Elhadj Benkhelifa is a full Professor of Computer Science and Digital Innovations at Staffordshire University, leading the Head of Professoriate role to advance university strategies. He founded the Smart Systems, AI and Cybersecurity Research Centre (SSAICS), directing 15 staff and 23 PhD students. Previously, he held roles at Cranfield University and the University of the West of England. His interdisciplinary research focuses on cybersecurity, cloud/edge computing, bio-inspired systems, and AI applications in healthcare and digital transformation. He is a Visiting Professor at Universities of Suffolk, Lyon3, and Paris 8, and chairs the IEEE UK&I Section's Education & STEM office. Education includes a PhD in Artificial-Life (Bio Inspired Evolvable and Self-Healing Systems) from UWE Bristol Robotics Lab, alongside postgraduate certifications in research supervision and higher education. His work bridges academia and industry, addressing challenges in cybersecurity, IoT, and healthcare through applied research. Research interests span cloud computing paradigms, semantic technologies, and AI ethics, with over 150 publications. His recent articles emphasize AI-driven cybersecurity solutions, quantum computing applications, and medical diagnostics using deep learning. He actively contributes to open-access publishing and advises on national cybersecurity strategies. Awarded Fellow of the UK Higher Education Academy, he leads impactful projects including malware detection systems, semantic knowledge graphs for public health, and blockchain-based authentication frameworks. His teaching spans undergraduate to PhD levels, with over 100 supervised projects. He also serves on advisory boards for cybersecurity initiatives and Staffordshire Police's evidence-based practices.
Kwanghee Won is an Associate Professor at the McComish Department of Electrical Engineering and Computer Science, South Dakota State University. His research focuses on computer vision, deep learning, and their applications in autonomous systems, precision agriculture, and structural health monitoring. He holds a Ph.D. from Kyungpook National University, Korea. Education: B.S., M.S., and Ph.D. in Computer Engineering from Kyungpook National University, Daegu, Korea Research Interests: Deep convolutional neural networks for anomaly detection and classification Autonomous vehicle systems and augmented reality applications Computer vision in smart agriculture and infrastructure assessment Model compression techniques for efficient neural networks Professional Contributions: Upsilon Pi Epsilon honor society faculty advisor (2021–present) ACM member and SIG Applied Computing participant Labs and Teams: Director of the Intelligent Vision Systems Laboratory , focusing on advanced vision-based solutions for real-world challenges.
Samer Hijazi is a Professor at Texas Lutheran University (TLU) specializing in integrating technology into education. His work focuses on web development, database systems, and innovative teaching methodologies. He holds a D.B.A. in Information Systems from the University of Sarasota, an M.A. in Economics from Morgan State University, a B.S. in Computer Science and Applied Mathematics from Towson State University, and an A.A. in Computer Science from Montgomery College. Research & Collaboration His research emphasizes practical applications of technology in education, including: Web technologies interfacing with relational databases AJAX-based rich user interfaces Portable learning environments via USB drives Knowledge management systems with business intelligence components Presentations & Publications Key contributions include: ACET Conference 2013 presentations on WAMP server utilization and Syrian student virtual learning communities ASCUE presentation on portable web programming tools Development of Camtasia-based instructional videos Awards & Leadership Recipient of the 2001 Servant Leader of the Year award. Served as President-elect of the Association of Computer Educators in Texas (ACET). Advocates for technology-enhanced education through campaigns like the Seguin Respect Initiative. Teaching Innovations Pioneered using jump drives for teaching web programming via XAMPP Lite, enabling offline development environments. Developed methodologies to teach object-oriented concepts through JavaScript. Focused on making technology accessible in resource-constrained settings.
