Vittorio Solina is a Researcher at the Department of Mechanical, Energy and Management Engineering (DIMEG) of the University of Calabria, specializing in industrial systems simulation and digital twin technologies. His work spans Industry 4.0/5.0 applications, supply chain optimization, and human-robot collaboration. Affiliation: University of Calabria Academic Role: Researcher (Legge 240/10) Research Focus: Digital twins, simulation modeling, sustainable manufacturing His research emphasizes advanced simulation tools for decision support, including applications in Industry 4.0 , Smart Manufacturing , and Agri-Food Supply Chains . Recent work explores adaptive simulation optimization for Industry 5.0 and 5G-enabled robotics. Key publication trends include: (1) Digital twin integration for supply chain resilience (2024-2025), (2) Simulation automation methodologies (2025), and (3) Human-robot collaboration frameworks (2024).
Benjamin Carrison-Schafer is an Associate Professor and Assistant Dean for Graduate Student Success at the Department of Electrical and Computer Engineering, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He leads the Design Automation and Reconfigurable Computing Laboratory (DARClab), focusing on systems, high-level design, and programming methodologies for VLSI computing systems and FPGAs. Education: MBA from McGill University, Canada (2012) PhD in Electrical and Electronic Engineering from University of Birmingham, UK (2003) Research Interests: His work spans modeling, analysis, synthesis, optimization, and implementation of VLSI systems. Current projects include: Approximate Computing, High-Level Synthesis Design Space Exploration, Hardware Security, Behavioral MPSoC Optimization, and Automatic Fault-Tolerant System Generation. His research blends theory and practice, using analytical and experimental techniques to solve real-world problems. Publication Trends: Recent publications demonstrate strong focus on hardware security, FPGA optimization, and automated design methodologies. Key themes include High-Level Synthesis innovations, hardware acceleration techniques, and cross-disciplinary applications of reconfigurable computing. His work frequently addresses challenges in hardware trustworthiness, energy efficiency, and design automation scalability. Professional Activities: Associate Editor: IEEE Transactions on Sustainable Computing (2022-present) Conference Chair: ICCD 2024, DCAS 2024 Program Committee: ASP-DAC, DATE, FCCM, GLSVLSI Advising & Labs: Supervises multiple PhD and Master's students at DARClab. Current research includes domain-specific architecture design, hardware security, sustainable computing, and ML for VLSI design. The lab collaborates with industry partners like Renesas Electronics and develops commercial tools through spin-off company highX Technologies.
Sergio Bermudo Navarrete is a Professor at the Department of Economics, Quantitative Methods and Economic History, Universidad Pablo de Olavide. His research spans combinatorics, graph theory, and operator theory. Education: PhD in Mathematics (2003, University of Seville) with thesis on functional models of operators in Hilbert spaces. Research Interests: Focus on graph theory, domination problems, topological indices, and operator theory. Publication Trends: Recent work includes vertex-degree-based indices for oriented graphs, domination parameters in product graphs, and differential analysis of line graphs. Keywords span Computer Science , Mathematical Chemistry , and Operations Research . Collaborations: Frequent co-authors: José María Sigarreta Almira, José Manuel Rodríguez García, Juan Alberto Rodríguez-Velazquez. Contact: Email sbernav@upo.es .
Professor Haijiang Li is a Chair in BIM for Smart Engineering at Cardiff University's School of Engineering. His roles include leading the Computational Mechanics and Engineering AI Research Group, directing the BIM for Smart Engineering Centre, and overseeing the BIM MSc programme. He holds editorial roles for journals like Construction Innovation and Automation in Construction , and chairs the European Group of Intelligent Computing in Engineering (EG-ICE). Research focuses on smart computational engineering platforms integrating BIM, AI, and big data for sustainable infrastructure. Key areas include digital twins, disaster management, and resilient urban systems. He has secured £40M in research funding, including £9M as PI, and led over 70 research staff and students. Prof. Li is a Standards Committee Technical Executive at buildingSMART, driving international BIM standards. His work includes co-authoring a book on BIM standards across China, the US, and the UK. Awards include Fellowships from the British Computer Society (FBCS) and the Higher Education Academy (FHEA). His research outputs span over 250 publications, covering topics like AI-driven bridge maintenance, ontology-based decision-making, and energy-efficient urban systems. Collaborations with industry and global partners emphasize practical applications of BIM and smart technologies.
