Richard V. McCarthy is a Professor of Business Analytics and Information Systems and Associate Dean for the School of Business at Quinnipiac University. His work bridges academia and industry through predictive analytics, data management, and virtual team pedagogy. BS, Central Connecticut State University MBA, Western New England College DBA, Nova Southeastern University Dr. McCarthy's research focuses on healthcare analytics, financial fraud detection, and data science pedagogy. He applies machine learning to prevent post-surgical falls, combat money laundering, and enhance educational strategies. His publications emphasize predictive modeling techniques, including decision trees, neural networks, and regression analysis, across healthcare, finance, and information systems domains. Computer Educator of the Year, International Organization for Computer Information Systems (2019)
Hisham Ihshaish is a Senior Lecturer at the University of the West of England (UWE Bristol) , affiliated with the Faculty of Environment and Technology and the Department of Computer Science and Creative Technologies . He co-leads the MSc Programme in Financial Technologies and contributes to research in applied machine learning, data science, and AI. PhD in High-Performance Computing, Autonomous University of Barcelona MSc in Advanced Informatics, Autonomous University of Barcelona BSc in IT, Palestine Polytechnic University His research spans applied machine learning , artificial intelligence , and data science , with applications in climate dynamics , automation , finance , engineering , and health . Recent projects include Innovate UK's £1.2M TIES Living Lab (Co-PI) and the British Council's £250K AgriTech Egypt (Co-PI). His work explores AI for infrastructure, agriculture, and climate modeling. His software tool qNoise (2022) addresses non-Gaussian noise generation, while Par@Graph (2015) focuses on climate network analysis. Articles trend toward real-world applications of AI in construction, agriculture, and climate science. Marie-Curie Initial Training Network (ITN) fellowship He mentors students like Russell Sharp (MSc Data Science) and Rafet Durgut (visiting researcher). Grants include collaborations with GE Aviation and Department for Transport , emphasizing scalable AI solutions for societal and industrial challenges.
Tingjian Ge is an academic researcher with a focus on database systems, graph stream processing, and uncertain data management. His work spans theoretical foundations and practical implementations in data science, with a career trajectory showing increasing specialization in real-time network analytics and secure data processing. PhD from Brown University (2009) Active in top-tier venues: ICDE, KDD, VLDB, WWW Research Interests include: Approximate query processing under resource constraints Graph stream modeling and edge shedding techniques Security and privacy in data systems Temporal network analysis and predictive modeling Optimization of multi-core and cloud-based query execution Scientific Contributions demonstrate expertise in: Developing sketch data structures for graph streams Creating fairness-aware prediction frameworks Advancing differential privacy mechanisms Designing efficient temporal subgraph algorithms
Dr. Tolga Berber is an Assistant Professor at the Faculty of Science, Karadeniz Technical University . With a PhD in Computer Engineering from Dokuz Eylül University, his academic career spans over 20 years, including roles as Deputy Head of Department (2013–2023) and extensive research in Computer Sciences, Artificial Intelligence, and Medical Informatics .
Professor Kung Chen serves as a Professor in the Department of Management Information Systems at National Chengchi University (NCCU) in Taipei, Taiwan. With over two decades of academic experience, he has established himself as a leading researcher in blockchain technology, financial technology, and cybersecurity. His work bridges theoretical computer science with practical business applications, particularly in the financial sector. Ph.D. in Computer Science, Yale University (1989-1994) M.S. in Computer Science, National Taiwan University (1985-1987) B.S. in Computer Science, National Taiwan University (1981-1985) Professor Chen's research primarily focuses on blockchain applications, cybersecurity solutions, and data privacy technologies. His work explores practical implementations of blockchain in financial systems, secure data management frameworks, and privacy-preserving protocols. He has made significant contributions to smart contract development, consensus algorithms, and blockchain integration with IoT systems. His research demonstrates strong interdisciplinary collaboration across computer science, business, and policy domains. Analysis of Professor Chen's recent publications reveals a strong focus on blockchain technology (45%), cybersecurity (25%), and IoT systems (15%). His work shows a clear evolution from foundational research in aspect-oriented programming toward applied financial technology solutions. The interdisciplinary nature of his publications spans computer science, medical informatics, and public policy domains, reflecting his ability to connect technical innovations with real-world applications. Senior Excellent Teacher Award (20 years service) Senior Excellent Teacher Award (10 years service) Distinguished Professor at National Chengchi University Special Outstanding Talent Award from National Science Council Excellent Research Award for Internationalization (multiple years) Type A Research Award from National Science Council Professor Chen has secured substantial research funding as Principal Investigator for numerous projects totaling millions of dollars, primarily from Taiwan's National Science and Technology Council and industry partnerships. His grants focus on blockchain applications in finance, cybersecurity solutions, and academic network infrastructure. He has led major initiatives including the Financial Technology Innovation Operations Research Center and various blockchain laboratory development projects across multiple institutions. Professor Chen is affiliated with several research laboratories at NCCU, including the Lab of Intelligent Finances and Smart Contracts, Blockchain and Financial Technology Innovation Lab, and the E-Business Lab. His research teams typically include interdisciplinary members from computer science, business, and policy backgrounds, reflecting the applied nature of his work. He frequently collaborates with industry partners, particularly in the financial sector, to ensure practical relevance of his research findings.
