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Marcos KalinowskiView profile

Associate Professor

• Software Engineering for Data Science and Artificial Intelligence
• Requirements Engineering
• Software Process and Product Quality
• +3 more

Marcos Kalinowski is an Associate Professor at the Department of Informatics, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), where he has served as a faculty member since 2017. He is the co-founder and coordinator of the ExACTa PUC-Rio laboratory, General Chair of the International Conference on Software Engineering (ICSE) 2026, and Associate Editor of the Journal of Systems and Software. His academic affiliations also include membership on the steering committees of ICSE and the Ibero-American Conference on Software Engineering (CIbSE), as well as being a Senior Advisor and Lead Appraiser for the Brazilian Software Quality Program (MPS.BR). Dr. Kalinowski earned his Ph.D. and M.Sc. in Software Engineering from COPPE/UFRJ under advisor Guilherme Travassos, and his B.Sc. in Computer Science from UFRJ. His academic career includes previous faculty positions at Fluminense Federal University (UFF) from 2014-2017 and the Federal University of Juiz de Fora (UFJF) from 2012-2014. His research focuses on bridging software engineering and data science, with particular emphasis on developing reliable and maintainable AI/ML systems through rigorous engineering practices. His work addresses critical challenges in requirements engineering for ML-enabled systems, investigating how to effectively specify quality attributes and functional requirements for complex AI applications. His research on software process and product quality emphasizes agile methods adapted to modern data-intensive development contexts. Through experimental software engineering approaches, he validates these methods through empirical studies in both academic and industrial settings. His work has significantly contributed to establishing Software Engineering for Data Science as a distinct discipline, including creating Brazil's first extension course on the subject in 2021. His publication record demonstrates a strong focus on the intersection of software engineering and artificial intelligence, with recent work addressing technical debt in ML systems, requirements engineering for AI, and lean methodologies for industry-academia collaboration. His research shows a clear trajectory toward making AI development more systematic, reliable, and aligned with software engineering best practices, evidenced by his over 160 scientific publications and multiple books including the Jabuti Acadêmico award-finalist 'Engenharia de Software para Ciência de Dados' (2023). Dr. Kalinowski has received numerous prestigious awards including: CNPq Research Productivity Grant (Level 1D, 2024-2028) FAPERJ Scientist of Rio de Janeiro State Distinction (2024) Multiple best paper awards at international conferences including SWQD 2025 and CAIN 2024 Recognition as advisor of multiple award-winning Ph.D. and M.Sc. theses SBES 2024 Advisor of the Best Brazilian Software Engineering Ph.D. Thesis As an academic leader, Dr. Kalinowski has supervised numerous doctoral and master's students, many of whom have received national awards for their research. He has secured significant research funding from Brazilian Research Council (CNPq) and Rio State Agency (FAPERJ), as well as industry collaborations with major organizations including CEPEL, Eletrobras, Galp, Petrobras, and Stone. His laboratory, ExACTa PUC-Rio, has established successful industry partnerships through its Lean R&D approach, creating a robust pipeline between research and practical application. Dr. Kalinowski leads the ExACTa PUC-Rio laboratory which focuses on applying lean R&D methodologies to industry-academia collaboration projects. He also created and coordinates PUC-Rio's specialization courses in Software Engineering and Full Stack Development through CCEC PUC-Rio, demonstrating his commitment to bridging academic research with industry needs. His leadership in bringing ICSE 2026 to Brazil represents a significant milestone for the software engineering community in Latin America.

