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Enrique Herrera ViedmaView profile

Professor

• Digital libraries
• Intelligent decision making systems
• Computational intelligence
• +8 more

Enrique Herrera-Viedma is a Full Professor in Computer Science and AI at the University of Granada (UGR) since 2007. He currently serves as Vice-President for Research and Knowledge Transfer at UGR (2015-present) and held leadership roles in IEEE System, Man & Cybernetics Society, including Vice-President for Publications (2019-2020) and Vice-President for Cybernetics (2021-2023). His research spans computational intelligence, fuzzy decision-making systems, and consensus models. IEEE Fellow (2021) IFSA Fellow (2021) Highly Cited Researcher (Computer Science, Engineering) by Clarivate Analytics (2014-2020) Andrew P. Sage Best Transactions Paper Award (2016) 2011 IEEE Transactions on Fuzzy Systems Outstanding Paper Award His publications focus on consensus reaching processes , computing with words , linguistic modeling , and social network analysis . His work integrates fuzzy systems , optimization models , and trust mechanisms in group decision-making contexts. Articles highlight distributed linguistic trust , non-cooperative behavior management , and feedback mechanisms to enhance decision-making efficiency. He contributes to prestigious institutions like the Royal Institute Elcano (Spain) and Ulster University's Artificial Intelligence Research Center. His h-index (85 in WoS, 101 in Google Scholar) reflects his significant impact, with over 560 peer-reviewed papers and 50 highly cited works.

Sean MacAvaneyView profile

Lecturer

• Machine Learning
• Information Retrieval
• Neural Networks
• +5 more

Dr. Sean MacAvaney is a Lecturer in Machine Learning at the School of Computing Science, University of Glasgow. His research focuses on advancing information retrieval techniques, particularly in neural models, sparse/dense retrieval architectures, and system optimization. He actively contributes to open-source tools like PyTerrier and ir_datasets, emphasizing reproducibility in research. Education: Not explicitly detailed in the text, but his academic contributions imply advanced qualifications in computer science or related fields. Research Interests: MacAvaney's work spans neural information retrieval, adversarial model analysis, query expansion strategies, and large-scale system efficiency. He explores topics like instruction-following models, multilingual benchmarking, and the integration of machine learning with traditional retrieval methods. Article Trends: Recent work emphasizes practical system optimizations (e.g., in-memory indexes), cross-lingual evaluation frameworks, and bridging gaps between human and machine relevance judgments. He also investigates challenges in productionizing neural models and maintaining test collection relevance over time. Advising & Grants: Supervises students including Andreas Chari (deep learning for language tools) and Andrew Parry (uncertainty modeling in neural networks). His grants likely support projects in scalable retrieval systems and reproducible research practices. Labs/Teams: Engaged with the Glasgow Information Retrieval (GIR) group and collaborates internationally on initiatives like the Information Retrieval Experiment Platform (IREP).

Salvatore ORLANDOView profile

Professor

• Computer Science
• Machine Learning
• Data Mining
• +4 more

Salvatore Orlando is a Full Professor and Director of the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. He holds academic roles including membership in the University Scientific Instrumentation Service Center (CSA) Management Committee and the Academic Senate. His research focuses on machine learning, information retrieval, and data mining, with notable contributions to learning-to-rank algorithms, decision tree ensembles, and adversarial machine learning. Key areas include efficient algorithms for large-scale systems, model interpretability, and fairness in AI. Orlando’s work bridges theoretical advancements with practical applications in database technology and cybersecurity. His recent publications emphasize optimizing ranking models, enhancing algorithm resilience, and addressing ethical considerations in AI systems. He is actively involved in organizing international conferences and serves on program committees, further contributing to the academic community.

• Network Security
• Cloud Computing
• Internet of Things
• +3 more

Olivier Festor is a Researcher at INRIA (French National Institute for Research in Digital Science and Technology), specializing in network security, cloud computing, and IoT. His work focuses on developing scalable solutions for modern network challenges, including in-network computation, cloud service security, and anomaly detection. Research Interests: Dr. Festor investigates vulnerabilities in distributed systems, designs protocols for efficient data processing (e.g., stateful in-network computation), and pioneers frameworks for IoT threat emulation. His recent work emphasizes cloud gaming optimization, automated security for service migrations, and darknet-based threat intelligence. Publication Trends: Over 200 publications (1993–2024) reflect a shift toward cloud/IoT security and programmable networks. Recent articles prioritize machine learning for traffic classification, TOSCA-based cloud orchestration, and P4-enabled data planes, highlighting applied research with industry relevance.

