جست‌وجوی استاد و پژوهشگر

• Neuroimaging
• Neuroscience
• Cognitive Science
• +۳ مورد دیگر

Jorge Jovicich is a Full Professor in the Department of Psychology and Cognitive Science at the University of Trento, Italy. He holds concurrent roles at the Center for Mind/Brain Sciences (CIMeC) and the Centre for Medical Sciences (CISMED). With expertise in functional neuroimaging and MRI technology, he leads the MRI lab and Methods Group at CIMeC since 2006. His academic career spans postdoctoral research at institutions like MIT, Caltech, and Harvard Medical School, alongside roles as a lecturer in neuroscience and medical imaging. Education includes a PhD in Physics from the Max-Planck Institute and University of Leipzig, an MSc in Medical Imaging from the University of Aberdeen, and a Licenciatura en Física from the Universidad Nacional de Córdoba. His research focuses on brain connectivity, neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s), and advancing MRI techniques for clinical applications. He has contributed to projects like the ISMRM reproducibility challenge and developed tools such as ReStNeuMap for functional MRI network analysis. Teaching spans global institutions including Harvard, MIT, and the Instituto Balseiro in Argentina. His work emphasizes translational neuroimaging, bridging basic science and clinical practice. Key interests include biomarker development for neurodegenerative diseases and optimizing neuroimaging protocols for accuracy and reproducibility. Recent research trends include exploring temporal resolution in fMRI, thalamic nuclei segmentation, and microstructural pathways in cognitive impairment. Collaborative efforts aim to standardize neuroimaging practices and improve diagnostic accuracy through advanced MRI methods.

Emmanuel VAZQUEZمشاهده پروفایل

استاد

• Bayesian Statistics
• Computer Experiments
• Uncertainty Quantification
• +۳ مورد دیگر

Emmanuel Vazquez is a Professor and researcher at CentraleSupélec, specializing in Bayesian design and analysis of computer experiments. He coordinates Data Science projects and leads research in Gaussian processes, uncertainty quantification, and optimization. He holds a PhD (2005) and HDR (2015) from Université Paris-Saclay, and has taught Bayesian statistics at CentraleSupélec. His work focuses on sequential design strategies, multi-fidelity simulations, and risk assessment in engineering and microbiology. He collaborates with institutions like the GdR MascotNum and PGMO, advancing methodologies for efficient simulation-based decision-making. His research includes applications in TBM excavation modeling, renewable energy integration, and food safety through quantitative microbial risk analysis. Education: École Normale Supérieure de Cachan (1997–2001), DEA/M2 in Mathematics (2001), PhD in Applied Mathematics (2005), Habilitation (2015). Research interests span Bayesian optimization, Gaussian process metamodeling, and stepwise uncertainty reduction (SUR) strategies. He develops algorithms for efficient sampling in expensive-to-evaluate systems, with applications to engineering, environmental modeling, and public health. His recent articles emphasize quantile set inversion, risk assessment in food safety, and scalable Gaussian process methods. Collaborations include projects on railway infrastructure digital twins (MINERVE) and multi-year renewable energy planning. He has contributed to open-source tools like the STK toolbox for kriging.

• Modality
• Evidentiality
• Evaluative Language
• +۷ مورد دیگر

Marta Carretero Lapeyre is a Full Professor at the Complutense University of Madrid, specializing in the Department of English Studies, Linguistics and Literature. Her research focuses on modality, evidentiality, and evaluative language in English and contrastive studies, alongside syntactic, semantic, and pragmatic analysis of noun phrases. Supervised 3 PhD dissertations Co-supervised 2 PhD dissertations Currently supervising 1 PhD dissertation Her work spans corpus linguistics, pragmatics, and digital communication. Recent publications address topics like machine learning applications in modal analysis, cross-linguistic pragmatic markers, and epistemic stance in pandemic discourse. She welcomes proposals on pragmatics, corpus linguistics, and digital communication research. Email: mcarrete@filol.ucm.es