Kai ZhangView profile

Assistant Professor

• Molecular Simulation
• Machine Learning in Materials Science
• Soft Matter Materials
• +5 more

Kai Zhang is an Assistant Professor of Chemistry at the University of Texas at Tyler. He specializes in computational chemistry, focusing on molecular simulation and machine learning applications in soft matter materials such as polymers, colloids, and glasses. His research explores nanoparticle self-assembly, gas transport through porous membranes, and data-driven materials discovery. Dr. Zhang holds a B.S. in Chemistry from Tsinghua University (2007) and a Ph.D. in Physical Chemistry from Duke University (2012). He joined UT Tyler in 2023 after postdoctoral or industry experience not explicitly detailed in the text. His research interests emphasize bridging computational methods with materials science. Key themes include understanding phase behavior in soft matter systems, optimizing gas separation membranes, and leveraging machine learning for material property prediction. Recent work highlights applications of generative models to thermodynamic systems and deep learning for nanoparticle assembly analysis. While no specific awards are listed, his publication record reflects sustained contributions to polymer physics, materials characterization, and computational methodologies. His work spans from fundamental theoretical studies (e.g., Ising model simulations) to applied problems like cell phone usage analysis via social media data. Laboratory and team affiliations are not explicitly mentioned, but his computational focus suggests involvement in interdisciplinary research groups at UT Tyler. No grants or advising details are provided in the source text.

Xiaojun QiView profile

Professor

• Image Processing
• Machine Learning
• Computer Vision
• +5 more

Dr. Xiaojun Qi is a Professor and Department Head of Computer Science at Utah State University (USU), where he has been since 2002. He holds a Ph.D. in Computer Science from Louisiana State University. His research focuses on Image Processing, Machine Learning, Computer Vision, and Deep Learning, with notable contributions to medical imaging, object detection, and multimedia security. Qi leads the Computer Science department and oversees research projects funded by grants from organizations like UDOT and GRCO. He has advised numerous graduate and undergraduate students across projects such as fake face detection, facial expression recognition, and LiDAR-based traffic sign detection. His work integrates computational geometry and AI to address challenges in vision systems and biomedical applications. Education: Ph.D., Computer Science (Louisiana State University); M.S., Systems Science (Louisiana State University); M.S., Computer Science (Shenyang Institute of Automation, Chinese Academy of Sciences); B.S., Computer Science and Engineering (Donghua University). Research Interests: Image Processing, Machine Learning, Deep Learning, Computer Vision, Medical Imaging, Object Detection, Digital Watermarking, and Data Mining. He has organized numerous conferences and served on technical committees for VISAPP, CompIMAGE, and DEXA. Grants include UDOC (Traffic Sign Detection), GRCO (Medical Image Segmentation), and NSF REU (Computer Vision). His lab focuses on interdisciplinary projects such as vision-based navigation, steganalysis, and bio-inspired algorithms.

• Multimedia Content Adaptation
• Service-Oriented Systems
• Bioenergy
• +5 more

Mohd Farhan Md Fudzee is an academic researcher with a focus on interdisciplinary computational research spanning multimedia systems, bioinformatics, and network engineering. His work emphasizes service-oriented architectures, data fusion techniques, and optimization of complex systems. Key contributions include advancements in content adaptation policies for distributed multimedia, machine learning methods for disease gene prediction, and disaster management protocols in mobile ad-hoc networks (MANET). He has collaborated extensively with institutions on projects involving fuzzy logic applications, healthcare informatics, and safety-critical system development. Research interests are driven by practical applications in: Multimedia Adaptation: Developing QoS-aware service selection frameworks and dynamic path determination policies for content delivery networks. Health Informatics: Leveraging machine learning for disease module identification and medical systems reliability assessment. Data Science: Innovating classification methods using fuzzy soft set theory and multi-agent systems for data fusion challenges. Recent work (2022-2024) highlights trends in bioinformatics pathway analysis, social network prediction algorithms, and hybrid routing approaches for disaster response systems. His publications consistently address real-world system optimization across domains like transportation, healthcare, and energy sectors.

