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Nazar AliView profile

Associate Professor

• Antennas
• RF Circuits and Systems
• Power Amplifiers
• +2 more

Dr. Nazar Ali serves as an Associate Professor in the Department of Electrical Engineering at Khalifa University, Abu Dhabi, UAE. With over three decades of academic experience since his 1990 PhD, he maintains active roles in research, teaching, and professional leadership within the electrical engineering domain. His educational foundation includes: PhD in Electrical Engineering, University of Bradford, UK (1990) BSc in Electrical Engineering, University of Mosul, Iraq (1984) Research spans cutting-edge RF engineering with emphasis on antenna design, high-efficiency power amplifiers, and indoor localization systems. His pioneering integration of Artificial Intelligence into traditional RF frameworks addresses modern wireless communication challenges, focusing on system optimization and next-generation wireless solutions. This interdisciplinary approach bridges hardware engineering with machine learning methodologies to enhance signal processing and device performance. Professional distinctions include: Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) Internationally recognized Chartered Engineer certification Dr. Ali has mentored multiple MSc and PhD candidates through thesis supervision while teaching core electrical engineering curriculum including Applied Electromagnetics (ECCE320), Electric Circuits II (ECCE222), Electronic Circuits & Devices (ECCE312), and Microwave Circuits and Devices (ECCE316). His pedagogical approach combines theoretical rigor with practical laboratory implementation to develop industry-ready engineering competencies.

Maher MaaloufView profile

Associate Professor

• Machine Learning
• Data Mining
• Multivariate Statistics
• +3 more

Dr. Maher Maalouf serves as an Associate Professor in the Department of Industrial and Systems Engineering at Khalifa University, where he has been a faculty member since 2011. He holds a PhD in Industrial Engineering from the University of Oklahoma and teaches core courses including Six Sigma Methodology and Applications (ISYE 445), Business Analytics (ESMA 610), and Advanced Business Analytics (ESMA 711). His academic credentials include: PhD in Industrial Engineering, University of Oklahoma (USA) MSc in Industrial Engineering, University of Oklahoma (USA) BBA in Management Information Systems, University of Oklahoma (USA) Dr. Maalouf's research integrates advanced analytical methodologies with industrial applications, focusing on classification and regression algorithms in machine learning, statistical process optimization through Lean Six Sigma frameworks, and operational excellence implementations. His work bridges theoretical data science with practical business process improvement across manufacturing and service sectors. He maintains active research supervision through the Socio-Technical Systems Lab, currently mentoring PhD students Toheeb Olajide Salahudeen and Kehinde Ganiyu Ismaila alongside Research Associate Assia Chadly. No scientific awards were documented in the source material.

Andreas BenderView profile

Professor

• Drug Discovery
• Artificial Intelligence
• Cheminformatics
• +7 more

Prof. Andreas Bender is a Professor for Machine Learning in Medicine at the Department of Medicine, Khalifa University, Abu Dhabi. He focuses on integrating heterogeneous chemical, biological, and medical data for clinically relevant decision-making. PhD, University of Cambridge Diplom-Chemiker, University of Frankfurt His research interests include Drug Discovery , Artificial Intelligence , Cheminformatics , and Computational Biology , particularly in the context of life science data analysis and AI/ML applications. He has been involved in founding biotech companies such as Healx Ltd., PharmEnable Ltd., and Pangea Bio. His work bridges academic research and translational drug discovery, with a focus on in silico methods and solubilizing difficult-to-drug targets.

Shihab JimaaView profile

Associate Professor

• AI-based performance enhancement of LP-WAN technologies for IoT
• 6G wireless communications
• Atrial Fibrillation Data Analysis and Validation
• +3 more