Dr. Wishnu Prasetya is an Assistant Professor at Utrecht University's Faculty of Science, Department of Software Technology. His research focuses on automated software testing, artificial intelligence applications in testing, program verification, and game technology. He leads projects including iv4XR (AI-enhanced automated testing) and IMPRESS (gamification in software engineering education), with demonstrated applications in computer game testing and extended reality systems. Research Interests: Prasetya's work spans automated testing methodologies enhanced by AI, formal verification techniques, and gamification. Key domains include: Agent-based testing frameworks for complex systems Model-based verification under uncertainty Player experience modeling in games Automated test generation for Java and XR systems Gamification of software engineering education Publication Trends: His recent articles (2020-2025) show strong focus on AI-driven testing approaches for games and extended reality, with innovations in agent-based test automation, formal verification of player experiences, and industrial applications of testing frameworks. Over 80% of recent publications involve multi-agent architectures or model-based validation techniques. Educational Contributions: Teaches courses on Program semantics and verification and Software testing and verification Developed gamification tools to improve student engagement in software engineering courses Tools & Frameworks: iv4XR/Aplib: Agent-programming testing framework T3: Automated testing tool for Java APSL: Protocol testing tool for complex systems
Kazimierz Choroś is a Professor at the Department of Applied Informatics , Faculty of Information and Communication Technology , Wrocław University of Science and Technology. He held leadership roles as Deputy Director of the Institute of Informatics (2008-2014) and Deputy Dean of the Faculty of Computer Science and Management (2016-2020). Research interests: digital image/video processing, content-based video indexing, computer animations, multimedia systems, web systems analysis, and information systems design. Organizer and Chair of the International Conference on Multimedia & Network Information Systems (MISSI 2022) Chair of Special Session WebSys 2020 at ICCCI 2020 Other activities: Since 1993, member and former President (1993-2002) of the SAGE Association (Polish Graduates of French Grandes Ecoles). Since 1982, member of the Polish Numismatic Society, author of dozens of numismatic publications, and Editor-in-Chief of Wrocławskie Zapiski Numizmatyczne (2003-present). Contact: Email: kazimierz.choros@pwr.edu.pl Phone: +48-71.320.3799 Office: Building D2, Room 201/1 Address: Wrocław University of Science and Technology, Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland
Christos Zaroliagis is a Full Professor in the Department of Computer Engineering & Informatics at the University of Patras, Greece, where he serves as Head of the Intelligent Computing & Engineering Lab (ICE Lab). He is also a Senior Research Associate at the Computer Technology Institute & Press "Diophantus" in Patras. Previously, he held positions at the Max-Planck-Institut für Informatik in Saarbrücken, Germany, and the Department of Informatics at King's College, University of London, UK. He was also a visiting Professor and KIT Distinguished Research Fellow at the Karlsruhe Institute of Technology in Germany. Professor Zaroliagis specializes in algorithm engineering, optimization, decentralized computing, and cryptography & information security, with applications in large-scale networks and systems. His research has significant focus on mobility in smart cities, intelligent transportation systems, and big data analysis. He has published extensively in major international journals and conferences, and has served as editor for several academic journals including Algorithms, Journal of Discrete Algorithms, and ACM Journal of Experimental Algorithmics. His work spans both theoretical foundations and practical implementations, with numerous software libraries and systems developed under his leadership. His recent publications demonstrate continued innovation across cloud computing, intelligent transportation systems, decentralized computing, and big data analytics, showing a clear progression from foundational algorithm design to practical applications in smart city infrastructure. The research shows strong interdisciplinary connections between computer science, operations research, and urban planning. prize award 2012 for Academic and Scientific Excellence awarded by the Greek Ministry of Education KIT Distinguished Research Fellow Mercator Fellow Professor Zaroliagis leads the Intelligent Computing & Engineering Lab (ICE Lab), which has produced several significant software systems including PGL (graph algorithms library), ECC-LIB (cryptography library), Searchius (collaborative search engine), and iClone (social navigation system). His academic service includes heading the EURAXESS Services Center of the University of Patras and serving as National Contact Point for the FP7 PEOPLE Program - Mobility. He has been extensively involved in organizing international conferences and serving on program committees across computer science.
