Elena Katia Leal Algara is an Associate Professor at Universidad Rey Juan Carlos, affiliated with the Department of Telematic and Computing Systems. She holds a PhD from Universidad Complutense de Madrid (2010) with a thesis on federated grid scheduling. Her research focuses on Grid Computing, Ubiquitous/Pervasive Systems, and Distributed Scheduling. She contributed to projects like Plan B OS, a middleware-free environment for pervasive computing. Notable research groups include PROGRESSUS (Programming & Sustainability) and PMI (Intelligent Mobile Platforms). Her work emphasizes energy-efficient resource allocation, adaptive scheduling in federated grids, and security protocols in ubiquitous environments. Key achievements include proposals for self-adjusting resource sharing policies and reallocation strategies in dynamic systems. Over 20 peer-reviewed articles span scheduling algorithms, grid infrastructure optimization, and pervasive computing design. Leal Algara's academic career involves advancing decentralized scheduling frameworks and exploring middleware alternatives for distributed systems. Current research interests include sustainable computing and autonomous resource management in federated environments.
Arnold Arz von Straussenburg is a Researcher at the University of Koblenz, Department of Business Information Systems and Smart Data, since October 2022. He holds an M.Sc. and has experience in academic and corporate roles, including student assistantships at the Universities of Koblenz-Landau and Münster, and working as a tutor and working student in software development and verification training. His research focuses on IoT data platforms, AI integration, sensor technologies, and educational technology. He has contributed to projects like responsible LLM-based grading systems, CO2-based occupancy detection in smart spaces, and hybrid architectures for AI-driven chatbots. His work emphasizes practical applications of technology in education and urban environments. Education includes studies in Business Information Systems, reflected in his roles as a member of the Board of Symposium Oeconomicum Münster eV and his academic publications. His research spans IoT interoperability, AI literacy, and high-availability data architectures. Key research trends in his articles include leveraging IoT for smart city solutions, integrating large language models ethically in education and chatbots, and optimizing sensor-based systems for workplace efficiency. He has developed frameworks for evaluating conversational agents and improving data model flexibility in IoT systems. Arnold has no listed scientific awards but has been actively involved in collaborative projects and academic organizations. His work on automated application deployment and sensor-based smart parking demonstrates his interest in both theoretical and applied computing.
Prof. Bruno Defude is a Professor at Telecom SudParis, part of the University of Paris-Saclay. He has been affiliated with the institution since 1992, holding roles such as lecturer-researcher, department director, and currently serves as Deputy Director of Research and Doctoral Training. He leads the ACMES studies initiative and focuses on interdisciplinary research in distributed systems, cloud computing, and educational technologies. Education: Doctorate in Computer Science (1986), Grenoble INP Habilitation à Diriger des Recherches (HDR) (2005), UPMC Research Interests: Prof. Defude's work spans cloud computing architectures, data integration challenges, process mining, sensor networks, and adaptive learning systems. His contributions emphasize scalable solutions for distributed environments and privacy-aware service composition. Recent Trends: Recent publications highlight advancements in natural language querying of process data, multi-objective task scheduling in fog computing, and spatio-temporal data aggregation in vehicular networks. His work bridges theoretical foundations with practical implementations in distributed systems. Labs/Teams: Director of ACMES studies, involved in interdisciplinary research collaborations across computer science and telecommunications domains.
Chantal Taconet is a Lecturer at Telecom SudParis, part of the SAMOVAR research institute. Her work focuses on middleware systems for the Internet of Things (IoT), context-aware computing, energy efficiency, and blockchain applications in supply chains. She has co-authored over 20 peer-reviewed articles and conference papers since 2008, addressing IoT middleware design, QoC management, and middleware for cloud-IoT integration. Her research spans IoT middleware architectures (e.g., IoTVar framework), energy-efficient IoT protocols, and blockchain-based supply chain traceability. She contributed to the ANR INCOME project on multi-scale context management, emphasizing model-driven engineering and distributed system frameworks. Taconet has organized workshops like M4IoT and co-edited special issues on IoT middleware in Annals of Telecommunications . Key technical areas include semantic-based discovery services, fog computing integration for sensor networks, and trust models for IoT systems. Her work bridges theoretical middleware design with practical implementations for IoT applications.
Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.
Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.
Dr. Anis Zarrad is an Associate Professor in the School of Computer Science at the University of Birmingham, Dubai Campus, where he has been serving since 2018. He previously held the role of Digital Lead for computer and engineering programs from 2020 to 2022. His academic base is firmly rooted in computer science with a focus on innovative research and educational contributions. Education: Postgraduate Certificate in Higher Education (PGCHE), University of Birmingham PhD in Computer Science, University of Ottawa, Canada, 2010 MSc in Computer Science, Concordia University, Canada, 2014 BSc in Computer Science, University of Ottawa, Canada, 2010 BSc in Software Engineering, University of Ottawa, Canada, 2012 Dr. Zarrad's research interests span Search-Based Software Engineering (SBSE) , Machine Learning applied to software testing , and teaching and learning through collaborative virtual environments . His work integrates algorithmic optimization, AI-driven testing, and cloud-based systems, reflecting a multidisciplinary approach to modern software challenges. During his PhD, he developed a routing protocol for mobile collaborative virtual environments to reduce network traffic and improve efficiency. The recent publications highlight a strong trend in applying decision-making models like AHP-TOPSIS to technical debt evaluation, leveraging deep transfer learning for medical diagnosis (e.g., COVID-19 detection), and designing cloud-based disaster management systems. These works reflect his expertise in combining software engineering principles with machine learning and real-world applications in healthcare and emergency systems. Professional Service: Member of over ten conference and workshop program committees Reviewer for journals published by Elsevier, Springer, and IEEE Dr. Zarrad has collaborated extensively with researchers such as Professor Azzedine Boukerche in the PARADISE Computer Science Lab. While no formal students are listed, his role as an associate professor and active researcher suggests mentorship and advising responsibilities. He has not received any explicitly mentioned scientific awards. There is no indication of grant details, but his publication record suggests involvement in funded research projects. Laboratory and Research Group Affiliation: He was actively involved in the PARADISE Lab (Parallel, Ad-hoc, and Distributed Systems Laboratory) during his PhD at the University of Ottawa, working under the supervision of Professor Azzedine Boukerche. This lab focuses on networking, distributed systems, and simulation environments, aligning closely with his early work on routing protocols for mobile CVEs.
Gianluca Aloi serves as an Associate Professor in Telecommunications (IINF-03/A) at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) of the University of Calabria, Italy. He holds the position of scientific director for the Telecommunications and Information Theory for Advanced Networking Laboratory (TITAN Lab.). Dr. Aloi earned his PhD in Systems Engineering and Computer Science from the University of Calabria in 2003 and became a University Researcher in Telecommunications (ING-INF/03) in 2004 before advancing to his current academic rank. His research expertise spans wireless networks, cellular networks, sensor networks, Internet of Things systems and their interoperability, management of resources and services in the Cloud/Edge and IoT (CEI) Continuum, and management and orchestration of network resources using Artificial Intelligence. His work particularly focuses on UAV-assisted IoT systems for industrial applications, geological hazard monitoring, and maritime environments. Analysis of Dr. Aloi's recent publications (2023-2025) reveals a strong emphasis on applying reinforcement learning and deep learning techniques to solve complex networking challenges. His research shows a clear trajectory toward developing intelligent network architectures that optimize data collection, improve system resilience, and enhance resource management across the edge-to-cloud continuum. Key application areas include smart factories, disaster monitoring, and urban vehicular systems. Dr. Aloi teaches courses including Fundamentals of Telecommunications Networks and Telecommunications Networks for both Electronic Engineering and Computer Engineering programs. He maintains regular reception hours every Tuesday from 9am to 11am or by appointment via email. As scientific director of TITAN Lab, Dr. Aloi leads research initiatives focused on advanced networking technologies, with particular emphasis on developing solutions for next-generation communication systems that integrate artificial intelligence with traditional networking paradigms to address real-world challenges in telecommunications and IoT applications.