Mohammadali VOSOOGHIDIZAJI is an Assistant Professor in Supply Chain Management at EM Normandie since 2022. He earned his PhD in Management Sciences from Université de Le Havre Normandie (2021) and a Master's in Industrial Management from IKIU, Iran. His research focuses on quantitative approaches to supply chain coordination under information asymmetry and CSR integration. Professional experience includes roles as a research engineer and ATER (Teaching and Research Assistant) at Université Le Havre Normandie, alongside industry roles as a manager and consultant. Education highlights include: PhD in Management Sciences, Université de Le Havre Normandie (2021) Master's in Industrial Management, IKIU, Iran Research interests emphasize: Designing coordination mechanisms under information asymmetry Risk management in energy supply chains Applying game theory to CSR integration Optimization of decentralized supply chains Publications span topics like collaborative forecasting, bullwhip effect mitigation, and risk prioritization in oil supply chains. His work bridges theoretical insights with practical supply chain challenges, leveraging advanced analytical methods. No scientific awards are listed. His professional trajectory includes both academic and industry roles, reflecting a blend of theoretical and applied expertise.
Dalila Fontes is a Full Professor at the Faculty of Economics of the University of Porto (FEP) and a board member of LIAAD (Laboratório de Inteligência Artificial e de Apoio à Decisão), a unit of INESC TEC since 2007. She holds a Ph.D. in Management Science from Imperial College London (2000), an MSc in Operational Research from the London School of Economics (1994), and a 5-year degree in Electrical Engineering and Computer Science from the University of Porto (1993). She has served as a Visiting Professor at the University of Florida (2007/08) and Texas A&M University (2015–16). Her research focuses on applying Operational Research and Artificial Intelligence techniques to solve complex management problems in production, logistics, transportation, and services, emphasizing combinatorial optimization. She has authored over 60 publications and coordinates research projects in these areas. She is an Associate Editor for the Journal of Combinatorial Optimization and Operations Research Forum (Springer). At FEP, she teaches Operational Research, Logistics, and Optimization courses in English to undergraduates, masters, and Ph.D. students. She has held administrative roles including Vice-Dean at FEP and currently directs ISFEP (FEP’s Research and Services Institute). Her work bridges academia and industry, addressing sustainable production systems and energy-efficient scheduling strategies.
H M Abdul Fattah is an Instructor at the University of Arizona, specializing in Applied Machine Learning, Natural Language Processing, and Data Analytics. His work focuses on bridging complex data with impactful solutions, particularly in healthcare informatics and data ethics. He holds a Master's in Information Science from the University of Arizona (Dean's List 2024) and a Master's in Computer Science and Engineering from Khulna University of Engineering and Technology (KUET), Bangladesh. Previously, Fattah served as a teaching faculty member at KUET from 2018–2022, teaching courses in Object-Oriented Programming, Data Structures, and Discrete Mathematics. He also conducted research and mentored undergraduate theses. His research intern at Mayo Clinic’s AI and Informatics department contributed to developing a rare disease study identification tool. Awards include recognition on the Dean's List of Distinguished Graduate Scholars. Research interests span AI ethics, feature engineering in healthcare, and multilingual NLP. Recent work includes SMS spam detection using hybrid ensembles, breast cancer diagnosis via neural networks, and Bangla emergency post classification. His technical contributions focus on top-k dominating queries for incomplete data and MRI-based Alzheimer’s diagnosis using ResNet architectures. Teaching experience includes leading ISTA courses on web design and development. Grants and labs: no specific grants or lab affiliations mentioned in the text.
Daniele Pretolani is an Associate Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia. He specializes in Operations Research (MATH-06/A) with a focus on decision support systems, multi-objective optimization, and directed hypergraph algorithms. Teaches Methods and Algorithms for Optimization in Digital and Creative Industry (Management Engineering) Teaches Models and Methods for Decision Support (Management Engineering) His research explores: Stochastic time-dependent network routing Multi-criteria decision analysis for supplier selection Hypergraph reductions for satisfiability problems Visual decision support tools for composite indexes Recent work includes developing a decision support system for global service providers (2024) that improved supplier selection by 25% over company practices, and foundational contributions to the SDEWES sustainability index (2018-2020). No specific awards or student advising details were mentioned in the provided texts.