• Digital Rock Physics
• Computational Pore-Scale Physics
• Tomographic Imaging
• +9 more

Professor Christoph Arns is a full Professor at the University of New South Wales (UNSW) School of Engineering, specializing in Mineral and Energy Resources Engineering. He has been at UNSW since 2008 and has held the position of full Professor since 2014. Currently, he leads the Geoenergy and Geostorage discipline within the school. Prior to joining UNSW, he was a research fellow at the Australian National University from 2001-2008. Professor Arns obtained his Dipl. Phys. from RWTH Aachen, Germany in 1996 and his PhD in Petroleum Engineering from UNSW Sydney in 2002. His academic background combines physics and petroleum engineering, creating a unique interdisciplinary perspective for his research. His primary research focus is on Digital Rock Physics, where he has pioneered computational pore-scale physics based on tomographic images. He specializes in integrating 3D tomographic imaging technology with NMR techniques for petrophysical applications, with particular emphasis on heterogeneity analysis. His work spans multiple sub-disciplines including pore-scale modeling, digital core analysis, rock physics, reservoir characterization, and Minkowski functionals for structural analysis. Professor Arns has developed the MPI-parallel software package 'morphy' for computational physics operating on segmented tomographic images, which includes sophisticated NMR response modeling, electrical property calculations, permeability estimation, elastic moduli determination, and morphological property analysis. His research has substantial industry relevance, as evidenced by his role as a founding member of Digital Core Pty Ltd, an ANU/UNSW spin-off that was sold to FEI for $76 million in 2014 and is now part of ThermoFisher. He has received significant research support through three successive Australian Research Council (ARC) fellowships. His scholarly contributions are reflected in numerous publications spanning from 2000 to 2025, with recent work focusing on advanced computational methods, machine learning applications in rock physics, and detailed analysis of fluid-rock interactions at the pore scale. Professor Arns maintains active leadership roles in multiple professional societies including Interpore (lifetime member, former council chair 2016-2019), Society of Core Analysts (Australasia Regional Director since 2016), Society of Petrophysicists & Well Log Analysts, Society of Exploration Geophysicists, and Society of Petroleum Engineers.

Cédric HERZETView profile

Research Fellow

• Statistical Learning Theory
• Optimization
• Operational Research
• +3 more

Cédric HERZET is a Permanent Member of CREST at INRIA Rennes, focusing on statistical learning theory, optimization, and inverse problems. His work bridges theoretical analysis with practical algorithm development. Primary affiliation: INRIA Rennes (French National Institute for Research in Digital Science) Research Interests: Statistical Learning Theory, Optimization algorithms, Operational Research, Inverse Problems. His work particularly addresses sparse approximation, compressed sensing, and iterative thresholding methods. Key Contributions: Development of Bayesian pursuit algorithms, geometric analysis of subspace clustering with outliers, and analysis of state evolution in dense graph message passing. His theoretical work has direct applications in signal/image processing and machine learning. Software: Created MATLAB implementations of sparse approximation algorithms and Bernoulli-Gaussian Lab toolbox for Bayesian pursuit simulation.

• Phylogenetics
• Mathematical Modeling of Infectious Diseases
• Epidemiology
• +4 more

Mark Poulsen Khurana is a Medical Doctor and Epidemiologist currently working as a Research Fellow at the University of Copenhagen's Faculty of Health and Medical Sciences. He is affiliated with the Department of Public Health, specifically within the Section for Health Data Science and AI. His work bridges clinical medicine, epidemiology, and computational approaches to understanding infectious disease dynamics. Dr. Khurana holds an MD and an MSc in Epidemiology, with ongoing doctoral studies focused on phylogenetics and mathematical modeling of infectious diseases. His educational background combines clinical training with advanced quantitative skills in epidemiology. His research spans several interconnected domains with a strong emphasis on phylogenetics and the mathematical modeling of infectious diseases . He has made significant contributions to understanding SARS-CoV-2 transmission through genomic epidemiology, developing AI approaches for disease modeling, and examining broader questions about medical progress and innovation. His work in digital health explores how emerging technologies can transform medical education and healthcare delivery. Dr. Khurana's research demonstrates a unique integration of clinical insight, epidemiological methods, and computational approaches. An analysis of his recent publications reveals a strong trajectory connecting computational methods with practical public health applications. His work on AI for infectious disease modeling, phylogenetic tree representations, and large-scale genomic epidemiology of SARS-CoV-2 demonstrates technical sophistication combined with real-world relevance. He has also published thought-provoking analyses on research trajectories in medical science and the implementation of solutions for antimicrobial resistance. Dr. Khurana actively collaborates with researchers across multiple institutions and countries, as evidenced by his extensive co-authorship network. His work has been recognized through citations and engagement on academic platforms, with several publications gaining attention in policy discussions and academic discourse. His leadership in the analysis of Denmark's extensive SARS-CoV-2 genomic dataset (covering approximately 290,000 genomes) demonstrates his capacity to manage and interpret large-scale public health data. This work has contributed significantly to understanding viral evolution and transmission patterns during the pandemic.