Dr. Brahim Medjahed is a Professor in the Department of Computer and Information Science at the University of Michigan - Dearborn's College of Engineering and Computer Science (CECS) . He also holds administrative roles as Associate Dean at the Rackham Graduate School and Acting Associate Dean of Undergraduate Education at CECS. He earned his PhD in Computer Science from Virginia Tech in 2004 and an M.Sc. from Algiers University of Sciences and Technology (USTHB) . Research Interests include advancing service-oriented software integration in emerging environments like cloud computing, IoT, big data, social computing, and crowdsourcing . His work focuses on privacy, trust, and fault tolerance in these domains. He has over 100 publications and two Springer-published books. Scientific Awards highlight his excellence: 2019 Michigan Distinguished Professor of the Year 2018 Trevor O. Jones Outstanding Paper Award 2015 Best Student Paper Award (ICWS) 2008 Wilkes Award (The Computer Journal) Grants include significant funding from Ford Motor Company ($191,154 for privacy algorithms) and NSF ($900,000 shared for IT education initiatives).
Prof. Dr. Thomas Richter is a faculty member at Rhine-Waal University of Applied Sciences, where he holds the professorship for the Development of Web-Based Systems within the Faculty of Communication and Environment at the Kamp-Lintfort Campus. He serves as the university's representative for e-learning and digitalization and leads the Software Laboratory, playing a key role in both academic and technical advancement. His educational background, while not detailed in the text, includes a doctoral degree (Dr.) and extensive professional experience spanning over 20 years in software development, project transformation, and industrial software engineering. His research interests center on the engineering of reliable and efficient software systems in distributed and mobile environments. He emphasizes pragmatic, organization-tailored software processes that integrate modern methodologies with real-world constraints. Key areas include: Software Engineering and Process Design Web and Mobile Application Development Cloud Computing and Big Data Integration Cross-Platform Programming Business Process Management Agile, DevOps, and Continuous Delivery Practices Although no specific publications are listed, his teaching and applied work suggest a strong focus on practical software engineering challenges, inspired by historical and systemic failures such as the Vasa shipwreck — a metaphor he uses to highlight recurring issues in software projects like time pressure, creeping requirements, and lack of documentation. He has contributed to academic discourse through recorded lectures available on YouTube since 2015 and actively supervises student internships, semesters abroad, and final theses. His supervision topics include mobile apps, web development, social media integration, and empirical studies in software engineering. Scientific Awards: No awards are mentioned in the provided texts. Prof. Richter advises students on bachelor’s and master’s theses and supports their academic development through structured guidance. While no grants are explicitly mentioned, his role in digitalization and e-learning suggests institutional leadership in funded or strategic initiatives. He audits software projects, provides expert opinions, and supports project reorganization, bridging academia and industry. He is responsible for the Software Laboratory at the university, which likely serves as a hub for student projects, applied research, and software development initiatives. The lab supports hands-on learning and innovation in web-based systems.
Prof. Dr. Martina Lehser is a faculty member at the Saarland University of Applied Sciences in the Faculty of Engineering , specifically within the Department of Computer Science, Fundamentals, and Sensor Technology . She has held leadership roles in multiple research laboratories, including the Embedded Robotics Lab and the Communication Informatics Laboratory . Spokesperson for the Center for Digital Neurotechnologies Saar (CDNS) Scientific Director of the Chinese-German Institute @ htw saar (CDI) Her research focuses on robotics , internet technologies , sensor systems , and software engineering , with applications in industrial automation, mobile robotics, and web-based tools. She has supervised numerous Bachelor's and Master's theses on topics like embedded systems, cloud computing, and distributed networks. Current and former leadership roles include: Head of the Test Field Digitalization in Production @ htw saar (until 2023) Vice Dean of the Faculty of Engineering (2009–2018) International Representative, Computer Science Department (2010–2018) She actively engages in STEM projects and Internet workshops , educating students and parents on technical and safety aspects of digital systems.