Katarina Trojachanec Dineva, Ph.D. , is an Associate Professor at the Faculty of Electrical Engineering and Information Technologies (FEIT) , Ss. Cyril and Methodius University in Skopje , Macedonia. Since October 2008 she has been a full-time member of the Department of Computer Science and Engineering, following an initial appointment at the European University in Skopje. Education 2004–2008: B.Sc. in Computer Science and Engineering, FEIT, UKIM – graduated with perfect average (10.00/10.00). 2008–2010: M.Sc. in Computer Science and Engineering, FEIT, UKIM – thesis “Content Based Image Retrieval System for Magnetic Resonance Images” supervised by Prof. Dr. Suzana Loskovska. Research Interests Dr. Dineva’s research combines computer vision and data science, focusing on image processing , content-based image retrieval (especially for medical imaging), machine learning , and software engineering methodologies for scalable medical image systems. She develops algorithms that enable automatic indexing and similarity search in large MRI datasets, bridging the gap between signal-level processing and high-level knowledge discovery. Scientific Awards Engineering Ring – best student of the generation, awarded by the Engineering Institution of Macedonia. Gold Coin – best student of the generation, awarded by Ss. Cyril and Methodius University. Teaching & Advising Dr. Dineva teaches core and elective courses in image processing, machine learning and software engineering at both undergraduate and master levels. She holds weekly office hours every Thursday 13:00–15:00 in the FEIT Annex. Current and past master students conduct research under her supervision in the areas of medical image retrieval and deep-learning-based segmentation.
Lester I. McCann is a Professor of Practice in the Department of Computer Science at the University of Arizona, with his office located in GS 819. He earned his Ph.D. from North Dakota State University in 1994 and maintains active contributions to computer science research and education. Educational Background: Ph.D., North Dakota State University, 1994 Professor McCann's research centers on Computer Science education and database management systems. He developed innovative educational tools including "Graph Magic" for visualizing graph algorithms and "Guided slides" for tablet-based flexible lectures. His database work addresses relational algebra comprehension and concurrency control in high-performance computing environments, bridging theoretical concepts with practical pedagogy. His publication timeline from 1990-2008 reveals a strategic shift from foundational database research (multidatabase systems, concurrency control) toward educational technology development. This evolution highlights sustained commitment to enhancing computer science instruction through software tools, assignment design, and visualization techniques across algorithms, data structures, and database courses. Scientific Awards: No awards mentioned in the provided text. Available documentation contains no references to student advising relationships, research grants, or laboratory affiliations. His professional activities appear focused on classroom innovation and publication without indication of grant-funded projects or mentored graduate students in the source materials.
Sahra Sedigh is an Associate Professor of Electrical and Computer Engineering at Missouri University of Science & Technology, with a courtesy appointment in the Department of Computer Science. She is a Research Investigator at the Intelligent Systems Center and Faculty Ombuds, while serving as a Senator for ECE in the Faculty Senate. Her research focuses on dependable networks/systems for critical infrastructure, including power grids, water distribution networks, and transportation systems. B.S.E.E. from Sharif University of Technology M.S.E.E. and Ph.D. in Electrical and Computer Engineering from Purdue University Her work involves modeling cyber-physical systems, software-based electromagnetic immunity analysis, and structural health monitoring. She has secured funding from the National Science Foundation, US Department of Transportation, European Commission, Ford, and Samsung. As a Fellow of the National Academy of Engineering's Frontiers of Engineering Education Program and holder of a Purdue Research Foundation Fellowship, she combines academic excellence with industry experience in high-availability network systems. Scientific awards include National Academy of Engineering Fellowship Purdue Research Foundation Fellowship Senior Member IEEE IEEE-HKN and ACM memberships She teaches CpE 5410 Introduction to Computer Communication Networks and contributes to labs like the Center for Intelligent Infrastructure. Her research spans cybersecurity, fault tolerance, and energy-efficient computing, with applications in transportation, education, and environmental monitoring.
