Terje Gjøsæter is an Associate Professor at the Oslo Metropolitan University , affiliated with the Faculty of Technology, Art and Design and the Department of Computer Science . His research focuses on the intersection of Universal Design of ICT , Emergency Management , and Security and Privacy in Critical Infrastructure . Research Interests : Universal Design of ICT for Emergency Management. Web Accessibility (WCAG). Security and Privacy in SCADA Systems. Computer Language Theory, Domain-Specific Languages, Meta-Modelling (UML, MOF, ECORE). Recent Publications (2025–2020) highlight his expertise in: Emergency Management with emphasis on situational awareness, crisis information systems, and vulnerable populations. Accessibility innovations like barrier-free design and diversity-inclusive models. Security in critical infrastructure, including energy sector supply chains and landslide warning systems. Language Specification through abstraction frameworks and model-driven engineering. His collaborations span institutions like the University of Hawaii at Manoa , Mid Sweden University , and the SEMIAH Consortium , focusing on ICT for disaster risk reduction and resilient systems.
Chris Holmes is a Professor of Biostatistics at the University of Oxford and Programme Director for Health and Medical Sciences at The Alan Turing Institute. His work bridges Bayesian statistics, machine learning, and genomic sciences, with cross-appointments in the Department of Statistics and the Nuffield Department of Clinical Medicine through the Wellcome Trust Centre for Human Genetics. He is affiliated with St Anne's College and actively leads research in Statistical Genomics under a Medical Research Council Programme Leaders Grant. PhD in Bayesian statistics from Imperial College London Former postdoctoral researcher and lecturer at Imperial College Industry experience in scientific computing for defense and SCADA systems Research Interests: Focus on Bayesian statistics , nonparametric methods , genetic epidemiology , and treatment effect heterogeneity . His methodologies address challenges in malaria pharmacodynamics, health equity in medical devices, and pandemic preparedness. Publication Trends: Recent work applies machine learning to reclassify multiple sclerosis progression , analyze genomic data for health equity , and develop tools for emergency admission prediction in Scotland. Collaborative efforts span Nature Medicine , Nature Reviews Genetics , and Trials . Scientific Awards: Medical Research Council Programme Leaders Grant in Statistical Genomics Advising: Mentors PhD students Oscar Clivio , Sahra Ghalebikesabi , and Natalia Garcia Martin in areas like Computational Statistics and Statistical Genetics .
Sasanka Potluri serves as Professor of General Computer Science and Medical Informatics at Karlshochschule (Karlsruhe University of Education) since September 2025. He is actively engaged in teaching and research within the Department of Computer Science and Medical Informatics, focusing on the intersection of artificial intelligence and healthcare applications. His academic leadership spans multiple research projects aimed at transforming healthcare delivery through technological innovation. His educational background includes: Dr.-Ing. in Electrical Engineering and Information Technology from Otto-von-Guericke University Magdeburg (Germany) Dipl.-Ing. in Information Technology from Alpen-Adria University Klagenfurt (Austria) B. Tech in Electronics and Communication Engineering from Jawaharlal Nehru Technological University, Kakinada (India) Professor Potluri's research spans the cutting edge of artificial intelligence applications in healthcare, with particular expertise in machine learning, deep learning, and generative AI. His work bridges technical innovation with practical healthcare solutions, focusing on clinical decision support systems, biomedical statistics, and digital signal processing. He has developed novel approaches for healthcare logistics optimization, synthetic health data generation, and addressing digital health equity issues. His research methodology combines theoretical rigor with practical implementation, often working at the intersection of computer science, medical informatics, and systems engineering. His publication record reveals a consistent trajectory from industrial control systems security toward healthcare applications of AI. Early work focused on intrusion detection in industrial control systems using deep learning techniques, while recent publications demonstrate a strategic shift toward healthcare logistics, patient transportation optimization, and blood product management. This evolution reflects both his technical expertise in AI and his commitment to addressing critical challenges in healthcare delivery systems. His research increasingly incorporates generative AI approaches to solve complex healthcare resource allocation problems. Professional service includes: Member and Reviewer at GMDS (German Society for Medical Informatics, Biometry and Epidemiology) since 2024 Reviewer for European Federation for Medical Informatics since 2024 Reviewer for IEEE Transactions on Network and Service Management since 2020 Reviewer for Elsevier Journals including Engineering Applications of Artificial Intelligence since 2017 Professor Potluri actively supervises B.Sc, M.Sc, and PhD students in medical informatics, AI applications, generative AI, clinical decision support systems, and healthcare logistics. His current research projects focus on hospital resource and process optimization, synthetic health data generation, digital health equity studies, and generative AI in healthcare. He previously held research positions as Junior Research Group Leader at University Hospital Jena, Project Leader at Otto von Guericke University Magdeburg, and Research Assistant for EU Projects, building a strong foundation for his current interdisciplinary work.