• Geotechnical Engineering
• Site Characterization & Monitoring
• Novel Sensing Technology
• +6 more

Pradeep Kurup is a Distinguished University Professor in the Department of Civil and Environmental Engineering at the Francis College of Engineering, University of Massachusetts Lowell. With over 25 years of academic experience, he has established himself as a leading researcher in geotechnical engineering with particular expertise in site characterization, novel sensing technologies, and computational modeling approaches for geoenvironmental applications. Education: Ph.D. in Civil and Environmental Engineering (1993), Louisiana State University M.Tech. in Civil Engineering (1987), Indian Institute of Technology - Madras, India B.Tech. in Civil Engineering (1985), University of Kerala, India Professor Kurup's research spans multiple domains within geotechnical engineering, with particular emphasis on multi-sensor data fusion, site characterization & monitoring, application of novel sensing technology to geotechnical & geo-environmental engineering. His work integrates advanced experimental techniques with computational approaches including finite element modeling and artificial neural networks. His research has evolved from traditional geotechnical methods toward innovative sensing technologies for environmental monitoring and hazard detection. Analysis of his recent publications reveals a strong trend toward interdisciplinary research combining geotechnical engineering with chemical sensing, machine learning, and nanotechnology. His work increasingly focuses on developing electronic nose and tongue technologies for environmental monitoring, explosives detection, and heavy metal characterization, while maintaining strong connections to traditional geotechnical applications. Selected Awards: University Professor (2014) - University of Massachusetts Lowell Diplomat Geotechnical Engineering (D.GE) (2012) - Academy of Geo-Professionals CAREER Award (1999) - National Science Foundation Departmental Teaching Award (2002) - UMass Lowell Member - Chi Epsilon Civil Engineering Honor Society Professor Kurup has secured substantial research funding from the National Science Foundation, Federal Highway Administration, Environmental Protection Agency, and other agencies, totaling millions of dollars over his career. His grant portfolio includes multiple NSF CAREER awards and REU (Research Experience for Undergraduates) supplements, demonstrating his commitment to both research and education. He has mentored numerous undergraduate and graduate students through these research programs, fostering the next generation of engineers. His laboratory work focuses on developing innovative sensing technologies including the Electronic Nose Cone Penetrometer (ENCPT) for geoenvironmental site characterization, modular integrated cone penetrometers, and portable air samplers for detecting toxic chemicals and explosives. His research group collaborates extensively with industry partners and international institutions to translate laboratory innovations into field applications.

Thomas LeducView profile

Associate Professor

• Urban Climate Modeling
• Geospatial Analysis
• Urban Thermal Comfort
• +7 more

Thomas Leduc is an Associate Professor at Nantes Université, affiliated with the École Nationale Supérieure d'Architecture de Nantes and the UMR AAU CNRS 1563 research unit. He has been a key figure in urban climate and geospatial research, serving as Director of the CRENAU research team (2015-2019) and Deputy Director of UMR AAU (2014-2019). Currently, he remains an active researcher and member of the Council of UMR CNRS 1563 through 2025, while also contributing to the Scientific Council of École de Design Nantes Atlantique and serving on the Doctoral School Council SIS. Professor Leduc's research focuses on the intersection of urban climate science and geospatial analysis, with particular emphasis on urban thermal comfort, pedestrian mobility, and visibility analysis in urban environments. His work combines advanced GIS methodologies with practical urban planning applications, developing innovative approaches to measuring and modeling urban microclimates. He has made significant contributions to understanding how urban form affects thermal comfort and has developed specialized techniques for analyzing urban visibility using isovists and wavelet transforms. His recent publications (2022-2025) demonstrate a strong research trajectory in urban climate modeling, with a focus on practical applications for climate adaptation. The publications reveal a consistent pattern of interdisciplinary collaboration, particularly with researchers specializing in environmental science, urban design, and computer vision. His work increasingly integrates mobile measurement techniques with geospatial frameworks to provide more nuanced understandings of urban microclimates at pedestrian level. Active participant in multiple research projects including Coolscapes, ResilientGAIA, and Lunne Coordinator of the Urbaclim research action of the GDR MAGIS Organizer of significant scientific events including SCAN'18 and Vu-pas-vu-2017 Co-supervisor of numerous doctoral students working at the intersection of urban climate and geospatial analysis Professor Leduc's research group maintains strong connections with both academic and professional communities, regularly collaborating with urban planners, architects, and environmental scientists to translate research findings into practical urban design strategies. His work on urban cooling strategies and thermal comfort has direct relevance to cities facing increasing heat stress due to climate change.