Gerhard A. HolzapfelView profile

Professor

• Biomechanics
• Mechanobiology
• Cardiovascular System
• +3 more

Gerhard A. Holzapfel is a Full Professor and Head of the Institute of Biomechanics at Graz University of Technology (Austria) since 2007. He holds adjunct and visiting professorships at the Norwegian University of Science and Technology (NTNU, Norway) and the University of Glasgow (UK). His career includes roles at the Royal Institute of Technology (KTH, Sweden) and international visiting scholarships at Stanford University and others. He specializes in biomechanics, mechanobiology, and cardiovascular system modeling, with a focus on soft biological tissues and computational methods. Education : Ph.D. Mechanical Engineering, Graz University of Technology (1990, with distinction) Habilitation in Mechanics, Vienna University of Technology (1996) Erwin Schrödinger Scholarship at Stanford University (1993–1995) Research Interests : Experimental and computational biomechanics, mechanobiology of soft tissues, cardiovascular system mechanics, nonlinear continuum mechanics, finite element methods, medical imaging techniques (e.g., polarized light microscopy, MRI). Awards : 2025: Huiskes Medal (European Society of Biomechanics) 2025: NAE International Member 2024: Honoris Causa Doctorate (École des Mines) 2021: William Prager and Warner T. Koiter Medals 2014: Highly Cited Researcher (Thomson Reuters) Grants & Leadership : Secured grants from ERC, NIH, and the European Commission. Co-founder of Biomechanics and Modeling in Mechanobiology journal. Served as Chair of multiple academic departments and research groups. Labs/Teams : Leads the Institute of Biomechanics at TUG, focusing on interdisciplinary research in biomechanics and mechanobiology.

• Statistics
• Data Mining
• Machine Learning
• +3 more

Dr. Nicos Pavlidis is a Senior Lecturer in the Department of Management Science at Lancaster University's Management School. His research focuses on developing novel methods for clustering and classification in high-dimensional and time-varying data environments, with applications in machine learning and statistics. He teaches courses such as MSCI 562 (Intelligent Data Analysis and Visualisation) and MSCI 331 (Data Mining for Direct Marketing and Finance), supported by DataCamp platforms. His research interests include statistics, data mining, and machine learning, with a particular emphasis on adaptive methods for dynamic data streams. Pavlidis has contributed to projects like STOR-i: DSI (2017–2021), which aimed to improve clustering efficiency in high-dimensional datasets. He is affiliated with the STOR-i Centre for Doctoral Training and the Centre for Marketing Analytics & Forecasting. His publications span topics such as spectral clustering, metric learning, and ensemble methods. He has advised PhD student Danielle Notice and has reviewed for journals like IEEE Transactions on Evolutionary Computation and Applied Soft Computing. Pavlidis actively participates in conferences and workshops, including the International Symposium on Intelligent Data Analysis and the IEEE Big Data Conference.

• Geotechnical Engineering
• Soil Mechanics
• Mine Backfilling
• +4 more

Associate Professor Siva Sivakugan is a faculty member at James Cook University, specializing in Civil and Geotechnical Engineering. He holds a First Class Honours Bachelor's from the University of Peradeniya (Sri Lanka), and MSCE and PhD from Purdue University (USA). His expertise spans geotechnical engineering, mine backfilling, and soil mechanics. He has authored eight books and over 150 journal papers, focusing on topics like sedimentation, hydraulic consolidation, and geopolymer stabilization. Sivakugan currently serves as Associate Editor for the International Journal of Geotechnical Engineering and has consulted for geotechnical firms and the World Bank. He has supervised 16 PhD students and examined over 50 doctoral theses globally. Education : Bachelor's in Civil Engineering, University of Peradeniya (First Class Honours) MSCE and PhD from Purdue University, USA Research Interests : His work integrates experimental and computational methods to address challenges in geotechnical engineering, including: Sedimentation and consolidation of dredged materials Performance of cemented paste backfill in mining Electrokinetic processes for environmental remediation Rock mechanics and mine stope design Sustainable construction using recycled materials Recent Trends in Publications : Recent studies emphasize innovative applications of electrokinetic techniques, optimization of mine backfill materials, and computational modeling of geotechnical systems. His work bridges laboratory experiments with field-scale applications, particularly in climate-resilient infrastructure. Grants & Awards : While no specific awards are listed, his Fellowships with Engineers Australia and the American Society of Civil Engineers highlight professional recognition. He has secured consultancy projects with global firms and institutions. Labs & Teams : He leads research groups focused on geotechnical testing, mine waste management, and sustainable construction materials at James Cook University.