Jan Madsen is a Professor at DTU Compute, Technical University of Denmark, and Head of the Embedded Systems Engineering section. His research focuses on system-level modeling and design of embedded computing systems, particularly cyber-physical systems, microfluidic biochips, and synthetic biology applications. Develops design automation tools and methodologies for embedded systems Supervises numerous PhD students and leads major research projects Research Interests Key areas include: Embedded systems-on-a-chip Cyber-Physical Systems (Internet-of-Things) Microfluidic Lab-on-Chip devices Synthetic biology with molecular computing Design, modeling, and optimization of complex systems Scientific Awards DATE Fellow (2019) IEEE CEDA Outstanding Recognition (2019) DTU Scientific Advise Award (2013) Best Paper Awards at MECO (2013) and CASES (2009) Jorck’s Foundation Research Award (1995) Publications His 14+ journal papers and 115+ conference papers demonstrate expertise in: SystemC-based modeling frameworks Energy-aware sensor networks Self-healing eDNA architectures Microfluidic biochip synthesis RTOS modeling and MPSoC exploration
Noorbakhsh Amiri Golilarz is an Assistant Professor in the Department of Computer Science at The University of Alabama, College of Engineering. He has established himself as a prominent researcher in artificial intelligence, particularly in computer vision, deep learning, and image processing. His educational background includes: Postdoctoral Research Fellow, Computer Science, Boston College (2023) Ph.D., Electrical and Computer Engineering, Southern Illinois University Carbondale (2023) D. Eng., Computer Science and Technology, University of Electronic Science and Technology of China (2021) M.S., Electrical and Electronic Engineering, Eastern Mediterranean University (2017) B.S., Electrical Engineering, University of Guilan (2012) Dr. Golilarz's research spans multiple domains of artificial intelligence with a particular focus on computer vision, deep learning, and image processing applications. His work addresses challenges in medical imaging, satellite imagery, and cognitive neuroscience. He has made significant contributions to image denoising techniques, control chart pattern recognition, and AI applications in healthcare. His recent work has expanded into generative AI, large language models, and secure machine learning operations. His publication portfolio demonstrates consistent productivity with over 2500 citations and an h-index of 25. His most impactful work includes applications of blockchain and federated learning for COVID-19 detection, optimized support vector machines for medical diagnosis, and innovative image denoising techniques using metaheuristic optimization algorithms. Among his professional achievements: Co-founded AI Letters journal in 2024, serving as Associate Editor-in-Chief Served as Lead Guest Editor and Topic Editor for several SCI-indexed journals Held the role of Conference Program Chair Dr. Golilarz has supervised numerous graduate students and research projects, with his work spanning theoretical advancements in AI algorithms to practical applications in healthcare, energy systems, and cybersecurity. His research group has established collaborations with institutions including Boston College and Mississippi State University.
Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.
Francesco Pilati is an Associate Professor at the Department of Industrial Engineering, University of Trento, where he serves as local coordinator for the scientific field ING-IND/17 (Industrial Plants and Logistic Systems). He chairs the research group on Industrial Plants, Production Systems, and Logistics, and teaches courses in Industrial Plants and Design of Digital Production and Assembly Systems. As coordinator of the Master's program in Management and Industrial Systems Engineering and University Coordinator for the EIT double degree in Zero-Defect Manufacture, Pilati bridges academic leadership with advanced manufacturing research. He has also served as Invited Lecturer at universities in Vienna and Göttingen. His research focuses on integrating environmental sustainability with technical-economic criteria through multi-objective optimization and impact assessment. Key areas include: Distribution networks and warehousing systems Manufacturing and assembly line design Hybrid energy production systems Digitization of manual production processes using depth cameras Recent publications highlight applications of Industry 4.0 technologies to pandemic safety, logistics optimization, and smart manufacturing. Pilati has received significant recognition including the Philip Morris Italia Empowering Research Award (2016) and Autostrade per l'Italia academic recognition. His editorial contributions include guest editing special issues on Digital Twins and Smart Factories in Q1 journals.
