Alexander Harms serves as a Researcher at Erasmus MC within the Department of Radiology & Nuclear Medicine, Erasmus University Rotterdam. His work bridges advanced computational methodologies with clinical imaging applications, focusing on scalable solutions for data-intensive medical research through active participation in EU-funded consortia and international initiatives. His research program emphasizes: Reproducibility frameworks for medical imaging biomarkers Federated learning architectures for multi-cohort dementia studies OMOP-based data harmonization in healthcare AI infrastructure development for diagnostic imaging Open science practices in perfusion MRI Cardiovascular-neurological interface investigations Recent publications reveal a strategic trajectory toward solving critical bottlenecks in medical AI, particularly addressing data privacy through federated approaches and enhancing reliability of imaging analytics. His leadership in the ISMRM Open Science Initiative and EU infrastructure projects demonstrates translational impact, with work appearing in high-impact venues like Magnetic Resonance in Medicine and EClinicalMedicine attracting significant citations and policy attention.
Christian S. Jensen is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His primary research focuses on data management, spatiotemporal systems, and AI-driven solutions for mobility and cyber-physical systems. He leads projects like DiCyPS (Data-Intensive Cyber-Physical Systems) and MALOT (Managing Mobility Data Quality for Location of Things). He has published over 700 papers, with recent work emphasizing time series forecasting, trajectory analysis, and edge computing. Notable contributions include frameworks like Memory Guided Transformers and TEAM for traffic prediction. His work has been recognized with awards including the IEEE TCDE Impact Award (2019) and the Order of Dannebrog (2016). Jensen actively collaborates internationally, holding roles in organizations like the Max Planck Institute and the Villum Foundation. His research bridges theory and practice, addressing real-world challenges in smart cities, energy systems, and autonomous driving.
Dr. Md Arafatur Rahman is a Senior Lecturer in Cyber Security at the School of Engineering, Computing & Mathematical Sciences, Faculty of Science and Engineering, University of Wolverhampton, UK. He previously served as an Associate Professor at Universiti Malaysia Pahang and holds a PhD from the University of Naples Federico II, Italy. He is a Senior Member of IEEE and a Fellow of the Higher Education Academy, with international recognition including the Royal Academy of Engineering Global Talent endorsement. PhD in Electronic and Telecommunications Engineering, University of Naples Federico II, Italy Former Associate Professor, Universiti Malaysia Pahang Postdoctoral Research Fellow, University of Naples Federico II Visiting Researcher, Sapienza University of Rome Dr. Rahman’s research focuses on cutting-edge domains in cyber-physical systems and connectivity. His primary interests include Internet-of-Things (IoT) , Wireless Communication Networks , Cognitive Radio , 5G , Vehicular Communication , Cloud-Fog-Edge Computing , Machine Learning , and Cyber Security . His work bridges theoretical innovation with real-world applications in smart cities, energy-efficient infrastructure, and disaster response systems. The recent publications reflect a strong trend toward intelligent, secure, and scalable systems. Key themes include IoT-enabled smart infrastructure , fog-edge computing for real-time analytics , machine learning in network optimization , and secure data handling in cyber-physical systems . His work frequently appears in top-tier journals like IEEE Transactions and Elsevier journals, emphasizing both technical innovation and societal impact. World Top 2% Scientists List (Stanford University, 2019–2021) Best Paper Award, ICNS’15 (Italy) Best Innovation Award & Gold Medal, MTE 2020 Gold & Silver Medals, iENA’17 Germany Diamond & Gold Medal, BiS’17 UK Best Supervisor Award, UMP Higher Education Academy (HEA) Fellowship IBM Center of Excellence Fellow Royal Academy of Engineering Global Talent (Exceptional Talent, 2022) Dr. Rahman has supervised over 30 students at B.Sc., M.Sc., and PhD levels and led numerous international research grants from UK, EU, Italy, and Malaysia. He has collaborated with industry partners such as FUSI Technology Indonesia, developing commercializable innovations. He serves as an editor and guest editor for journals including IEEE Access and Frontiers in the Internet of Things, and has held leadership roles in major conferences like IEEE Globecom and IEEE DASC. He is actively involved in research labs focusing on IoT, wireless security, and smart infrastructure, contributing to both academic and industrial advancements.