Dr. Haoning Xi is a Lecturer in Business Analytics at the Newcastle Business School, University of Newcastle (UON), Australia. She previously served as a Research Fellow at the Institute of Transport and Logistics Studies (ITLS), The University of Sydney Business School. Dr. Xi received her Ph.D. in Transportation & Operations Research from the University of New South Wales (UNSW) Sydney, where she was also a co-cultured Ph.D. student at CSIRO Data 61. She holds a Master's degree from Tsinghua University and a Bachelor's from Central South University, China, and has research experience at the University of California, Berkeley and Hong Kong University of Science and Technology. Ph.D. in Transportation & Operations Research, University of New South Wales Master of Engineering, Tsinghua University, China Bachelor of Engineering, Central South University, China Research Assistant, University of California, Berkeley Visiting Researcher, Hong Kong University of Science and Technology Dr. Xi's research focuses on applying business analytics, machine learning, and operations research to mobility services and transportation systems. Her work centers on Mobility-as-a-Service (MaaS), travel behavior analysis, data-driven optimization, and sustainable transportation. She investigates how to leverage millions of smart card data from various transport modes to uncover user travel patterns and preferences, enabling intelligent decision-making for transport authorities. Her research also explores predictive analysis using AI and ML algorithms to forecast travel patterns, service disruptions, and resource allocation strategies. A significant portion of her work examines personalized mobility services and how to integrate transportation with non-mobility offerings to create comprehensive subscription models. Dr. Xi has published over 23 SCI/SSCI indexed papers, including 9 ABDC A* journal articles (8 as first/corresponding author) in top journals like European Journal of Operational Research and Transportation Research series. Her publications reveal a strong emphasis on mathematical modeling of transportation systems, with increasing focus on AI/ML applications in recent years. The articles demonstrate progression from traditional transportation modeling toward more sophisticated data-driven approaches integrating machine learning with operational research techniques, particularly in the context of Mobility-as-a-Service ecosystems and pandemic-related travel behavior changes. Rising Stars Women in Engineering, Asian Deans' Forum (2024) Best Research Silver Award, International Symposium on Sustainable Development of Urban Transport Systems (2024) Best Paper Award, International Workshop on Computational Transportation Science (2024) Global Talent Independent Scheme, Australian Government (2021) University Postgraduate Award, UNSW (2021) CSIRO Data 61 Top-up Ph.D. Scholarship (2020) Dr. Xi currently supervises 5 PhD students across various topics including digital transformation's impact on ESG, digital sustainability measurement, data analytics for hospitality management, social media sentiment analysis, and organizational capabilities in regulated environments. She has secured over $463,500 in research funding from multiple sources including National Natural Science Foundation of China ($80,000), iMOVE Australia Limited ($300,000), and various internal university grants. Her current projects focus on AI-driven bus network optimization, parking management models, and enhancing user mobility experience through business analytics. Dr. Xi serves as CHSF College Research Committee Member and NBS Equity Diversity and Inclusion (EDI) Committee Member at the University of Newcastle. She is Co-chair of the Multimodal Urban Transportation Systems Analysis Committee in the World Transport Congress (2024-2026) and serves on editorial boards for International Journal of Transportation Science & Technology and Transportation Safety and Environment. She also acts as a peer reviewer for top transportation journals including Transportation Science and Transportation Research series.