ALBERTO JOSE BUGARIN DIZView profile

Professor

• Artificial Intelligence
• Natural Language Generation
• Machine Learning
• +3 more

Alberto Jose Bugarín Díz is a Full Professor of Artificial Intelligence at the University of Santiago de Compostela (USC), affiliated with the Department of Electronics and Computing at the Higher Technical School of Engineering. He leads the Intelligent Systems Group (GSI), a competitive research group recognized by the Xunta de Galicia since 2006. His research focuses on Natural Language Generation (NLG), Explainable AI, Machine Learning, and Soft Computing, with applications in healthcare, process mining, and environmental monitoring. He has co-authored over 230 scientific papers and led 25+ R&D projects. Currently, he chairs the Degree in Artificial Intelligence across Galicia’s universities and serves on the boards of AEPIA and SEPLN. Notable roles include Deputy Director of the USC School of Engineering and organizing the 2024 ECAI conference. Education: PhD in Computer Science from USC (1994). Key projects include TELSEC4TAI (Trustworthy AI challenges) and CONFIA (trust in AI systems). His work emphasizes ethical and socio-technical aspects of AI, with contributions to reproducibility in NLP evaluations and linguistic summarization systems like the SoftLearn platform.

• Fuzzy Sets
• Possibility Theory
• Imprecise Probability
• +3 more

Didier Dubois is a CNRS Research Director (DR2) at the Institut de Recherche en Informatique de Toulouse (IRIT) within Université Paul Sabatier, Toulouse. He leads the ADRIA team (Argumentation, Decision, Reasoning, Uncertainty, Learning) in the 'Reasoning and Decision' thematic group. His work focuses on fuzzy sets, possibility theory, imprecise probability, decision theory, and uncertainty management in AI and operational research. Education: Ingénieur Civil de l'Aéronautique from ENSAE (1975) Docteur Ingénieur from ENSAE (1977) Docteur d'Etat from Université de Grenoble (1983) Habilitation à diriger des recherches from Université Paul Sabatier (1986) Research Interests: Explores mathematical models for uncertainty and vagueness, including numerical/qualitative possibility theory, risk analysis under partial ignorance, generalized possibilistic logic, qualitative decision theory, and applications in scheduling, information retrieval, and soft constraints. Key Contributions: Co-developed fuzzy set theory applications, edited influential volumes on uncertainty and decision-making, and pioneered formal frameworks for combining probability and possibility. Current research emphasizes bipolar knowledge representation and epistemic reasoning. Editing Roles: Co-Editor-in-Chief of Fuzzy Sets and Systems since 1999, and series editor for the Handbooks of Fuzzy Sets . Awards: Docteur Honoris Causa, Polytechnic School of Mons (1997) IFSA Fellow (1999) ISI Most Cited French Scientist (2001) IEEE Neural Network Society Pioneer Award (2002) Advising: Supervised numerous PhD students since 2005, including work on possibility theory, risk analysis, and decision fusion. Collaborates with institutions like IRSN, ONERA, and Airbus.