Dr. Abigail Koay is an Honorary Research Fellow at the University of Queensland's School of Electrical Engineering and Computer Science. Her research focuses on cybersecurity, machine learning applications in industrial systems, and healthcare technology. She has contributed to projects such as real-time cyber-attack detection using weakly supervised learning, supported by UQ Cyber Seed Funding (2021–2022). Her work spans multiple disciplines including: Cybersecurity for Industrial Control Systems (ICS) IoT network anomaly detection using fog-assisted frameworks Machine learning for medical imaging (e.g., glaucoma detection) AI-driven cybersecurity strategies for smart grids Recent publications highlight advancements in: Irregular time series analysis using GNNs Positive-unlabeled learning with random forests Domain generalization in retinal image analysis Dr. Koay has authored/co-authored 15+ peer-reviewed articles across journals like Frontiers of Computer Science , IEEE Access , and conferences including NeurIPS and ISGT Asia. She collaborates with industry partners on projects like Plan2Defend for smart grid security and SDGen for synthetic cybersecurity dataset generation. Her research integrates theoretical machine learning with practical cybersecurity challenges, emphasizing real-world applicability in critical infrastructure and healthcare systems.
Dr. Sikha Bagui is a Distinguished University Professor in the Department of Computer Science at the University of West Florida , within the Hal Marcus College of Science and Engineering . She previously served as Chair of the department and Founding Director of the Center for Cybersecurity. Her research spans Big Data Analytics, Machine Learning, Data Mining, and Database Design, with extensive publications and funded projects from NSF and NSA. Ed.D. in Curriculum & Instruction: Math & Stat / Science / Computer Science, University of West Florida M.B.A., University of Toledo B.S., Cuttington University (Liberia) Dr. Bagui's research centers on data-intensive computing , focusing on scalable algorithms for Big Data analytics, optimization in distributed environments (Hadoop, Spark, Hive), and applications in cybersecurity such as intrusion detection and phishing classification. She is particularly known for her work in data preprocessing, association rule mining, and improving classifier performance on imbalanced datasets. Her recent publications reveal a strong trend toward cybersecurity applications of machine learning , leveraging frameworks like MapReduce and Spark for scalable solutions. Topics include network traffic classification, load balancing in FP-Growth, and resampling techniques for intrusion detection. Her work bridges theoretical algorithm development with practical implementation in real-world Big Data systems. Distinguished University Professor Askew Fellow NSF CSForALL Grant ($300,000) NSA NCAE Grant ($375,511) Dr. Bagui has successfully led multiple federally funded research projects and mentored numerous students through research and academic programs. While specific advisees are not listed, her leadership in research groups and outreach initiatives like Women in Computing demonstrates strong mentorship. She has also authored influential textbooks used internationally. Her lab and research efforts are aligned with the UWF Smart Home Research , AI Research Group , and High Performance Computing Research , contributing to interdisciplinary innovation.
Dr. Matthew Pocock is a Research Fellow in the Department of Computing Science at Newcastle University. His work focuses on synthetic biology, systems biology, and bioinformatics, with significant contributions to data standards (SBOL), workflow management (Taverna), and semantic integration tools (Saint, FuGE). He has collaborated extensively with Professor Anil Wipat and others on projects like BacillOndex and Microbase2.0. His research spans Developing standardized languages for biological design (SBOL, ShortBOL) Creating cloud-based frameworks for computationally intensive bioinformatics workflows Building ontologies and controlled vocabularies for systems biology Integrating experimental data through tools like GELI and Saint Key article trends include Evolution of SBOL standards across versions 1.0–2.3 Applications of probabilistic networks in drug repurposing Workflow environments for life sciences (Taverna, Microbase) Model validation through semantic constraints (SBML, SBO)
Guangmo Tong serves as an Associate Professor at the University of Delaware, conducting research through the Computational Data Science Lab. His work bridges theoretical computer science with real-world applications in critical infrastructure and information systems. His academic background includes: PhD from The University of Texas, Dallas (2018) Bachelor of Mathematics and Applied Mathematics from Bejing Institute of Technology (2013) Dr. Tong's research focuses on combinatorial optimization and learning method design within artificial intelligence. He develops computational frameworks for social network analysis—particularly online information diffusion and misinformation prevention—and cyber-physical systems including real-time autonomous operations. His methodologies address scalability challenges in large-scale data environments while maintaining theoretical rigor. He leads the Computational Data Science Lab, which develops novel algorithms for data-intensive systems with applications in national security, public health, and autonomous technologies. The lab emphasizes both theoretical foundations and practical implementations for time-sensitive decision-making scenarios.