Wei Niu is an Assistant Professor in the School of Computing at the University of Georgia, affiliated with the Franklin College of Arts & Sciences. His research focuses on real-time machine learning systems, AGI on embedded devices, compiler optimization, and parallel computing. He holds a Ph.D. in Computer Science from William & Mary (2023) and a B.S. from Beihang University (2016). Prior to academia, he worked at Bytedance as a mobile software developer (2016–2018). Research Interests: Dr. Niu’s work emphasizes optimizing deep neural networks (DNNs) for real-time execution on mobile and embedded systems. His contributions include frameworks like SmartMem (memory optimization for DNNs on mobile devices), GCD² (compiler for mobile DSPs), and SoD² (static optimization for dynamic DNNs). He also explores applications in autonomous vehicles, healthcare, and edge computing. Recent Trends in Publications: His articles address DNN efficiency, compiler-driven optimization, and real-time systems. Recent work includes semantic segmentation for autonomous vehicles, food recognition, and lossy data compression for scientific computing. He prioritizes practical solutions for edge devices and embedded systems. Funding: He leads NSF-funded projects totaling $600K, collaborating with Gagan Agrawal. These include MITTEN (memory hierarchy optimizations for transformers) and CropDL (scheduling DNNs on HPC clusters). Teaching & Labs: He offers RA positions for Ph.D. candidates and maintains a lab developing real-time AI applications (e.g., style transfer, object detection). Videos of his work are available on YouTube and Bilibili.
Dr. Sara Amar is an Assistant Professor of Industrial and System Engineering at Bowling Green State University (BGSU), part of the College of Technology, Architecture and Applied Engineering. She holds a Ph.D. in Industrial and Logistics Engineering and a Master's in Logistics and Transportation. Her research focuses on Multi-Criteria Decision Making, Operations Research, Industry 5.0, Sustainable Development, and Urban Logistics. Dr. Amar actively contributes to academic conferences as a reviewer and committee chair, emphasizing practical applications of theoretical frameworks in industrial and logistical challenges. Her educational background includes advanced degrees in logistics and engineering, complemented by a strong publication record spanning topics from facility layout optimization to emotional factors in supply chain digitalization. Research highlights include sustainable urban planning frameworks, emotional intelligence integration in decision-making models, and bi-criteria optimization approaches for industrial problems. Dr. Amar's work bridges theoretical models with real-world applications in transportation, energy systems, and socio-economic policy design. Her publications demonstrate a focus on interdisciplinary solutions, combining mathematical modeling with practical case studies. Notable contributions include studies on energy transportation cost optimization using nonlinear programming and interactive facility layout design methodologies. While no specific grants or awards are listed, her active conference participation underscores her engagement with the academic community.
Jie Yang is a prominent academic affiliated with Carnegie Mellon University in the Department of Machine Learning under the School of Computer Science . With a research focus on Computer Science Artificial Intelligence Remote Sensing Machine Learning Data Analysis Pattern Recognition , Yang's work bridges theoretical advancements with practical applications across diverse fields. Recent publications highlight trends in object detection , trajectory prediction , 5G network security , and agricultural automation . These contributions leverage cutting-edge techniques such as ConvGRU networks , YOLO-seg , and transfer learning to address complex challenges. Despite extensive research output, no formal scientific awards or student advisement details were identified in the provided materials. Yang's work remains interdisciplinary, impacting both engineering and healthcare domains through innovative methodologies.
Alper ÇİÇEK is an Assistant Professor at the Department of Electrical Engineering, College of Engineering at Yildiz Technical University. He holds a PhD in Electrical Engineering (2021) and a Master’s degree in Power Systems (2018), both from Yildiz Technical University. His research focuses on renewable energy integration, smart grids, electric vehicle systems, and hydrogen-based energy solutions. He has advised multiple master’s theses, including work on V2G systems and energy storage optimization. Education: PhD in Electrical Engineering (2021) – Yildiz Technical University Master’s in Power Systems (2018) – Yildiz Technical University Bachelor’s in Electrical Engineering (2014) Research Interests: Optimization of hybrid energy systems Electric vehicle infrastructure and grid interaction Hydrogen storage and refueling systems Resilience-oriented energy management Market mechanisms for renewable energy portfolios Publications: Over 30 articles in top journals like *Energy for Sustainable Development*, *Sustainability*, and *International Journal of Hydrogen Energy*. Recent work emphasizes AI-driven energy arbitrage, resilience strategies, and multi-objective system design. Most articles focus on renewable integration, EV systems, and hydrogen applications. Advising & Grants: Advised 3 master’s students. Actively involved in IEEE editorial boards and conference organization (e.g., Technical Co-Chair at SEST 2023). No explicit grants mentioned. Labs/Teams: Engaged in interdisciplinary projects at Yildiz Tech’s power systems laboratory, focusing on grid resilience and EV infrastructure.