Dr. Shihab Jimaa is an Associate Professor at the Department of Computer and Information Engineering , Khalifa University, and currently serves as the Associate Dean of UG Studies at the College of Computing and Mathematical Sciences. He has held academic positions including Research Fellow, Senior Lecturer, and Assistant Professor at institutions such as Loughborough University, Warwick University, and Queen Mary University of London. PhD : Digital Communications, Loughborough University (1990) MSc : Digital Communication Systems, Loughborough University (1986) PGCE : Post Graduate Certificate in Higher Education, University of Hertfordshire (1998) Dr. Jimaa’s research focuses on energy-efficient wireless communication systems , AI-driven 6G technologies , and machine learning applications for early detection of Atrial Fibrillation . His work spans adaptive filtering, channel estimation, and signal processing in both telecommunications and biomedical engineering domains. Current projects include optimizing LP-WAN technologies for IoT scalability and developing deep learning-based scheduling techniques. His research also investigates terahertz communications , optical wireless networks , and ultra-massive MIMO for next-generation systems. He has contributed to 3 book chapters and over 100 conference/journal papers. IEEE Senior Member HEA Fellow Recipient of 1 patent Dr. Jimaa has supervised 30 MSc, 3 MPhil, and 4 PhD students . He actively participates in academic leadership as TPC Chair for IEEE conferences and previously served as Guest Editor for the International Journal of Communication Systems . Affiliated centers include the 6G Research Center and Center for Cyber-Physical Systems .

Omar AlhusseinView profile

Assistant Professor

• Network Automation and Intelligence
• Network Resource Management and Optimization
• AI and Networking
• +2 more

Dr. Omar Alhussein serves as an Assistant Professor in the Department of Computer Science at Khalifa University. Previously, he worked as a Senior Research Engineer at Huawei Technologies Canada (2020-2023), where he developed machine-learning solutions for networks and filed six patent applications. His educational qualifications include: Ph.D. in Electrical and Computer Engineering, University of Waterloo (2020) M.A.Sc. in Engineering Science, Simon Fraser University (2015) B.Sc. in Electrical and Computer Engineering, Khalifa University (2013) Research focuses on the convergence of networks and artificial intelligence: Network Automation and Intelligence Network Resource Management and Optimization AI and Networking NFV/SDN Wireless Networks He employs interdisciplinary methods to advance network intelligence through fundamental research principles. Scientific recognition includes: Outstanding Individual Award (Huawei Technologies) Quality Research Award (Huawei Technologies) Dr. Alhussein mentors PhD candidates and holds six patent applications for probabilistic/distributed AI in next-generation networks and AI trustworthiness. He actively recruits PhD students for ongoing research. He maintains active affiliations with Khalifa University's 6G Research Center and Center for Cyber-Physical Systems.

Abderrahim OulhajView profile

Professor

• Biostatistics
• Epidemiology
• Public Health
• +10 more

Abderrahim Oulhaj serves as a Professor of Biostatistics and Epidemiology in the Department of Public Health and Epidemiology at Khalifa University, bringing over 28 years of academic and research experience. His prior roles include eight years as Associate Professor at United Arab Emirates University (UAEU) where he directed the PhD program in Public Health, and nearly a decade as a senior statistician at University of Oxford leading projects like OPTIMA and EXSCEL. His educational background includes: PhD in Statistics (2003) from the Institute of Statistics, Biostatistics and Actuarial Sciences, UCL, Belgium Master degree in Statistics (1997) from the Institute of Statistics, Biostatistics and Actuarial Sciences, UCL, Belgium Prof. Oulhaj specializes in statistical modeling for infectious and chronic diseases—including diabetes, cardiovascular disorders, cancer, neurodegenerative conditions, COPD, and COVID-19—with methodological expertise in advanced survival analysis, risk prediction modeling, longitudinal data analysis, and joint modeling of repeated measures with time-to-event data. His work increasingly integrates artificial intelligence, machine learning, and deep learning into healthcare analytics. He has received multiple research excellence awards from UAE University and Khalifa University, alongside three international patents and copyrights focused on Alzheimer’s and cardiovascular diseases. Prof. Oulhaj directed UAEU's PhD program in Public Health and currently leads the KU-SEHA Clinical Research Certificate Program training 150+ medical professionals across Abu Dhabi. His grant leadership encompasses the EU-funded POCCardio project on cardiovascular diseases and the European Society of Cardiology's Cardiovascular Risk Collaboration Unit, with significant contributions to UAE national committees for therapeutic trials, epidemiology, diagnostics, and cardiovascular prevention guidelines. He operates within international research frameworks through POCCardio and the European Society of Cardiology, while maintaining strategic ties with the UAE Department of Health and SEHA for clinical research capacity building.