Evgeny OsipovView profile

Professor

• Artificial Intelligence
• Vector Symbolic Architectures
• Hyperdimensional Computing
• +3 more

Professor Evgeny Osipov is a full professor in Dependable Communication and Computation Systems at Luleå University of Technology, Department of Computer Science within the Department of Systems and Space Engineering. His research focuses on Communication and computing systems, with particular expertise in Artificial Intelligence frameworks. His educational background includes: PhD in Computer Science (Cum Laude) from University of Basel, Switzerland (2005) Licentiate of Technology in Telecommunications from KTH Royal Institute of Technology, Sweden (2003) Pre-doctoral school in Communication Systems from EPFL, Switzerland (1999) Engineer degree with Honors from Krasnoyarsk State Technical University, Russia (1998) Professor Osipov's research interests center around Vector Symbolic Architectures (also known as hyperdimensional computing), which serves as a bridge between symbolic and connectionist AI approaches. His work explores how mathematical properties of random hyperdimensional spaces can be leveraged for AI functionality, with potential applications in creating artificial general intelligence. His research is particularly relevant for low-resource machine learning tasks, such as those encountered in wearable Internet of Things devices. His recent publications (2024-2025) demonstrate a strong focus on improving classification performance using hyperdimensional computing techniques. He has explored confidence-driven training of centroids, implementations for spiking neural networks, and margin-based training approaches across numerous datasets to validate these techniques. Professor Osipov has received research funding from several notable organizations: Swedish Foundation for Strategic Research (grants UKR22-0024, UKR24-0014) Swedish Research Council (grants GU 2022/1963, 2022-04657) Luleå University of Technology Flemish Government Scholars at Risk (SAR) His active publication record across multiple high-impact journals indicates ongoing research activity and collaboration. His work on Vector Symbolic Architectures represents a significant contribution to the field of efficient AI computation, particularly for resource-constrained environments where traditional deep learning approaches would be impractical.

Andreas WenzelView profile

Professor

• Embedded Systems
• Diagnostic Systems
• Neural Networks
• +6 more

Prof. Dr.-Ing. Andreas Wenzel is a Professor at Schmalkalden University of Applied Sciences in the Faculty of Electrical Engineering, specializing in Embedded Systems. His office is located in Building M, Room 0401, and he can be reached at +49 3683 688 5113. With a distinguished research career spanning over two decades, Prof. Wenzel has established himself as a leading expert in embedded diagnostic systems with applications across multiple domains including biomedical engineering, industrial automation, and assistive technologies. Prof. Wenzel's primary research interests focus on the development and application of embedded diagnostic systems, with particular emphasis on neural networks for pattern recognition, fuzzy logic systems for classification problems, and real-time monitoring solutions. His work bridges theoretical machine learning approaches with practical engineering applications, resulting in innovative solutions for quality control in manufacturing processes, medical diagnostics, and mobility assistance technologies. He has made significant contributions to EEG data analysis for sleep stage and anesthesia depth monitoring, as well as developing smart systems for injection molding quality assessment and advanced mobility solutions for elderly individuals. Analysis of Prof. Wenzel's publication record reveals a consistent research trajectory focused on applying computational intelligence to solve practical engineering problems. His work demonstrates exceptional versatility across domains while maintaining a cohesive research theme centered on embedded diagnostic systems. From 2012-2016, his research output shows increasing focus on real-time embedded solutions with industrial applications, particularly in manufacturing quality control and assistive technologies, while continuing his foundational work in biomedical signal processing. The interdisciplinary nature of his research is evident in collaborations with medical professionals, industrial partners, and robotics specialists. Prof. Wenzel leads the Embedded Diagnostic Systems Research Group at Schmalkalden University of Applied Sciences, which maintains strong industry partnerships, particularly in plastics manufacturing and medical technology sectors. His team develops practical embedded solutions that address real-world challenges in production quality monitoring, medical diagnostics, and mobility assistance. The research group's work is characterized by its practical orientation, with many projects resulting in deployable systems rather than purely theoretical contributions. Prof. Wenzel's strategic focus on applied research ensures that his work has direct industrial relevance and practical impact.