Ravishankar K. Iyer is the George and Ann Fisher Distinguished Professor at the University of Illinois at Urbana-Champaign, holding appointments in Electrical and Computer Engineering, Computer Science, Coordinated Science Laboratory (CSL), National Center for Supercomputing Applications (NCSA), Carle Illinois College of Medicine, and the Carl R. Woese Institute for Genomic Biology. He is also a Research Affiliate at Mayo Clinic and Yeoh Ghin Seng Visiting Professor at National University Health System, Singapore. His work bridges Dependable and secure systems Computational genomics Health analytics Machine learning AI in personalized medicine His research focuses on systems and software combining deep measurement-driven analytics and machine learning for applications in critical infrastructure resilience , healthcare cybersecurity , and genomic medicine . Key article trends include Smart grid security frameworks EEG-based cognitive modeling Pharmacogenomic drug response prediction Hardware/software fault tolerance Malware detection in autonomous systems Genomic data analysis Scientific accolades include Fellowships in AAAS, IEEE, and ACM, the IEEE Emanuel R. Piore Award, ACM Outstanding Contributions Award, and an Honorary Doctorate from Toulouse Sabatier University. He leads the DEPEND Group at CSL and directs the NSF-funded Illinois/Mayo Center for Computational Biotechnology and Genomic Medicine.
Nursalim Hadi served as Assistant Professor at the University of Indonesia's College of Computer Science from 1995 to 1999, holding multiple leadership roles including Head of the Master in Information Technology program and General Secretary of the Graduate School in Computer Science. His educational background includes: Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (1995), specializing in Fault-Tolerant Distributed Systems, Distributed Database Systems, and Software Engineering Bachelor of Engineering in Computer Science from Bandung Institute of Technology (1986), focusing on Digital Image Processing Hadi's research spans distributed systems, database technologies, and applied computer science. His work demonstrates strong industry-academia integration, particularly in digital security frameworks for e-commerce and SCADA protocol standardization for critical infrastructure like power plants. Recent interests emphasize data analytics and big data applications. His publication record shows consistent focus on practical implementations of computer science in national infrastructure projects, with early work addressing Indonesia's power sector challenges through industrial control systems standardization, later evolving toward digital security frameworks for emerging e-commerce applications. No scientific awards were documented in the source material. Hadi secured multiple competitive grants from the Indonesian National Research Council (DRN) and University Research for Graduate Education, including projects on Digital Security Frameworks for E-Commerce (1997-1999), X-Group Communication protocol development (1996-1998), and SCADA Standard Protocol for Electrical Transmission Networks (1995-1997). While specific advisees aren't listed, he led the Master in Information Technology program and Pre-Magister in Computer Science program. During his academic tenure, Hadi founded Delta Dimensi Datalisis and Raginar Intermedia, establishing direct links between university research and commercial technology implementation, particularly in network infrastructure and database solutions for Indonesian enterprises.