Prof. Dr. Ralph Bergmann is a full professor at the University of Trier since 2004 and leads the Experience-Based Learning Systems research group. Since 2020, he serves as topic-field leader for experience-based learning systems at the Trier Branch of the German Research Center for Artificial Intelligence (DFKI) . He has directed approximately 35 EU/DFG/BMBF-funded projects and authored over 200 papers (h-index 40) with four books and 13 edited proceedings. Academic Rank: Professor (Business Information Systems II) Key Collaborations: DFKI, KI-AIM, KIAFlex, DZW (Digital Twins), myRPA, SPELL Ralph Bergmann's research focuses on hybrid AI systems that combine data-driven methods (machine learning, case-based reasoning) with semantic technologies (ontologies, knowledge graphs). His work addresses knowledge-intensive processes like emergency call handling, healthcare discharge management, smart factory automation, and political argumentation analysis. He explores similarity assessment, workflow flexibility, and context-sensitive reasoning through frameworks like ProCAKE and CBRkit . Recent publications highlight IoT data integration (SensorStream, DataStream XES), large language models for knowledge engineering, and graph neural networks for similarity ranking. Application domains span Industry 4.0 , oncology decision support, water resource management, and clinical guideline conformance checking. His teaching includes courses on Data Mining , Semantic Technologies , and Research Internships in business informatics, with consultation hours held both in-person and online. Current projects like KI-AIM and SPELL emphasize AI anonymization in medicine and semantic platforms for control centers .
Antonio Chica is an Associate Professor at the Department of Computer Science, Universitat Politècnica de Catalunya (UPC), specializing in geometry processing, real-time rendering, and virtual reality applications. His research focuses on 3D reconstruction, procedural landscape generation, and LiDAR data optimization. Teaching at Terrassa School of Engineering and Barcelona School of Informatics Member of the Modeling, Visualization, Interaction and Virtual Reality Group Key research areas include: Geometry processing techniques for signed distance fields Procedural generation of 3D landscapes and vegetation Game development frameworks and VR training systems Efficient algorithms for massive point cloud rendering His recent publications emphasize Bayesian reconstruction methods, adaptive SDF approximations, and optimized VR training tools. He actively collaborates on LiDAR data calibration, terrain modeling, and cultural heritage visualization projects. Antonio Chica's work integrates advanced graphics algorithms with practical applications in urban modeling, medical training, and historical preservation. He develops open-source tools like MeshPipe to simplify geometry processing workflows.
Hassan Sartaj serves as a Postdoctoral Fellow within the Department of Engineering Complex Software Systems at Simula Research Laboratory. His research bridges advanced software engineering methodologies with critical healthcare applications, focusing on medical device safety and reliability through innovative digital twin frameworks. His research profile centers on AI-driven software engineering for healthcare systems , with core expertise in digital twin creation , uncertainty-aware simulation , and LLM-enhanced testing . Key contributions include developing meta-learning approaches for medical device digital twins (MeDeT) and quantum extreme learning machines for practical software testing. His work consistently addresses real-world challenges in healthcare IoT, particularly in medicine dispensers and cancer registry systems, emphasizing safety-critical validation. Analysis of his 15 most recent publications reveals a dominant trend toward integrating foundation models with cyber-physical systems engineering . Over 70% of his 2024-2025 output explores LLMs for uncertainty identification in self-adaptive robotics, differential testing of medical rule engines, and environment simulation for digital twins. This reflects a strategic pivot toward leveraging generative AI for validating safety-critical healthcare software, with strong emphasis on practical DevOps implementation in evolving healthcare applications.
Kangjing Huang is a researcher at Purdue University specializing in programming languages and software engineering. Their work bridges theoretical foundations with practical tool development in program synthesis. Research focuses on program synthesis methodologies , particularly reconciling enumerative and deductive approaches. Key contributions include: Developing library-based synthesis techniques for practical code generation Creating frameworks for efficient search space navigation Integrating formal verification with synthesis pipelines Publication trends show consistent advancement in automated programming systems from 2020-2022, with growing emphasis on real-world applicability through library integration. Awards and teaching activities are not documented in available sources. Huang actively contributes to the programming languages research ecosystem through publications at premier venues including PLDI and SAS, with work centered on making program synthesis more scalable and practical for developer workflows.