Shahrear Iqbal is an Adjunct Associate Professor at Queen's University and a Cyber Security Researcher at the National Research Council (NRC) Canada . He holds a PhD in Cybersecurity (2017) and MSc in Combinatorial Optimization (2011) from Queen's University, along with a BSc in Computer Science and Engineering (2008) from Bangladesh University of Engineering and Technology. Research Focus: Security and Privacy of Smart Systems, including in-vehicle security, self-aware operating systems, IoT-cloud security, and AI-driven cybersecurity. Teaching: Previously taught CISC490: Cybersecurity at Queen's University. Key Projects: Droid Mood Swing (DMS) for context-aware Android security policies Securing ECU Communications in connected vehicles FCFraud for user-side click-fraud detection
Eric Bartley Jul is a Professor in the Department of Informatics at the University of Oslo, specializing in Programming Technology within the Faculty of Mathematics and Natural Sciences. His research spans multiple domains of computer science with a focus on practical applications in medical imaging, distributed systems, and mobile computing. Professor Jul's research interests encompass a broad spectrum of computer science disciplines. His expertise lies particularly in Object-Oriented Programming, Distributed Computing, and Design Patterns, with expanding work in Cloud Computing and Security. His recent publications demonstrate a significant shift toward applying computer vision and deep learning techniques to medical diagnostics and healthcare applications, particularly in microcirculation analysis and reproductive medicine. This interdisciplinary approach bridges traditional computer science with cutting-edge medical research. His recent publication record shows a strong trend toward medical applications of computer vision and mobile sensor technologies. The 2024 papers reveal sophisticated applications of AI in reproductive medicine (sperm detection) and transportation monitoring (flight detection), while the 2022 publications establish his foundational work in medical imaging systems like CapillaryNet for blood flow analysis. This trajectory demonstrates a consistent focus on applying programming technology to solve complex real-world problems, particularly at the intersection of computing and healthcare. Professor Jul leads research within the Programming Technology group at the University of Oslo and is involved in several significant projects including A Modern Approach to Teaching Classes at the University Level in Theoretical Computer Science , Leveraging Energy-Aware Programming (LEAP) , and Reliable models of computation for concurrent and distributed problems . His work demonstrates strong collaborative efforts with researchers across multiple institutions, particularly with Paulo Ferreira and other colleagues at the University of Oslo.
Öznur Özkasap is a Professor in the Department of Computer Engineering at Koc University's Graduate School of Sciences and Engineering. She serves as Head of Department and has research interests spanning distributed systems, computer networks, and energy-efficient networking protocols. Her work integrates artificial intelligence into distributed computing frameworks and explores security in peer-to-peer systems. Education: PhD (2000), MSc (1994), and BS (1992) from Ege University Research Areas: Distributed Systems, Cloud/Edge Computing, Network Security, and AI applications in distributed environments Her recent publications focus on blockchain-assisted distributed systems, federated learning optimization, and energy-efficient frameworks for software-defined networks. She explores multi-objective load balancing, decentralized energy trading, and secure P2P architectures. Key trends include cross-disciplinary applications of distributed computing in healthcare and energy systems. Professor Özkasap contributes to advancing edge computing, IoT-based sensor systems, and privacy-preserving mechanisms in decentralized environments. Her work addresses challenges in reliable network protocols, microgrid energy sharing, and security vulnerabilities in autonomous systems.
Jussara M. Almeida is an established computer science researcher specializing in social network analysis, misinformation detection, and human mobility modeling. Her extensive publication record (1996–2025) demonstrates active research in web science, political communication on messaging platforms (WhatsApp/Telegram), and cloud systems. She frequently collaborates with Brazilian institutions and international partners on large-scale data projects. Research Focus: Her core interests include: Modeling information diffusion in encrypted messaging apps (WhatsApp/Telegram) Predicting human mobility patterns and privacy implications Analyzing political discourse and election-related coordination online Developing computational methods for misinformation detection Optimizing cloud/edge computing performance Publication Trends: Recent work (2021-2025) shows intensified focus on: Telegram's role in political mobilization and information dissemination Advanced techniques for identifying fake news websites and image-based misinformation Privacy-preserving mobility analysis and edge computing Child safety in live-streaming platforms Collaborations & Impact: Key collaborators include Marcos André Gonçalves, Fabrício Benevenuto, and Marco Mellia. Her research provides critical insights into real-world problems like election integrity, platform governance, and user privacy.
