Joseph M. Zurada is a Professor in the Department of Computer Information Systems at the University of Louisville's College of Business. He has held visiting scholar positions at Edith Cowan University (Perth, Australia) and the University of Alberta (Edmonton, Canada). His academic career spans decades, with recent publications focusing on computational intelligence and data analytics applications in business and manufacturing systems. Education: DSc (Technical Sciences, Informatics) from Polish Academy of Sciences; PhD (Computer Science Engineering) from University of Louisville; MS (Electrical Engineering) from Gdansk University of Technology Research interests center on soft computing methods , streaming data analytics , and decision support systems , particularly in business intelligence and manufacturing optimization. His technical work bridges theoretical neural systems with practical enterprise solutions. Recent publications (2019-2021) demonstrate consistent focus on data-driven decision making , with innovations in text classification , adverse event detection , and price prediction models using hybrid machine learning approaches. Scientific Awards Distinguished Research and Development Award (2017, 2011) Faculty Excellence Award (2017, 1998) President's Award (2017, 1981) Outstanding Scholarship Award (2017, 1996) As an educator, he teaches core courses in Data Mining , Machine Learning , and Infrastructure Technologies , contributing to graduate programs with his technical expertise.
Prof. Mohsen Asadnia is a biomedical and mechatronics engineer and full professor at Macquarie University's School of Engineering, serving as director of Master by Research in the Faculty of Science and Engineering. With over 20 years of experience, his work focuses on smart materials, sensors, and biomedical engineering applications. He holds a PhD from Nanyang Technological University and postdoctoral experience at MIT. Research interests include biomimetic sensors, energy applications, and proteomics. He leads projects on CO₂ capture, lithium extraction membranes, and wearable sensors. Collaborations span fields like renewable energy, AI in mining, and inner ear theragnostics. Notable contributions include 3D-printed prosthetic devices and advanced sensor technologies. His work integrates engineering, materials science, and environmental sustainability, with over 200 peer-reviewed publications.
Mubashir Ali is an Assistant Professor in Computer Science, actively contributing to interdisciplinary research. His work intersects Natural Language Processing Machine Learning Biometrics Public Health Informatics Smart City Technologies with focus on addressing UN Sustainable Development Goals through computational methods.
Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.
Argimiro Alejandro Arratia Quesada is a Professor at the Universitat Politècnica de Catalunya-BarcelonaTech (UPC), affiliated with the IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group and the SOCO - Soft Computing sub-group . He works in the Department of Computer Science under the College of Industrial, Aerospace and Audiovisual Engineering (ESEIAAT) . Research Interests: Time series analysis, machine learning, descriptive computational complexity, and financial engineering. Projects: Developed the clustAnalytics R package for network clustering assessment, contributed to Bayesian modeling of pandemic data, and designed entropy-based portfolio optimization techniques. Conference Participation: Actively involved in the International Conference on Time Series and Forecasting (2021-2023) and the Catalan Association for Artificial Intelligence conferences. Scientific Output Trends: Recent publications span finance (portfolio optimization, cryptocurrency forecasting), epidemiology (Covid-19 burden estimation), network science (clustering evaluation), and mathematical finance. Methodologies emphasize statistical inference, entropy optimization, and machine learning.
Dr. Martha Ivon Cárdenas Domínguez is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Science. She is an active member of the IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, as well as the SOCO (Soft Computing) research group. Education: BSc in Mathematics PhD in Artificial Intelligence (UPC) Master's in Artificial Intelligence Her research focuses on applying computational intelligence and machine learning techniques to biomedical data analysis, particularly for pharmacoproteomic applications involving G protein-coupled receptors. She specializes in kernel-based methods and data visualization techniques for sequence analysis and classification tasks. Recent publications demonstrate her expertise in manifold learning, subtype discrimination of GPCRs, and phylogenetic tree integration with visualization techniques. Her work addresses challenges in classification errors, mislabeling, and sequence transformation for complex biomedical datasets. Dr. Cárdenas employs advanced visualization strategies to explore metabotropic glutamate receptors and analyze protein sequence overlaps through manifold-based approaches. Her research bridges computational methods with biomedical applications, particularly in personalized medicine contexts. She actively contributes to competitive R&D+i projects and participates in international conferences. Her professional activities include 5 conference presentations, 2 book chapters, and 2 journal articles focusing on data science applications in bioinformatics. Dr. Cárdenas utilizes computational intelligence for analyzing receptor sequences and has developed innovative approaches for visualizing complex biological data structures. Her work has implications for drug discovery and personalized treatment development through enhanced understanding of receptor classification.