Oumayma LAOUINI is a Researcher affiliated with the Université de Technologie de Compiègne (UTC). Her work focuses on operations research and industrial engineering, particularly in disassembly line balancing, stochastic programming, and uncertainty management in manufacturing systems. She holds a Doctorate (PhD) and has contributed to advanced methodologies in disassembly optimization under yield uncertainty and robotic integration. Her research addresses challenges in production systems through exact and stochastic approaches, with recent contributions emphasizing collaborative robotics in disassembly processes and capacitated lot-sizing under random yield conditions. Key areas of expertise include bi-objective optimization, decision-making under uncertainty, and reverse logistics. Dr. LAOUINI’s publications reflect a strong emphasis on bridging theoretical models with practical industrial applications, particularly in enhancing efficiency and adaptability in manufacturing and disassembly operations.
Rune Møller Jensen is an Associate Professor in Data Science and Head of The Maritime Hub at the IT University of Copenhagen. He specializes in container vessel stowage planning optimization, with expertise in metaheuristics, constraint programming, and mathematical programming. His work bridges academia and industry through projects like churn prediction and AIS-based ETA forecasting. He co-founded Sealytix, a leading stowage analytics firm, and previously led Optivation until 2022. Jensen holds awards including the Hede Nielsen Prize (2005) and The McKinsey Award (1998). Education: Studied Symbolic AI at Carnegie Mellon University (CMU). Research focuses on optimizing maritime logistics, energy-efficient transportation, and decision support systems for liner shipping. Key projects include Greenship (2014-2016), REALCAP (2016-2018), and BAYSTOW (2008-2012). Media engagements include discussions on AI ethics and industry collaboration. His research portfolio spans 86 publications and 10+ projects, emphasizing optimization algorithms and real-world maritime applications. As head of The Maritime Hub, he supports emerging researchers through interdisciplinary initiatives.
Dr. Maysam Abbasi is a Research Fellow at Queensland University of Technology (QUT) in the School of Electrical Engineering & Robotics, Faculty of Engineering. His research focuses on power electronics, DC-DC converters, microgrids, and renewable energy systems integration. Dr. Abbasi holds a PhD from the University of Technology, Sydney, which forms the foundation of his expertise in power electronics and energy conversion systems. Dr. Abbasi's research interests span power electronics , particularly in the design and optimization of DC-DC converters for renewable energy applications. His work addresses critical challenges in voltage gain enhancement , voltage stress reduction , and power conversion efficiency . He has made significant contributions to transformer-less converter topologies, switched-capacitor circuits, and multilevel inverter designs. His research extends to microgrid optimization , where he develops bi-objective approaches for economic emission dispatch and explores communication systems and control methods for renewable-integrated microgrids. His work bridges theoretical innovation with practical applications in renewable energy integration and power system stability. Dr. Abbasi's publication record from 2019-2024 demonstrates a consistent research trajectory focused on power electronics and renewable energy systems. His work appears in high-impact journals including IEEE Transactions, IET Power Electronics, and Energies, with a particular emphasis on novel converter topologies and optimization techniques for renewable energy integration. As a Research Fellow, Dr. Abbasi likely supervises graduate students in electrical engineering, guiding research in power electronics and renewable energy systems. His collaborative work with researchers like E. Abbasi, L. Li, and others suggests active participation in research teams focused on power conversion and microgrid technologies. Dr. Abbasi's research is conducted within the School of Electrical Engineering & Robotics at QUT, which provides a collaborative environment for advancing power electronics research and its applications in renewable energy systems.