Michael Short is a Professor and Acting Associate Dean (Research & Innovation) at Teesside University, leading the Centre for Sustainable Engineering within the School of Computing, Engineering and Digital Technologies (SCEDT). He holds visiting professorships at VIT Chennai, India, and serves as an External Examiner at Robert Gordon University (RGU), Scotland. With a BEng in Electronic and Electrical Engineering (1999) and a PhD in real-time robot control (2003), both from the University of Sunderland, he has over 20 years of academic and industrial experience. His roles include research leadership, teaching in embedded systems, and international collaborations. Michael’s research focuses on Control Engineering and Systems Informatics applied to sustainable systems, addressing challenges in energy efficiency, smart grids, and battery recycling. His work spans projects funded by FP7, H2020, and Innovate UK, totaling over £3.0M. He has authored/co-authored over 200 publications, with an H-index of 27 and 55 i10-index citations. Notable achievements include eight Best Paper Awards at international conferences and supervising 11 PhD completions. His consultancy expertise spans industrial technology applications, including marine systems, smart energy, and demand response. Awards and recognitions include being named one of the UK’s top 10 influencers in the Net Zero agenda (2022). He serves on editorial boards for journals like International Journal of Energies and actively reviews for IEEE, IET, and ACM conferences. Michael’s current projects include hydrogen infrastructure development, EV battery recycling, and smart port energy systems. His research themes emphasize sustainable engineering solutions, digital trade innovation, and decarbonization strategies for industry and cities.
Rameez Asif is an Associate Professor at the School of Computing Sciences, University of East Anglia (UEA) since 2021. He specializes in cybersecurity, IoT, and AI, with a focus on securing interconnected systems and developing robust frameworks for threat detection and resilient IoT architectures. Previously, he held an Assistant Professor position at the University of Strathclyde (Glasgow, UK), where he researched energy systems and smart grids. He earned his PhD in Digital Backpropagation for Secure Optical Networks from Friedrich-Alexander University Erlangen-Nuremberg (Germany) in 2012, with additional research experience at DTU (Denmark) and the University of Cambridge (UK). His research interests span 5G/6G networks, wireless security, blockchain, cryptocurrencies, and metaverse technologies. He actively supervises PhD candidates and postdoctoral researchers in interdisciplinary projects. His work emphasizes practical applications, including secure localization algorithms, intrusion detection systems leveraging AI and blockchain, and optimizing IoT for smart cities. He serves on editorial boards for journals like MDPI and Frontiers Media. Rameez has over 130 publications in top-tier conferences and journals. His research addresses critical challenges in cybersecurity, edge computing, and emerging technologies, with collaborations across multiple countries and industries.
Yong Fu is a Professor and Tennessee Valley Authority (TVA) Endowed Professor in the Department of Electrical & Computer Engineering at Mississippi State University (MSU), affiliated with the Bagley College of Engineering. His research focuses on power systems optimization, renewable energy integration, and smart grid technologies. He holds a Ph.D. from Illinois Institute of Technology (2006), and M.S. and B.S. degrees from Shanghai Jiao Tong University (2002 and 1997, respectively). His research interests include energy infrastructure interdependencies, electric ship power systems, and high-performance computing applications in power system operations. Recent work emphasizes resilient voltage management, price forecasting with machine learning, and microgrid protection strategies. He has contributed to over 50 peer-reviewed articles addressing challenges in smart grid resilience, renewable integration, and grid security. His publications highlight advancements in parallel optimization algorithms, fault detection systems for digital substations, and stochastic models for wind power uncertainty. Ongoing research explores cyber-physical system interdependencies and decentralized operation frameworks for interconnected grids.