William YoungView profile

Professor

• Operations Management
• Operations Research
• Predictive Analytics
• +7 more

Dr. William A. Young II is the Charles G. O’Bleness Full Professor of Business Analytics at Ohio University’s College of Business, specializing in the Department of Analytics and Information Systems. He holds a Ph.D. (2010), M.S. (2005), and B.S. (2002) in Electrical and Systems Engineering from Ohio University. His research focuses on operations management, predictive analytics, and decision support systems, with notable work in supply chain optimization, healthcare analytics, and educational technology. Education: Ph.D., Mechanical and Systems Engineering, Ohio University (2010) M.S., Electrical Engineering, Ohio University (2005) B.S., Electrical Engineering, Ohio University (2002) Research Interests: Dr. Young’s work bridges operations research and business analytics, emphasizing practical applications in logistics, healthcare, and education. His studies often employ artificial neural networks and optimization algorithms to address real-world challenges such as inventory management, energy efficiency, and student success prediction. Recent projects include viral marketing incentive models and pandemic risk mitigation in warehouse operations. Publications: His 100+ articles span journals like *European Journal of Operational Research*, *Applied Soft Computing*, and *Expert Systems with Applications*. Key themes include multi-objective optimization, predictive modeling, and decision support systems for complex systems. Awards: 2023 Faculty Excellence Award for Teaching and Learning 2020 University Professor Award Multiple College of Business Research Awards (2017, 2018) 2015 Cincinnati-Dayton Outstanding Young OR/MS Award Grants & Collaborations: He has secured funding from organizations like the National Science Foundation and General Electric Aviation. His interdisciplinary teams address challenges in manufacturing, environmental systems, and educational technologies. Labs & Initiatives: Leads Ohio University’s analytics research efforts, collaborating with industry partners to develop innovative solutions in supply chain, healthcare, and education. His work often integrates artificial intelligence with domain-specific knowledge extraction techniques.

Slobodan SavićView profile

Professor

• Applied Mechanics
• Tribology
• Mechanical Engineering
• +4 more

Slobodan Savić is a Professor at the Department of Applied Mechanics and Automatic Control, Faculty of Engineering, University of Kragujevac, Serbia. His research focuses on applied mechanics, tribology, mechanical systems optimization, and sustainable energy solutions. He has contributed to advancements in materials science, fluid dynamics, and automotive engineering, with a particular emphasis on reducing emissions and enhancing mechanical component durability. His work spans diverse areas such as composite materials for vehicle suspension systems, tribological behavior of alloys, and optimization of hydraulic pumps. He also explores environmental applications, including biomethane utilization in transport and waste management systems. His research integrates experimental methods with computational modeling to address real-world engineering challenges. Key contributions include studies on particulate matter reduction in diesel engines, stress analysis in mechanical components, and energy-efficient technologies for industrial and domestic systems. His publications reflect a multidisciplinary approach, bridging mechanical engineering with environmental and biomedical sciences.

• Data Mining
• Rough Sets
• Granular Computing
• +4 more

Georg Peters is a Professor affiliated with the School of Arts and Humanities. His research spans data mining, machine learning, and educational technology, with a focus on rough sets, granular computing, and STEM education frameworks. He collaborates frequently with researchers like Richard Weber, Jan Seruga, and Tom Rueckert on interdisciplinary projects. His work integrates computational methods with practical applications, such as credit scoring systems, dynamic data analysis tools, and educational platforms for mathematics and programming. A recurring theme is the development of frameworks that bridge theoretical computer science with real-world educational and analytical challenges. Publications emphasize data-driven decision-making, clustering algorithms, and technology-enhanced learning. Recent articles explore probabilistic rough sets for finance, R-based educational environments, and dynamic granular clustering. No awards, students, or grants are mentioned in available sources.