Shantenu Jha is a Professor of Computer Engineering at Rutgers University and Chair of the Department (Center) for Data Driven Discovery at Brookhaven National Laboratory. He leads the RADICAL Lab and the RADICAL-Cybertools project, focusing on middleware for large-scale scientific applications. His research bridges high-performance computing, data-driven science, and health informatics, collaborating with diverse fields like molecular sciences and high-energy physics. Education: Ph.D., Syracuse University (2004); M.Sc., IIT Delhi (1995). Research Interests : High-Performance and Distributed Computing Data-Intensive Science & Engineering Cyberinfrastructure for Science Health Computing (Personalized Medicine) Awards : NSF CAREER Award (2013) Chancellor’s Excellence in Research (2016) Rutgers Board of Trustees Fellowship (2014) Multiple best paper awards at SC/ISC conferences Grants & Funding : Supported by NSF, U.S. DOE, NIH, and UK EPSRC. Current projects include ExaWorks and quantum-HPC middleware development. Labs/Teams: PI of the RADICAL Lab, leading initiatives in FAIR principles, quantum computing integration, and autonomous laboratories.
Tiantian Liu is an Assistant Professor in the Department of Computer Science at Aalborg University, under the Technical Faculty of IT and Design. Her research focuses on data engineering and systems, particularly in the context of indoor location-based services and data science. Research Interests: Her work spans data management, indoor positioning, spatiotemporal databases, and scalable systems. She applies techniques from data science and computer science to solve challenges in real-time data processing, data quality, and context-aware applications. The publication trend from 2020 to 2024 shows a consistent focus on data engineering, with topics including indoor LBS, query optimization, distributed systems, and data integration. Her research combines theoretical database principles with practical system implementations. Scientific Awards: Prize (1) Advising and Grants: While no formal students are listed, she has participated in externally funded research projects. She was a project participant in Data Management Foundations for Indoor LBS (2019–2021), contributing to foundational work in indoor data systems. Labs and Teams: She is a member of the Data Engineering, Science and Systems research group at Aalborg University, collaborating on data-intensive systems and applications.
Aijuan Dong is a Professor and Department Chair of Computer Science & Information Technology at Hood College, where she teaches both undergraduate and graduate courses. Her academic work spans a broad range of computer science disciplines, with strong emphasis on practical and theoretical aspects of computing education and research. Her research interests include artificial intelligence, machine learning, health informatics, database technologies, cloud computing, knowledge representation and inference, and computer science pedagogy. Currently, she focuses on designing and applying scalable deep learning models and causal reasoning approaches to analyze large and diverse datasets. Dr. Dong earned her Ph.D. in Computer Science from North Dakota State University and a Master of Science in Computer Science from Minnesota State University. These educational foundations support her interdisciplinary research and teaching excellence. While no recent articles are listed, her research trajectory suggests strong engagement with data-intensive AI methods, particularly in health and educational domains, combining deep learning with causal inference for robust insight generation. Scientific Awards: No awards listed. Dr. Dong advises students at both undergraduate and graduate levels and leads curriculum development as Department Chair. She has not disclosed specific grant funding, but her research areas suggest potential involvement in federally or institutionally supported projects related to AI in health and education. She is active in academic leadership and professional engagement, as evidenced by her departmental leadership role and presence on LinkedIn. While no formal lab or research team is mentioned, her focus on scalable models implies collaborative, data-driven research efforts.
Thomas Rose is Professor for Media Processes at RWTH Aachen University and heads the research group on business process management at Fraunhofer FIT. He is affiliated with the Department of Computer Science (Informatik 5) and conducts research in media processes, process management, and digital collaboration systems for high-stakes domains such as healthcare and emergency response. His research focuses on the design and implementation of media processes for information capture and dissemination, along with advanced process management and customization techniques. Projects under his leadership include Setric (Security and Trust in Cities), ERMA (Electronic Risk Management Architecture), Olga (Online Guideline Assist for Intensive Care), and ZAMOMO (integration of model-based software and control engineering), all targeting real-world applications in public safety, healthcare, and industrial systems. His work has been recognized at the European level, with the Apnee(-Tu) project highlighted as a success story of European IST research by Commissioner Vivian Reding in 2005. The research group is funded by the B-IT Foundation, a 56 million Euro endowment supporting innovation at the intersection of Bonn and Aachen. Project Apnee(-Tu) selected as one of the success stories of European IST research by Commissioner Vivian Reding in 2005 Thomas Rose has supervised thesis projects and taught courses such as Data Visualization and Analytics and Distributed Ledger Technology. He collaborates extensively with Fraunhofer FIT and leads a research team focused on decision and process management support. His lab is embedded within the Fraunhofer Institute for Applied Information Technology, leveraging interdisciplinary teams to develop scalable, secure, and trustworthy process-aware systems.
Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.
Veerle Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05). She is affiliated with the Internet Technology and Data Science Lab and has been actively contributing to research in cloud computing, resource management, and related fields for over two decades. Her research interests focus on cloud computing technologies, including containerization, resource management strategies, multi-tenancy architectures, and migration of legacy systems to cloud environments. She has also explored applications in medical software, edge computing, and wireless sensor networks. Her work spans both theoretical frameworks and practical implementations, often validated through experimental testbeds like Raspberry Pi clusters. Dr. Ongenae's publication record shows a clear evolution from foundational mathematical work in the 1990s to contemporary cloud computing research. Her recent publications (2015-2024) demonstrate expertise in containerized cloud environments, resource allocation strategies, and the application of cloud technologies to various domains including medical software and educational frameworks. She frequently collaborates with researchers like Filip De Turck, Pieter-Jan Maenhaut, and Bruno Volckaert. Her work shows particular strength in bridging simulation with experimental validation, as evidenced by her Raspberry Pi testbed research. The publications reveal consistent contributions to both journal articles (A1 type) and conference papers (C1, P1 types), with a focus on practical implementations and performance evaluation. Her research has practical applications in medical software migration, educational technology, and distributed sensor networks, demonstrating the interdisciplinary nature of her work which connects computer science with healthcare, education, and industrial applications.
Nikolas Herbst is a Professor and Chair of Software Engineering at the Department of Computer Science, University of Würzburg. He leads research in Software Performance Engineering, High-Performance Data Processing, and Autonomic Computing. His work focuses on Cloud and Serverless Computing, Elasticity, and Time Series Analysis. He currently serves as JMU Chief Information Security Officer (CISO) and holds leadership roles in SPEC Research Groups and ICPE Steering Committees. Education: PhD in Computer Science (Karlsruhe Institute of Technology, 2018) Master of Computer Science (Karlsruhe Institute of Technology, 2012) Research Interests: His lab develops tools like CHAMELEON, TELESCOPE, and BUNGEE for cloud elasticity and performance analysis. He emphasizes benchmarking, resource demand estimation, and self-aware systems. Recent projects include real-time forest monitoring (ROOT) and serverless scientific computing (SOS). Teaching: Teaches Operating Systems, Performance Engineering & Benchmarking, and Self-Aware Computing at both undergraduate and graduate levels since 2012. Awards: 10 Year Most Impact Paper Award (ACM/SPEC ICPE 2023) SPEC Kaivalya Dixit Distinguished Dissertation Award (2019) IBM PhD Fellowship (2014) Grants & Projects: Coordinates DFG-funded projects like bidt-ROOT (2023–2026) and SOS (2025–2029). Leads development of open-source tools for cloud performance analysis, including WCF (Workload Classification & Forecasting).
Josep Lluís Berral García is an Associate Professor in the Department of Computer Architecture at the Barcelona School of Informatics (FIB), Polytechnic University of Catalonia · BarcelonaTech (UPC). He is actively engaged in teaching and research, with a strong focus on Artificial Intelligence, Cloud Computing, and sustainable computing practices. He leads innovative educational initiatives and is affiliated with the CROMAI research group and the Barcelona Supercomputing Center (BSC-CNS). Research Interests: Artificial Intelligence and Deep Learning Cloud and High-Performance Computing Resource Orchestration and Management Sustainable and Ethical AI AI Education and Pedagogy His recent research and teaching projects center on integrating sustainability and ethical responsibility into AI education, using active learning methodologies. The trend in his work shows a consistent focus on optimizing computing resources through AI, particularly in cloud and HPC environments, with increasing emphasis on environmental impact and responsible innovation. Scientific Awards: UPC Award for Quality in University Teaching 2025 (Teaching Initiative for Newly Recruited Professors) Advising and Grants: While specific students are not listed, his leadership in competitive R&D+i projects and innovation initiatives indicates active supervision and grant-funded research. His involvement in multiple competitive and non-competitive R&D projects demonstrates sustained funding and research leadership. Labs and Teams: He is a key member of the CROMAI (Computing Resources Orchestration and Management for AI) research group at UPC and maintains a strong collaborative link with the Barcelona Supercomputing Center (BSC-CNS), leveraging the MareNostrum supercomputing infrastructure for AI and systems research.