Muhammad Muzammal NaseerView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +5 more

Dr. Muhammad Muzammal Naseer serves as an Assistant Professor in the Computer Science department at Khalifa University's College of Computing and Mathematical Sciences. Previously, he held research positions at Data61 (CSIRO), Inception Institute of Artificial Intelligence, and Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) from 2018 to 2024. His academic background includes: Ph.D. in Engineering & Computer Science, Australian National University (ANU), 2022 M.Sc. in Electrical Engineering, King Fahd University of Petroleum & Minerals (KFUPM) B.Sc. in Electrical Engineering, University of the Punjab (PU) His research specializes in robust intelligent systems through adversarial machine learning for AI behavior explanation, self-learning methodologies (self-supervision, self-distillation), and multimodal large language models. Key application domains include healthcare security and image/video understanding, with publications in top venues like CVPR, NeurIPS, and TPAMI. Dr. Naseer teaches Artificial Intelligence (COSC604), Introduction to Machine Learning (COSC434), and Machine Vision and Image Understanding (COSC606). He is affiliated with the Center for Cyber-Physical Systems Research and actively recruits Ph.D. students for his research group focusing on AI robustness and security.

Mohammad AlsharidView profile

Assistant Professor

• Biomedical Image Analysis
• Computer Vision
• Natural Language Processing
• +6 more

Dr. Mohammad Alsharid is an Assistant Professor in Computer Science at Khalifa University, specializing in biomedical image analysis, computer vision, and machine learning. He holds a DPhil in Engineering Science from the University of Oxford (2022) and a BSc in Computer Engineering with highest honors from Khalifa University. Education DPhil (Oxon) in Engineering Science, University of Oxford, UK BSc in Computer Engineering, Khalifa University, UAE Research Interests Biomedical Image Analysis Natural Language Processing Computer Vision Machine Learning Healthcare AI Ultrasound Analysis Scientific Awards Best Presentation Award at ASMUS workshop of MICCAI

Tamer Mohamed Abdellatif MohamedView profile

Assistant Professor

• Applied Artificial Intelligence (AI)
• Machine Learning
• Information Retrieval (IR)
• +2 more

Dr. Tamer Mohamed Abdellatif Mohamed is an Assistant Professor and Program Coordinator at the Canadian University Dubai , affiliated with the School of Engineering, Applied Science and Technology and the Department of Computer and Networking Engineering Technology . He holds a PhD in Electrical and Computer Engineering from the University of Western Ontario, Canada, and a professional certificate in STEM teaching at the university level from the same institution. Education : PhD in Electrical and Computer Engineering (University of Western Ontario, Canada); STEM Teaching Certificate (University of Western Ontario). His research expertise spans applied artificial intelligence (AI) , machine learning , information retrieval (IR) , software analytics , and mining software repositories . With over 20 years of industry experience at multinational corporations like Hewlett-Packard Enterprise, he contributes practical insights as an experienced Project Manager and Software Practitioner. Dr. Mohamed has published extensively in top-tier journals and conferences. Prior leadership roles include Vice President of Professional Development at PMI, London Chapter and Treasurer at IEEE Young Professional, London Chapter . He is based at the university's Hub G,10 location, with contact via tamer.mohamed@cud.ac.ae or +971(04)7096822.

Sherif MoussaView profile

Professor

• Wireless Communication
• Computer Networks
• Machine Learning
• +1 more

Dr. Sherif Moussa is a Professor and Dean at the Faculty of Engineering, Applied Science and Technology, Canadian University Dubai. He holds a PhD in Electrical and Computer Engineering from the University of Quebec and an MSc from the University of Waterloo, both in Canada. Before joining CUD in 2007, he served as a lecturer at Centennial College in Toronto. His research focuses on: Wireless Communication Computer Networks Machine Learning Digital Circuit Design His recent publications explore machine learning applications in cybersecurity, MIMO-OFDM systems, FPGA implementations, and routing protocols, reflecting a consistent emphasis on optimizing wireless and network technologies. Trends show increasing work in AI-driven solutions and intrusion detection since 2018. He received the 2015 CUD Research Excellence Award and founded the university's first robotics club. He actively contributes as a reviewer and technical committee member for international conferences.