Massimiliano M. Schiraldi is a Full Professor of Industrial Plants at the University of Rome Tor Vergata, where he teaches Industrial Plants and Production Management at the School of Engineering. He also teaches Lean Production and Operations Management in the Master's program in Business Engineering. His academic appointment is in the Department of Business Engineering (IIND-05). Professor Schiraldi received his PhD in Management Engineering in 2003 and has been teaching Operations Management subjects since 2000. His research interests span multiple areas of industrial engineering and operations management, including production systems management, logistics and supply chain optimization, industrial plant design, production cost reduction techniques, and production information systems. He has pioneered approaches in Lean Production, Six Sigma standards, and advanced production management techniques with hybrid kanban systems. Professor Schiraldi's recent publications demonstrate his continued leadership in operations management, with a focus on digital transformation, performance measurement, Industry 4.0 technologies, and the human factor in operations excellence. His work bridges theoretical research with practical applications in manufacturing and service industries. Co-Editor of the International Journal of Engineering Business Management (Sage) Area-Editor (Supply Chain Management & Logistics) of Computers and Industrial Engineering (Elsevier) Reviewer for many primary international journals including International Journal of Production Research and International Journal of Logistics Systems and Management Professor Schiraldi has supervised over 20 PhD students and more than 300 undergraduate theses, many of which were conducted in collaboration with major Italian and multinational companies. He has participated in over 50 research and consulting projects with industrial companies at national and international levels. He established the Operations Excellence Think Tank and has served in numerous administrative roles at the university, including as Rector's Delegate for major events and coordinator of university merchandising and social media. He maintains strong industry connections with companies including Amazon, Ferrero, Calzedonia, Gucci, Ferrari, Johnson & Johnson, and many others across various sectors. Internationally, he serves as a Guest Professor at Guizhou University of Finance & Economics in China and a Visiting International Fellow at the University of Essex in the UK.
Allel Hadjali is a Full Professor in Computer Science specializing in Data Engineering at ISAE-ENSMA (École Nationale Supérieure de Mécanique et d'Aérotechnique) in Poitiers, France. He is affiliated with the Laboratory LIAS (Laboratoire d'Ingénierie des Applications de la Connaissance et des Systèmes) at ISAE-ENSMA. His academic career includes progression from Associate Professor to his current Full Professor position, with extensive teaching experience across multiple computer science domains. Professor Hadjali's research falls within the data science domain, with particular focus on Exploitation, Extraction, and Recommendation (E2R). His work applies Computational Intelligence and Soft Computing techniques to massive data exploitation and analysis, including flexible querying approaches (Skyline, Gradual, and Bipolar queries), modeling and querying uncertain/incomplete data, cooperative answering techniques, and data reduction through linguistic summaries. He also conducts research in recommendation systems (learning-based and group recommendation) and extraction techniques (mining gradual patterns), along with related interests in data quality, intelligent systems, and crowdsourced data management. His publication record demonstrates consistent contributions to top-tier journals and conferences, with recent work focusing on skyline query processing, uncertain data management, RDF knowledge bases, and explainable AI. His research shows a clear trajectory from foundational work in fuzzy logic and uncertain databases toward more applied research in semantic web technologies and machine learning explainability. Professor Hadjali serves on the editorial boards of several prestigious journals including the Journal of Smart Environments and Green Computing, Sensors Journal, and the Universal Journal of Aeronautics and Aerospace Research. He has also organized special issues on topics such as uncertainty in cloud computing and managing uncertain data. At ISAE-ENSMA, Professor Hadjali teaches courses including Formal aspects of software engineering, Language interpretations and compilation, Programming languages, and Data management and exploitation. Previously as an Associate Professor, he taught courses on object modeling, distributed algorithms, operating systems, and advanced databases focusing on preferences and uncertainty. He leads the Data Engineering team within the Laboratory LIAS, which focuses on developing computational intelligence approaches for modern data challenges. His current projects include work on data quality (QDoSSI project funded by CNRS Mastodons 2016-2018) and research actions in GDR MADICS 2018 related to scientific data quality.
Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.
Coen De Roover is a Professor at the Software Languages Lab (SOFT) of Vrije Universiteit Brussel (VUB) since October 2015, where he leads the Code Analysis and ManiPulation (CAMP) subgroup. His research focuses on program analysis design and its applications to software quality, including soft verification of contracts, incremental abstract interpretation, vulnerability detection in infrastructure code, and mining change patterns in commits. Key Research Areas: static analysis, dynamic analysis, mining software repositories, software engineering tools, security, and empirical studies. Conference Roles: Organizing Committee Chair for ECOOP Academy (2026), Steering Committee Member for GPCE, Program Committee Member for ICFP, SANER, and VMCAI, and Session Chair for multiple research tracks. Notable Contributions: Publications on WebAssembly analysis, Ansible security, concolic testing, and abstract interpretation frameworks. He also serves as Programme Director for the Bachelor in Computer Science at VUB since 2019-2020.
Ippei Obayashi is a Professor at Okayama University's Center for artificial intelligence and mathematical data science, with a visiting professorship at Tohoku University's Advanced Institute for Materials Research (AIMR). His academic career spans prestigious institutions including RIKEN, Tohoku University, and Kyoto University, where he earned his Doctor of Science degree. Okayama University, Center for AI and Mathematical Data Science, Professor (2021-present) RIKEN, Center for Advanced Intelligence Project, Researcher (2018-2021) Tohoku University, Institute for Advanced Materials Science, Associate Professor (2018) Tohoku University, Advanced Institute for Materials Science, Assistant Professor (2015-2018) Kyoto University, Research Fellow (2010-2015) Educational Background: Kyoto University, Graduate School of Science, Department of Mathematics and Mathematical Analysis (2006-2010, Doctoral) Kyoto University, Graduate School of Science, Department of Mathematics and Mathematical Analysis (2004-2006, Master's) Kyoto University, Faculty of Science (2000-2004, Bachelor's) Professor Obayashi's research focuses on topological data analysis (TDA) , particularly persistent homology and its applications, alongside dynamical systems theory. His work bridges pure mathematics with practical applications in materials science, where he has developed innovative methods to analyze complex material structures. He has made significant contributions to understanding magnetic materials, amorphous structures, and crystal formation through topological approaches, often integrating machine learning techniques with traditional mathematical analysis. His research has practical implications for energy materials, battery technology, and materials characterization. His publication record demonstrates a strong trend toward applying topological methods to solve real-world materials science problems, with increasing integration of machine learning techniques. Recent work shows sophisticated applications of persistent homology to analyze neutron scattering data, magnetic properties, and structural characteristics of materials, reflecting his ability to translate abstract mathematical concepts into practical analytical tools. Scientific Awards: JCS-JAPAN Excellent Paper Award, Ceramic Society of Japan (2020) 11th Sakuramai Research Encouragement Award, RIKEN (2020) Japan Society for Industrial and Applied Mathematics Best Author and Best Paper Awards (2017) 6th Fujiwara Hiroshi Mathematical Sciences Encouragement Award (2017) AIMR International Symposium Best Poster Award (2017) Professor Obayashi actively mentors graduate students through Okayama University's Graduate Student Program and leads research initiatives including the Japan Society for Industrial and Applied Mathematics Topological Data Analysis Research Group, which he chairs. His research is supported by multiple competitive grants, including Japan Society for the Promotion of Science (JSPS) funding for projects on mathematical data science and topological structure analysis. He collaborates extensively with researchers across disciplines, particularly in materials science and engineering. He is affiliated with the Center for artificial intelligence and mathematical data science (Angels) and the Cyber-Physical Engineering Informatics Research Division (Cypher) at Okayama University, where he leads efforts to develop and apply topological data analysis methods to complex scientific problems. His laboratory focuses on creating practical software tools like HomCloud for persistent homology analysis, bridging the gap between theoretical mathematics and applied scientific research.