Dr. Danda B. Rawat is an Associate Dean for Research & Graduate Studies and Full Professor in the Department of Electrical Engineering & Computer Science at Howard University's College of Engineering and Architecture (CEA). He leads several initiatives, including the Howard University Data Science & Cybersecurity Center and the DoD Center of Excellence in AI/ML. His research focuses on cybersecurity, machine learning, and wireless networking for emerging systems like IoT, smart cities, and tactical autonomy. Dr. Rawat has secured over $110M in research funding and graduated 39 PhD students, many from underrepresented groups. He holds numerous awards, including the NSF CAREER Award and DHS Scientific Leadership Award. Education : PhD in Computer Science from Old Dominion University. Research Interests : Cybersecurity, AI/ML, wireless networking, tactical autonomy, federated learning, and quantum computing. His work addresses challenges in secure autonomous systems, edge intelligence, and ethical AI. Awards : Includes NSF CAREER, DHS Leadership Award, Presidents’ Medal (Howard University), and IEEE recognitions. He is a Fellow of the IET and ACM/IEEE Distinguished Lecturer. Labs & Teams : Directs the CWiNs Lab, DoD CoE-AIML, and RITA (UARC). Active in federal consortia linking HBCUs to defense innovation. Grants : Over $110M as PI, including a historic $90M USAF contract for RITA, the first HBCU-led UARC.
Mark Gondree is an Associate Professor in the Computer Science Department at Sonoma State University, with research expertise in security pedagogy, applied cryptography, and secure computation. He holds a PhD from UC Davis and has received multiple teaching awards including the CSU Student Success Analytics Certificate and POGIL Activity Clearinghouse recognition. His research explores cybersecurity education methods, cloud data geolocation, and industrial control system vulnerabilities. Recent publications focus on curriculum development for computer security concepts and efficient cryptographic protocols. Awards: CSU Student Success Analytics Certificate (2024) POGIL Activity Clearinghouse Publications (2022) QuARRY Repository Award (2020) He actively advises graduate and undergraduate researchers on projects ranging from fuzz testing to pseudorandom number generator analysis. Current grants include NSF funding for CS teacher preparation programs and cloud security research. As department chair, he oversees curriculum development and leads the Computer Science Colloquium series.
Xavier Chesterman is a postdoctoral researcher at Vrije Universiteit Brussel (VUB), Belgium, affiliated with the Department of Informatics and Applied Informatics and the Acoustics & Vibration Research Group. His work focuses on advancing wind turbine operational reliability through data-driven condition monitoring solutions. His research spans wind turbine condition monitoring , anomaly detection , and data fusion with emphasis on rare failure scenarios and slowly progressing damage. Key methodologies include machine learning applications for SCADA data analysis, multi-modal sensor integration, and fleet-wide diagnostic systems for both onshore and offshore wind farms. Analysis of Chesterman's 8 research outputs (2021-2025) reveals a clear trajectory toward scalable solutions for wind farm operations. His recent 2025 publications demonstrate significant progress in farm-wide drivetrain event tracking and offshore turbine failure prediction, building upon foundational 2023-2024 work in normal behavior modeling and pattern mining techniques. As a postdoctoral researcher under Professor Jan Helsen's supervision, Chesterman actively contributes to VUB's wind energy research initiatives. His work suggests involvement in funded projects related to renewable energy analytics, with opportunities for collaboration in wind turbine health monitoring systems and predictive maintenance frameworks. The Acoustics & Vibration Research Group provides a specialized environment for vibration analysis and condition monitoring innovation in renewable energy systems.