Giuseppe Carlo Marano is a Full Professor at the Department of Structural, Building and Geotechnical Engineering at Politecnico di Torino. He is also a component of the SISCON Interdepartmental Center for Infrastructure Safety. With expertise in civil and structural engineering, his work focuses on machine learning applications, seismic risk reduction, and sustainable structural optimization. Education Graduated cum laude in Structural Engineering from Polytechnic University of Bari PhD in Structural Engineering from University of Florence (2000) Research Interests Marano's research spans structural optimization, seismic engineering, and machine learning applications in civil infrastructure. He develops advanced computational models for: Seismic retrofitting of existing structures Optimization of steel and masonry structures Recycled materials in concrete production AI-driven structural health monitoring Multiobjective design methodologies Publication Trends His recent work emphasizes: Machine learning for concrete mix design and damage assessment Optimization of gridshells and arch structures Seismic isolation systems and vibration control Sustainable construction practices with recycled materials Multiobjective genetic algorithms for structural design Scientific Recognitions National Scientific Qualification - First Band (2013, MIUR Italy) Certificate of Appreciation for Outstanding Lecture (2012, China) Academic Contributions As an educator, he teaches: Consolidamento Strutturale (Structural Consolidation) Dinamica delle Vibrazioni Random (Random Vibration Dynamics) Progettazione Generativa (Generative Design) He also leads Challenge@PoliTo initiatives and contributes to national infrastructure safety regulations. Research Projects ADAPT4CE - Adaptive Digital Systems for Circular Economy (2025-2028) AI-ENVISERS - AI for Seismic Retrofit Environmental Impact (2023-2025) ADDOPTML - Additive Manufacturing Optimization (2021-2025)
Salim ROSTAMI is an Associate Professor at the IÉSEG School of Management in France, specializing in Operations Management. He holds a Ph.D. in Economics and Mathematics Sciences from KU Leuven (2019) and a Master’s in Engineering from KU Leuven (2013), alongside a Bachelor’s in Industrial Engineering from Ferdowsi University of Mashhad (2012). His research focuses on scheduling under uncertainty, project planning, combinatorial optimization, and healthcare logistics. Notable achievements include the 2016 2nd Best Conference Paper Award from the University of Valencia. Education: Ph.D., Economics and Mathematics Sciences, Operations Management, KU Leuven, Belgium (2019) Master, Engineering, Operations Research, KU Leuven, Belgium (2013) Bachelor, Engineering, Industrial Engineering, Ferdowsi University of Mashhad, Iran (2012) His work spans stochastic resource-constrained project scheduling, sequential testing of systems, and chemotherapy appointment scheduling. He has published widely in journals like the European Journal of Operational Research and Flexible Services and Manufacturing Journal. Teaching roles include courses on operations management and project management across undergraduate and graduate programs. Awards: 2016: 2nd Best Conference Paper Award, University of Valencia His research emphasizes practical applications in healthcare and project management, leveraging dynamic programming and metaheuristic algorithms. Collaborations include work with institutions like École des Mines de Saint-Étienne and KU Leuven.
Zaklina Spalevic is a distinguished Professor at Singidunum University, specializing in the intersection of law, technology, and business. With a strong academic background in criminal law and extensive research in cyber law, artificial intelligence applications in legal systems, and tourism law, she has established herself as a leading scholar in digital legal frameworks. Her work spans multiple disciplines, connecting traditional legal principles with emerging technological challenges. Dr. Spalevic earned her doctoral degree in Criminal Law from the University of the Academy of Economics in Novi Sad (2008-2012), following basic studies in General Law at the University of Pristina (1996-2001). Her educational foundation in Science and Mathematics from First Pristina High School (1992-1996) provided the analytical basis for her interdisciplinary approach to legal scholarship. Her research interests focus on the evolving landscape of cyber law, particularly the legal implications of artificial intelligence in judicial systems, electronic governance, and tourism industry regulation. Dr. Spalevic has pioneered work on digital evidence processing, algorithmic justice, and the integration of green law principles into sustainable business practices. Her publications reveal a consistent pattern of addressing emerging legal challenges in the digital age, with increasing emphasis on AI applications across various legal domains. Analysis of her recent publications shows a clear trend toward interdisciplinary research that bridges law, technology, and business. Her work increasingly focuses on practical applications of legal frameworks for emerging technologies, particularly in tourism, agriculture, and judicial systems. The integration of technical solutions with legal regulation represents a distinctive feature of her scholarly contributions. Dr. Spalevic has authored numerous publications including three books: 'Fundamentals of Law with Special Reference to Business and Tourism Law' (2021, 2024 editions) and 'Legal Aspects of Cyberspace' (2018). Her extensive publication record includes over 100 journal articles and conference papers spanning cyber security, electronic governance, intellectual property in digital environments, and legal frameworks for emerging technologies. She actively contributes to academic discourse through conference presentations and collaborative research projects, particularly focusing on the legal implications of artificial intelligence across various sectors. Her work demonstrates a commitment to developing practical legal frameworks that address real-world technological challenges while maintaining foundational legal principles.