Muhammad Shazzad Hossain is a Professor at the University of Western Australia , School of Earth and Oceans. With over 15 years of experience in offshore geotechnical engineering, his work focuses on foundation solutions for marine infrastructure and environmental sustainability initiatives. PhD (University of Western Australia, 2009) Australian Research Council (ARC) Future Fellow Core member of ISO TC67/SC7/WG7/P4 His research addresses: Seabed deformation mechanics Novel spudcan foundation design Dynamically installed fish anchors Repurposing decommissioned flowlines Plastic pollution mitigation Cutting-edge projects include computational modeling of calcareous soils and centrifuge testing for offshore applications. Industry collaborations span Korea (Daewoo, Samsung, POSCO), USA (Delmar Systems), Norway (NGI), France (TOTAL), and Singapore (Subsea 7). Scientific recognition includes: 2015 Vice-Chancellor’s Mid-Career Research Award 2013 Woodside Early Career Scientist of the Year 2012 ASCE Best Civil Engineering Paper Award 2012 Outstanding Young Investigator Award Secured $3.4M in research funding as lead investigator, including ARC Linkage Projects with contributions from Daewoo Engineering, Daewoo Shipbuilding, and Keppel Offshore. Delivered 33 invited seminars across China, Korea, Singapore, and USA.
Andrey Kuehlkamp is a Postdoctoral Research Associate at the Center for Research Computing (CRC) of the University of Notre Dame. He holds a Ph.D. from the same institution, focusing on iris biometrics. His research spans blockchain analytics, supply chain management applications of blockchain, computer vision, machine learning, and distributed systems. He is particularly known for contributions to biometric security, including post-mortem iris recognition and presentation attack detection. Educational Background: Ph.D. in Biometrics and Computer Vision, University of Notre Dame Research Focus: Dr. Kuehlkamp’s work bridges theoretical advancements in biometrics and practical implementations in blockchain technologies. His biometric research emphasizes forensic applications and the ethical use of AI in recognition systems. In blockchain, he explores trust frameworks, decentralized applications, and security protocols to enhance transparency and reliability. Articles Trends: His publications reflect a dual focus on biometrics and blockchain innovation. Early work (2014–2020) centered on iris recognition techniques, including post-mortem analysis and gender prediction challenges. Recent contributions (2022–2025) pivot toward blockchain’s role in trust systems, fraud detection, and cross-chain interoperability, demonstrating interdisciplinary collaboration between computer science and cybersecurity. Labs/Teams: As part of the CRC, he collaborates on projects requiring high-performance computing and data analysis, leveraging the center’s resources to tackle complex problems in distributed systems and forensic technology.