• Infectious Disease Epidemiology
• Mathematical Modeling
• Vaccination Strategies
• +7 more

Professor Matt Keeling holds a joint appointment in Mathematics and Life Sciences at the University of Warwick, where he serves as Director of the Zeeman Institute (SBIDER) and Co-Director of the JUNIPER partnership. His research integrates mathematical and computational methods to model infectious disease dynamics, ecology, and evolution, with a focus on policy-relevant predictions. Key Research Areas: Epidemiology of infectious diseases (e.g., COVID-19, measles, HAT), mathematical modeling of transmission networks, climate-disease interactions, and vaccination cost-benefit analysis. Policy Impact: Active member of SPI-M and NERVTAG; former JCVI member. His models informed UK pandemic responses, including reproductive number estimation, circuit breaker policies, and vaccine deployment strategies. Selected Awards: Order of the British Empire (OBE) for pandemic advisory work (2021) Royal Zoological Society Scientific Medal (2007) Philip Leverhulme Prize in Mathematics (2005) Recent Publications address Mpox forecasting, antimicrobial AI stewardship, SARS-CoV-2 dose intervals, and climate-plague linkages. His work emphasizes aligning models with real-world data to optimize public health decisions.

• Visual Arts
• Graphic Design
• Visual Communication
• +5 more

Ivailo Saraliyski serves as an Assistant Professor in the Department of Arts and Design at New Bulgarian University (NBU), teaching core courses in Visual Media, Advertising and Vector Design (Adobe Illustrator), and Computer Layout Design of Printed Matter (InDesign). Concurrently, he operates as a creative director at CreateX digital agency, merging academic rigor with professional design practice. His academic credentials include: Bachelor's degree in Graphics from the National Academy of Arts (2016) Master's degree in Poster and Visual Communication from the National Academy of Arts (2018) Doctoral degree in Visual Arts from New Bulgarian University (2023) Saraliyski's research bridges historical art analysis and digital frontiers, with primary focus on Tarot symbolism's evolution from Renaissance to contemporary contexts. His work examines how Tarot iconography transforms across cultural movements while maintaining relevance in modern visual communication. Secondary research trajectories explore NFT ecosystems, generative art algorithms, and AI's disruptive impact on artistic originality, positioning him at the intersection of tradition and technological innovation in visual culture. His publication pattern (2022-2025) reveals a clear scholarly trajectory: early works establish foundational Tarot scholarship (2022-2023), while recent outputs pivot decisively toward digital disruption (2024-2025). This evolution demonstrates increasing engagement with blockchain art markets and machine learning's creative implications, reflecting broader shifts in art historical methodology toward technology-integrated analysis. Saraliyski maintains active artistic practice through exhibitions like "LET THERE BE LIGHT" (2020) and "LET THERE BE COLOR" (2021), while his professional role at CreateX facilitates real-world application of academic research. His office hours operate across physical (NBU II Building) and virtual (Google Meet) spaces, emphasizing accessibility for students navigating both traditional and digital art domains.

• Statistical Modeling
• Machine Learning
• Regression Analysis
• +3 more

Wang You-Gan is an accomplished academic researcher with over three decades of publication history spanning from 1991 to 2024. His work demonstrates a strong foundation in statistical methodology with applications across multiple domains including environmental science, fisheries management, machine learning, and computational statistics. His research has been published in high-impact journals across statistics, computer science, environmental science, and biology. Wang You-Gan's primary research interests include statistical modeling, regression analysis (particularly support vector regression), longitudinal data analysis, machine learning applications, and environmental statistics. His work shows a clear evolution from early fisheries and environmental applications to broader computational statistical methods with applications in energy systems, genomics, and cloud computing. His research demonstrates strong methodological development coupled with practical applications. His recent publications (2022-2024) show a strong focus on advanced regression techniques, particularly support vector regression with innovations in handling heterogeneous variances, autoregressive processes, and automatic hyperparameter selection. He has also expanded into machine learning applications for energy demand forecasting, air quality prediction, and data center optimization. His work often bridges theoretical statistical development with practical implementation across diverse domains. Wang You-Gan has received significant citation impact for his methodological contributions, with several papers accumulating over 100 citations. His collaborations span multiple institutions and disciplines, indicating his work's broad relevance across fields. His research has important applications in environmental monitoring, energy systems management, computational biology, and cloud computing infrastructure. The consistent publication record over more than 30 years demonstrates his sustained contribution to statistical methodology and its applications.