Aamir Younis RajaView profile

Assistant Professor

• Medical Physics
• Radiation Physics
• Medical Imaging Physics
• +8 more

Dr. Aamir Younis Raja is an Assistant Professor in the Physics Department at Khalifa University, UAE, where he co-founded the Medical Physics wing and co-developed the accredited M.Sc program in medical physics. He has held academic roles including Senior Research Fellow at the University of Otago, Visiting Research Fellow at the University of Canterbury, and Visiting Academic Teaching Staff at ARA Institute of Canterbury. His research focuses on radiation physics , medical imaging physics , and spectral photon-counting CT applications in bone/cartilage health , metal implant characterization , cancer imaging , atherosclerosis , and arthritis . Education: PhD in Medical Physics (University of Canterbury, 2013), M.Sc in Applied Physics (UET Pakistan, 2006), B.Sc in Physics & Mathematics (University of the Punjab, 2004) Dr. Raja’s work combines nanoparticle technology with low-dose multi-energy CT to identify non-toxic contrast agents, develops machine learning-based radiation monitoring tools , and pioneers AI-driven artefact reduction in CT imaging. His projects include collaborations with international institutions on material decomposition algorithms and biomedical applications of spectral CT. Notable scientific recognition includes being a Fellow of the Union for International Cancer Control and securing the Khalifa University Faculty Startup Grant . He has supervised over 20 thesis students across University of Otago, University of Canterbury, and Khalifa University, including PhD candidates working on material reconstruction software , parametric color imaging , and machine learning for CT artefact reduction .

Alessandro DecarlisView profile

Assistant Professor

• Tectonics
• Stratigraphy
• Rifted Margins
• +3 more

Dr. Alessandro Decarlis serves as an Assistant Professor in the Department of Earth Sciences at Khalifa University, Abu Dhabi, UAE, with a field-oriented focus on sedimentation-tectonics interactions and rifted margin evolution. His work bridges geological observations and deep tectonic processes through multi-scale analysis. Education Master's Degree in Geology: Universita' degli Studi di Pavia (Italy) PhD in Earth Sciences: Universita' degli Studi di Pavia (Italy) Research Interests Dr. Decarlis investigates the structural dynamics of rifted margins and sedimentary-tectonic interplay, emphasizing integration of field data with seismic observations to model crustal evolution. His methodology prioritizes up- and down-scaling of geological frameworks for comprehensive basin analysis. Tectonics Stratigraphy Rifted Margins Sedimentation Crustal Structure Basin Analysis Affiliations He is actively engaged with the Research and Innovation Center on CO2 and Hydrogen Research at Khalifa University. Grants & Projects 2022-2026: CIRA Grant, UAE (CoI) - New approaches to assess thermal evolution in sedimentary basins using Raman spectroscopy and machine learning. 2022-2024: Khalifa University/ADNOC project (CoI) - Textural characterization of surficial sand deposits for proppant applications. 2022-2024: Khalifa University/ADNOC project (PI) - Low-cost permanent CO2 storage via subsurface mineralization. Student Supervision Dr. Decarlis mentors two PhD students: Mohamed Abdelkawi Elfadly and Abdul-Naaza Nuhu Alhassan Nuhu. Scientific Awards No awards, fellowships, or medals are documented in the provided text.

Randa AsadView profile

Associate Professor

• Astrophysics
• Galaxy Evolution
• Chemical Enrichment
• +3 more

Dr. Randa Asad serves as Associate Professor in the Department of Physics at the College of Arts and Sciences, American University of Sharjah (AUS) since 2021, specializing in astrophysical research on galaxy evolution through star cluster analysis. Her work integrates observational astronomy with advanced computational techniques to decode chemical enrichment histories across cosmic time. Education background: PhD in Physics, University of Cincinnati, United States (2012) MS in Physics, University of Cincinnati, United States (2008) BS in Physics, Jordan University of Science and Technology, Jordan (2005) Her research program centers on spectroscopic analysis of star clusters to reconstruct galactic chemical evolution, employing full-spectrum fitting methodologies and machine learning algorithms. She has developed the ASAD (Analyzer of Spectra for Age Determination) framework and secured observing time at premier facilities including VLT, Gemini, and SOAR telescopes. International collaborations include visiting researcher appointments at Harvard-Smithsonian Center for Astrophysics, Space Telescope Science Institute, and ESO headquarters. Publication trends reveal sustained focus on stellar population analysis since 2014, with recent works (2022-2024) emphasizing machine learning applications for abundance measurements in clusters like NGC 1856. Earlier research established foundational methodologies for age determination through integrated light spectroscopy, consistently targeting the Large Magellanic Cloud as a key laboratory for galaxy evolution studies. Scientific recognition: CAS Excellence in Research Award, AUS (April 2022) Dr. Asad has secured significant observational resources at major telescopes worldwide while maintaining active membership in the International Astronomical Union (since 2016). Her research group conducts observational campaigns to unravel stellar population complexities through spectral analysis, contributing to international efforts in galactic archaeology. The Star Clusters Research Group she leads bridges theoretical modeling with empirical data to decode star formation histories across diverse galactic environments.