Dr. Estefania Lopez-Quiroga is an Associate Professor at the University of Birmingham's School of Chemical Engineering, specializing in Model-Driven Formulation Engineering. She leads the Centre for Doctoral Training in Formulation Engineering for Net Zero and coordinates Industry 4.0 modules for MSc programs. Her work integrates computational tools with process engineering to advance sustainable manufacturing in food, pharma, and FMCG sectors. Education: MEng in Mining Engineering, MSc in Mathematical Engineering, PhD in Applied Mathematics. She holds Fellow of the Higher Education Academy (FHEA) credentials. Research focuses on digital manufacturing, Industry 4.0 applications, and sustainable production. Key areas include model-based approaches for crystallization, freeze-drying, and production-scale optimization. Her work bridges soft matter physics with engineering solutions for product performance and process efficiency. Publications highlight sustainability assessments across production scales, energy-efficient processes, and computational modeling innovations. Awarded the IChemE Hutchison Medal 2020 for groundbreaking work on decentralized food manufacturing. Teaching responsibilities include undergraduate plant optimization and postgraduate Industry 4.0 modules. Supervises PhD/EngD students in formulation engineering, emphasizing real-world industrial collaboration. Labs/Teams: Active in the EPSRC Centre for Doctoral Training in Formulation Engineering, collaborating with industry partners on scalable and sustainable manufacturing solutions.
Marija Blagojević is a Full Professor at the Department of Information Technologies within the Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. With over fifteen years of experience in teaching and research, she has established herself as a leading academic in Information Technologies and Systems. Her work spans multiple domains including artificial intelligence, machine learning, and educational technologies, contributing significantly to both theoretical advancements and practical applications in these fields. Dr. Blagojević began her academic career at the Technical Faculty in Čačak in October 2007, initially conducting exercises for courses in Informatics Methodology and IT Applications-Practicum. She was appointed as an Assistant in June 2008 and has since advanced to her current position as Full Professor. Throughout her career, she has continuously expanded her expertise through various specialized courses including Oracle Academy courses in database design and programming, machine learning from Stanford University, and certifications in Huawei AI technologies. Her research interests primarily focus on the application of artificial intelligence techniques to solve complex problems across diverse domains. She has made significant contributions to neural network applications, developing models for predicting apricot yields, air pollution levels, and student success in programming courses. Her work in e-learning technologies demonstrates innovative approaches for adaptive course delivery using data mining techniques. She has also pioneered research at the intersection of AI and psychology, exploring concepts like 'Artificial Psychology' and 'PsAIchology'. Analysis of Dr. Blagojević's recent publications reveals a clear trajectory toward interdisciplinary applications of artificial intelligence. Her work increasingly bridges computer science with psychology, healthcare, and environmental science. A notable trend is her focus on explainable AI, ensuring complex machine learning models remain interpretable for end-users. Her research also demonstrates strong commitment to applying technology for social good, particularly in education and rural development contexts, as evidenced by projects like WINnovators Space. Scientific Awards and Recognitions Award from 'dr Milivoje Urošević' foundation for best graduating student in 2006/2007 Four awards from Technical Faculty for excellent academic results each school year Scholarship from Fund for Young Talents Scholarship from University of Kragujevac (as one of 11 best students) Scholarship from Čačak municipality Scholarship from 'Denise Hale' Foundation Award for the best second-place innovative idea from the Union of Engineers and Technicians of Serbia (February 2020) Recognition for the best female scientist with most research results at University of Kragujevac (February 2022) Dr. Blagojević has been actively involved in research supervision and grant-funded projects throughout her career. She has served as a reviewer for three scientific journals and participated in significant research initiatives including 'Development of new information and communication technologies using advanced mathematical methods' (Project III 44006) and 'Application of biomedical engineering in preclinical and clinical practice' (Project 41007). Her international collaboration includes participation in TEMPUS project 544482-TEMPUS-1-2013-1-IT-TEMPUS-JPHES and Erasmus mobility at Alexandru Ioan Cuza University of Iaşi. As a member of the Computer Science Laboratory at the Faculty of Technical Sciences, Dr. Blagojević contributes to a collaborative research environment focused on advancing information technologies. Her interdisciplinary approach connects computer science with psychology, healthcare, and environmental science, demonstrating how technology can address complex real-world challenges while enhancing educational outcomes and community development.