YUE MEIView profile

Associate Professor

• Computational Mechanics
• Biomedical Engineering
• Advanced Intelligent Materials
• +3 more

YUE MEI is an Associate Professor at Dalian University of Technology's School of Mechanics and Aeronautics. He holds a doctoral degree in Mechanical Engineering from Texas A&M University and has postdoctoral experience at Swansea University (UK) and Saint-Etienne Mines (France). His research focuses on computational mechanics, biomedical applications, and CAE software development, particularly addressing national needs in industrial software autonomy. He leads major national projects including National Key R&D Programs and National Natural Science Foundation grants. Awards include the Liaoning Provincial Talent Aggregation Plan and Dalian High-level Talents designation. His work spans mechanical inversion methods, multi-physics finite element analysis, and structural nondestructive testing. Education: PhD in Mechanical Engineering, Texas A&M University (2013-2017) MSc in Solid Mechanics, South China University of Technology (2010-2013) BSc in Engineering Mechanics, China University of Petroleum (2006-2010) Research Interests: Integrating computational mechanics with biomedical and advanced materials fields. Key areas include: Mechanical inversion techniques for medical/industrial applications Multiphysics finite element methods (fluid-structure, electromagnetics) Development of autonomous CAE software tools Grants & Projects: Leads 7+ ongoing projects including 'Jointly Driven Cross-Scale Mechanical Analysis' (Liaoning Provincial) and 'General CAE Implicit Solver Engine' (National Key R&D). Recent papers focus on topology optimization in biomechanics, soft tissue characterization, and energy harvesting.

Francisco A. Gómez VelaView profile

Associate Professor

• Machine Learning
• Data Mining
• Bioinformatics
• +3 more

Francisco A. Gómez Vela is an Associate Professor at Universidad Pablo de Olavide, Spain. His work bridges computational techniques with biomedical and energy data analysis. Education: PhD in Computer Science (Cum Laude) from Universidad Pablo de Olavide; BSc in Computer Science from University of Seville Research interests focus on: Machine learning for genetic and biomedical data Big data and high-performance computing (HPC) in bioinformatics Energy consumption analysis in smart buildings/cities Software tools for gene network reconstruction Recent publications highlight: Ensemble approaches for gene co-expression networks Biclustering methods in biological data mining GPU-accelerated bioinformatics software Applications in cancer biomarker discovery Educational data mining for microcompetency frameworks Teaching duties include: Computer Science (BSc and MSc) Bioinformatics Project Engineering History and Digital Humanities He has participated in national research projects and R+D+I transfer initiatives, developing tools like: bioScience (HPC bioinformatics library) CyEnGNet—App (Cytoscape gene network tool) BIGO (gene enrichment analysis) GNC–app (gene network validation)

• Machine Learning
• Process Systems Engineering
• Industry 4.0
• +5 more

János Abonyi is a Professor at the Department of Process Engineering, Faculty of Engineering, University of Pannonia, Hungary. With over 118 publications spanning from 2000 to 2024, he has established himself as a leading researcher in process systems engineering, machine learning applications, and Industry 4.0 technologies. His work bridges theoretical computational intelligence with practical industrial applications, particularly in process optimization, fault detection, and smart manufacturing systems. Abonyi's research interests focus on applying machine learning and data mining techniques to industrial process systems. His work spans several key areas including soft sensor development, fault detection and diagnosis, optimization algorithms for manufacturing, and Industry 4.0/5.0 applications. He has pioneered approaches combining sequence mining with deep learning for alarm management, reinforcement learning for disassembly line optimization, and explainable AI for industrial applications. His research demonstrates a consistent trajectory from fundamental computational intelligence methods to their practical implementation in real industrial settings. His recent publications (2020-2024) show a strong emphasis on Industry 4.0 and 5.0 applications, with particular focus on human-machine collaboration, real-time locating systems, digital twins, and explainable AI for industrial applications. The interdisciplinary nature of his work is evident through collaborations across computer science, electrical engineering, and chemical engineering domains. His research group has produced significant contributions in optimization algorithms, particularly in reinforcement learning applications for manufacturing and process systems. Abonyi has supervised numerous PhD students who have become active researchers in their own right, including Tamás Ruppert, Ágnes Vathy-Fogarassy, and Balazs Feil. His collaborative network extends internationally, with publications in top journals including IEEE Access, Sensors, and Computers & Chemical Engineering. His work demonstrates strong industry relevance with practical implementations in manufacturing, process control, and industrial automation contexts.