Yousof AlhammadiView profile

Associate Professor

• Information Security
• Intrusion Detection
• Botnet Detection
• +6 more

Dr. Yousof Alhammadi is an Associate Professor at Khalifa University of Science and Technology, affiliated with the Department of Electrical and Computer Engineering. His research focuses on Information and Network Security, with expertise in intrusion detection, botnet/virus detection, RFID security, blockchain, and mobile security. He also teaches courses in Linear Algebra, C++ Programming, and Network/Computer Security. Education: Ph.D. in Computer Science (University of Nottingham, UK, 2009) M.Sc. in Telecommunications Engineering (University of Melbourne, Australia, 2003) B.Eng. in Computer Engineering (Etisalat College of Engineering, UAE, 2000) His research team includes Post-Doctoral Fellows Dr. Khouloud Eledlebi (Cyber Physical Systems) and Dr. Lamees Mahmoud AlQassem (Electrical and Computer Engineering), along with graduate student Al Anoud Almemari.

Aamna AlShehhiView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Big Data Analytics
• +7 more

Dr. Aamna AlShehhi serves as an Assistant Professor in the Department of Biomedical Engineering and Biotechnology at Khalifa University, where she has pioneered AI-driven healthcare solutions since joining in 2020. Her interdisciplinary expertise bridges electrical engineering, computer science, and clinical medicine through innovative research collaborations with MIT, Imperial College London, and Novartis. Her educational foundation includes: Bachelor's in Software Engineering, United Arab Emirates University (2009) Master's in Computing and Information Science, Masdar Institute (MIT collaboration) (2013) PhD in Interdisciplinary Engineering, Masdar Institute (MIT collaboration) (2017) Dr. AlShehhi's research centers on artificial intelligence for healthcare transformation , with emphasis on neurodegenerative disease prediction, rare disease diagnostics, and wearable-based gait analysis. She develops constrained deep learning models for Alzheimer's biomarker discovery, self-supervised frameworks for emotion recognition using smartphone data, and physics-informed neural networks for biomechanical analysis. Her work integrates multimodal data including fMRI, genomic sequences, and real-world behavioral metrics to address critical gaps in dementia screening, cancer detection, and mental health monitoring. Analysis of her 2023-2025 publications reveals three dominant trends: (1) Explainable AI architectures for clinical decision support, particularly constrained graph neural networks analyzing comorbidity networks in Alzheimer's; (2) Multimodal sensor fusion systems combining wearables, keystroke dynamics, and fMRI for neurological assessment; (3) Self-supervised learning approaches enabling robust performance with limited medical data. Her work consistently targets real-world clinical deployment through portable systems like smart gait analyzers and remote depression screening tools. Dr. AlShehhi actively mentors the next generation of biomedical engineers: Ferial J. Abuhantash (Ph.D. candidate) Khalid Ahmed Abdulla (Master's student) Her research is supported through strategic partnerships with Novartis Institute for Biomedical Research and collaborative grants enabling AI-driven drug repurposing for dementia and genomic cancer detection. She leads projects analyzing skin lesions for melanoma screening and developing deep learning models for early-stage cancer detection using genomic data. As a core member of Khalifa University's Healthcare Engineering Innovation Group , she directs a multidisciplinary team including Postdoctoral Fellow Dr. Sherlyn Jemimah and Research Assistant Chahd Maher Musthafa Chabib. Current initiatives focus on creating deployable AI systems for clinical settings, with ongoing work on portable gait assessment tools for rehabilitation centers and real-time emotion recognition platforms for mental health monitoring.