Eslam Amer is a Senior Research Fellow at the Portsmouth AI and Data Science Centre, School of Computing, Faculty of Technology, University of Portsmouth. He holds a Ph.D. in Semantic Web-enhanced web search engines from Helwan University (2012), an MSc in Web Search Engine Optimization (2005), and a BSc in Computer Science (2000). Ph.D. : Enhancing Efficiency of Web Search Engines via Semantic Web, Helwan University (2012) MSc : Development in Web Search Engine Optimization, Helwan University (2005) BSc : Computer Science, Helwan University (2000) His research focuses on applying Artificial Intelligence and Machine Learning to solve challenges in Natural Language Processing , Cybersecurity , Medical Informatics , and Industrial Control Systems . He explores Deep Learning architectures for behavioral analysis in ransomware detection, enhances Semantic Web techniques for search engine efficiency, and develops Data Science frameworks for medical diagnosis and social media analysis. Recent publications highlight trends in AI-driven risk assessment for food safety, anomaly detection in ICS, and advanced NLP for misogyny and fake news detection. His work spans deep ensemble models , grammar-based feature engineering , and behavioral modeling of malware attacks. He leads research at the Portsmouth AI and Data Science Centre , focusing on collaborative projects across disciplines like healthcare, cybersecurity, and industrial systems. His expertise aligns with cross-cutting themes in UN Sustainable Development Goals related to technology and education.
Stefan Čubonović serves as a Lecturer at the Department of Electrical Power Engineering within the Faculty of Technical Sciences, University of Kragujevac, Serbia. His teaching portfolio includes core electrical engineering courses such as Power Transformers, DC and Asynchronous Machines, Synchronous Machines, Distribution Networks, and Electric Drives (2020-2022). His research centers on advanced power system analysis with specialization in distribution network state estimation under non-ideal measurement conditions and machine learning applications for hydroelectric power forecasting. Current work focuses on mitigating non-Gaussian noise impacts in state estimators and developing hybrid neural network models for renewable energy output prediction, demonstrating strong interdisciplinary integration of power engineering and data science. Recent publications (2022-2025) reveal a clear trajectory toward intelligent grid solutions, with 70% of work addressing hydroelectric forecasting and state estimation challenges. The research combines theoretical numerical methods with practical implementations for distribution system monitoring and optimization. Scientific recognition includes: 2nd place in best papers category at ENERGETIKA 2024 His experimental work spans synchrophasor device development for low-voltage networks, spatial sensor imaging platforms, and graphical monitoring environments for distribution system dynamics, reflecting hands-on engagement with power system instrumentation and data acquisition.
Dr. Nilufer Tuptuk is a Lecturer in Security and Crime Science at University College London (UCL). Her work bridges cybersecurity, crime science, and socio-technical systems, focusing on threats in emerging technologies like connected and autonomous vehicles, digital twins, and industrial control systems. Master of Science, University College London (2014) PhD, University College London (2019) Her research spans cybersecurity , cybercrime , and cyber-physical systems , with a strong emphasis on anomaly detection , evolutionary computation , and policy development . Recent publications analyze adversarial attacks on autonomous systems, socio-technical security models, and security challenges in water systems. Dr. Tuptuk collaborates extensively with researchers like John M. Watson and Angela Brown, contributing to transdisciplinary initiatives such as PETRAS and UCL’s Department of Security and Crime Science. She applies machine learning and optimization techniques to enhance security frameworks across industries.
Hua-Liang Wei is a Senior Lecturer at the University of Sheffield 's School of Electrical and Electronic Engineering. He leads two innovative research labs: the Dynamical Modelling, Data Mining and Decision Making (3DM) and the Digital Medicine & Computational Neuroscience (DMCN) Research Groups. Specializes in system identification for nonlinear dynamics Develops interpretable AI for healthcare applications Active in space weather and environmental forecasting His methodological expertise spans NARMAX modeling, wavelet neural networks, and multiresolution analysis. Collaborations include Sheffield Teaching Hospitals NHS Trust, multiple University of Sheffield departments (Chemistry, Oncology, Psychology), and international institutions like Beihang University. Scientific Awards include STFC and NERC grants for radiation belt modeling and environmental systems research, EU Horizon 2020 funding, EPSRC Platform grants, Royal Society support, and medical charity partnerships. Recent publications focus on hybrid wavelet-LSTM for wind power forecasting EEG analysis in epilepsy and Alzheimer's domain adaptation for fault diagnosis interpretable models for medical data covering applications from renewable energy to clinical diagnostics.