Sridhar R. Tayur is the Ford Distinguished Research Chair and University Professor of Operations Management at Carnegie Mellon University’s Tepper School of Business. He holds a Ph.D. in Operations Research from Cornell University and a B.Tech. in Mechanical Engineering from IIT Madras. His research focuses on quantum computing applications in operations research, healthcare systems optimization, and supply chain management. He has held visiting roles at MIT, Stanford, and Cornell, and founded companies like SmartOps and OrganJet. His recent work spans quantum-inspired optimization algorithms, healthcare decision support systems, and fair resource allocation policies. He has contributed to over 110 publications, including high-impact papers in Management Science , Operations Research , and IEEE Transactions . Awards include INFORMS Fellow and NAE membership. He teaches courses in quantum integer programming, healthcare operations, and service management at the Tepper School. Education: Ph.D. (Cornell), B.Tech. (IIT Madras) Research Labs: Quantum Technology Group, OrganJet Key Awards: INFORMS Fellow, NAE Member, MSOM Distinguished Fellow Teaching: MBA Operations Management, PhD Quantum Optimization, Healthcare Systems His interdisciplinary work bridges quantum computing, healthcare policy, and logistics, supported by collaborations with industry and government institutions.
Prof. Liam Murphy is a Full Professor of Computer Science & Informatics at University College Dublin (UCD) and Director of the Performance Engineering Laboratory. He holds a B.E. from UCD, M.Sc. and Ph.D. from UC Berkeley. His research focuses on performance engineering of networks, software systems, and multimedia transmissions. He has published over 150 peer-reviewed papers and is an IEEE member and Fellow of the Irish Computer Society. Education: B.E. in Electrical Engineering, UCD (1985) M.Sc. & Ph.D. in Electrical Engineering & Computer Sciences, UC Berkeley (1988, 1992) Research Interests: Dynamic resource allocation in networks Cloud computing efficiency Software performance engineering Wireless multimedia systems Quality of Service (QoS) optimization Recent work emphasizes energy-efficient cloud workflows, multi-objective data center optimization, and decentralized traffic simulation. Grants & Awards: Fellow of the Irish Computer Society (2007) Conference Paper Awards (2004, 2002, 2001) Principal Investigator in multiple funded projects (e.g., EU-funded traffic simulation, cloud resource allocation) Advising & Labs: Directed 24 Ph.D. and 8 M.Sc. students. Leads the Performance Engineering Laboratory (PEL), focusing on distributed systems, cloud efficiency, and network performance. Collaborates on industry-relevant projects like crovan (UCD/DCU campus company). Teaching: Coordinates courses on computer science fundamentals, distributed systems performance, and software engineering at UCD.
Horacio Rostro González is an Associate Professor in the Department of Industrial Engineering at IQS School of Engineering, Universitat Ramon Llull (URL), Barcelona, Spain. He is an active researcher with a strong international academic background and current affiliations in both research and teaching. Education: PhD in Control and Signal and Image Processing (2011, INRIA & University of Nice – Sophia Antipolis, France) Master’s in Electrical Engineering (2007, University of Guanajuato, Mexico) Electronic Engineer (2003, National Technological Institute of Mexico) His research interests lie at the intersection of Artificial Intelligence, Computational Neuroscience, Neuromorphic Computing, Embedded Systems, and Robotics. He applies advanced techniques in neural networks, machine learning, and control systems to solve complex engineering problems in robotics, biomedicine, and industrial design. His work emphasizes biomimetic approaches, such as spiking neural networks for robot locomotion and AI-driven analysis of physiological signals like ECG and facial expressions. The recent publications highlight a strong trend in interdisciplinary research, combining AI with photonics, robotics, cardiovascular diagnostics, and emotional recognition in children. His work spans from theoretical algorithm development to practical industrial applications, particularly in additive manufacturing and smart systems. Scientific Projects: Offshore Wind Farms: Data analysis using machine learning and power generation prediction (2023–2024) GEPI: Grup Enginyeria de Productes Industrials (2022–2025) He is actively involved in research grants and collaborative projects, with no mention of formal student advising in the provided text. He is a member of the GEPI (Industrial Products Engineering Group), which focuses on additive manufacturing, reverse engineering, and material characterization. His scientific output is robust, with consistent publication activity from 2005 to 2025, including numerous articles in indexed journals.
Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.
