Institute of Technology (IIT) Dhanbad. His research focuses on developing advanced wide-bandgap semiconductor devices for extreme-temperature power electronics applications, addressing challenges such as threshold voltage instability and high gate leakage currents in GaN HEMTs through innovative materials and gate design techniques.

Mritunjay has made significant contributions to the field of power electronics for extreme temperature, as evidenced by publications in reputed journals such as Applied Physics Letters (APL) and Japanese Journal of Applied Physics (JJAP). His achievements include the development of a high-threshold voltage (7.4 V) enhancement-mode GaN HEMT and the implementation of bi-layer gate stacks for stable operation at temperatures up to 450°C. His work also explores heterogeneous integration, such as combining GaN HEMTs with indium oxide-based driver circuits and gallium oxide transistors, demonstrating stable operation in harsh environments.

Mritunjay’s research interest includes Optoelectronics, and III-Nitrides Power semiconductor devices.

Mufeeda  earned her Bachelor’s degree in Chemistry from the University of Calicut, Kerala, India, in 2020. During her studies, she completed an internship at the Indian Institute of Science (IISc), Bengaluru, where she worked on electrocatalysts for sodium hybrid air batteries. She went on to obtain her M.Sc. in Analytical Chemistry from the National Institute of Technology (NIT), Warangal, India, where she specialized in electrochemical analysis and sensor development. Following her master’s, she worked as a Research Assistant, focusing on electrochemical sensors for biomedical applications with an emphasis on novel two-dimensional materials such as MXenes, and published her work in leading Q1 journals. In 2024, she joined King Abdullah University of Science and Technology (KAUST), Saudi Arabia, as a Ph.D. student in Bioengineering. Her current research focuses on the development of wearable sensors for stress monitoring in plants, integrating interdisciplinary approaches across nanotechnology, bioelectronics, and materials science.

Mufeeda’s research focuses on the development of wearable and implantable electrochemical sensors for real-time stress monitoring in plants. She is exploring various electrode systems, including laser scribed graphene and microneedle arrays integrated platforms, for the highly sensitive and selective detection of reactive oxygen species and other stress biomarkers. By combining Materials engineering and bioelectronics, she aims to create innovative sensing systems that provide direct, in vivo insights into plant physiological status, contributing to sustainable agriculture. Beyond plant monitoring, Mufeeda is also interested in extending her research toward the development of advanced sensing platforms for biomedical applications, leveraging similar interdisciplinary strategies to address challenges in healthcare diagnostics.