Jesus Joaquin Garcia Molinaمشاهده پروفایل

استاد

• Computer Science
• Database Systems
• Model-Driven Engineering
• +۵ مورد دیگر

Jesus Joaquin Garcia Molina is a Professor in the Department of Computer Science and Systems Engineering at the Universidad de Murcia's Faculty of Informatics. He holds a Doctorate from the same university with a thesis on the conformational and dynamic study of flexible macromolecules (1988). His research focuses on database systems, model-driven engineering, NoSQL technologies, and software modernization. He is actively involved in projects related to schema evolution, user interface generation from wireframes, and AI applications in healthcare and education. Key research areas include database schema management, cross-platform data migration, and the development of unified query languages like Skiql. He has contributed to frameworks such as Guizmo for automated UI generation and tools for reverse-engineering legacy systems. His work bridges theoretical computer science with practical applications in software development and education technology. Recent publications emphasize NoSQL schema evolution, AI-driven radiology support systems, and the integration of interactive educational content in social robots. His interdisciplinary approach spans computer science, medicine, and pedagogy, with notable contributions to both academic and applied domains.

João Barrosoمشاهده پروفایل

دانشیار

• Digital Image Processing
• Accessibility
• Human Computer Interaction

João Barroso serves as Associate Professor with Habilitation at the University of Trás-os-Montes and Alto Douro (UTAD) and Senior Researcher at INESC TEC's Human-Centered Computing and Information Science Centre, where he has been Research Coordinator since October 2012. Previously, he held the position of Pro-Rector for Innovation and Information Management at UTAD from July 2010 to July 2013. His academic credentials include: Doctorate in Electrical Engineering from UTAD (2002) Habilitation in Informatics/Accessibility (2008) Barroso's research centers on Digital Image Processing, Accessibility, and Human-Computer Interaction, with significant extensions into biosignal processing for healthcare applications, machine learning for gaming and autonomous systems, and context-aware architectures for Industry 4.0 environments. His work consistently emphasizes real-world implementation, as demonstrated by his leadership in developing the ElderMind mobile application for cognitive stimulation and portable ECG/EMG acquisition systems. He founded two major conference series: Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI, 2006) and Technology and Innovation in Sports, Health and Wellbeing (TISHW, 2016). Analysis of his 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) Machine learning validation for gaming and autonomous driving (PPO vs. SAC algorithms), (2) High-fidelity biosignal acquisition systems with visual electrode monitoring, and (3) Systematic vulnerability analyses in web/mobile security and context-aware Industry 4.0 architectures. His publications demonstrate a consistent pattern of translating theoretical computing advances into practical health and industrial applications, with 2025 outputs showing particular emphasis on hardware-software integration for telemedicine and production management. Barroso has supervised 40 postgraduate students including Dennis Lourenço Paulino (2024, UTAD) who developed crowdsourcing personalization models and Luís Filipe Jesus Correia (2022, UTAD) who researched Brain-Computer Interfaces. His project portfolio spans 35 research and development initiatives, with recent grants supporting his virtual assistant prototype for Industry 4.0 production environments and high-resolution Bluetooth biosignal modules. He leads the Human-Centered Computing and Information Science Centre at INESC TEC, where his laboratory focuses on prototyping accessible technologies through interdisciplinary teams. Current projects integrate computer vision for elderly care, ROS2 middleware for unmanned vehicle coordination, and semantic interoperability frameworks for digital twins, maintaining his dual focus on theoretical innovation and socially impactful applications.

• Augmented Reality
• Conceptual Modeling
• Enterprise Modeling
• +۳ مورد دیگر

Dr. Fabian Muff is a Senior Researcher at the Department of Informatics, University of Fribourg, within the Interfaculty Informatics unit. He holds a position as Oberassistent (Senior Research Assistant) specializing in Digitalization and Information Systems. His research focuses on the intersection of conceptual modeling with augmented reality (AR) and virtual reality (VR), emphasizing workflow modeling languages, AR application frameworks, and AI integration in modeling tools. He teaches courses on Requirements Engineering, Information Systems Modeling, and Project Development. Dr. Muff's work addresses challenges in hybrid modeling environments, contextual AR applications, and the limitations of large language models in conceptual modeling. His recent projects include developing the M2AR web-based modeling tool and exploring the use of ChatGPT in schema generation. He actively collaborates with academic and industry partners, publishing widely in venues like ER Conference and AAAI workshops. His contributions span technical innovations in AR modeling languages (e.g., ARWFML) and methodological advancements in combining conceptual modeling with emerging technologies. Current research explores AI-augmented visualization, context-aware AR systems, and the ethical implications of digital-physical integration in enterprise systems.

Pasi Fräntiمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Pattern Recognition
• +۷ مورد دیگر

Pasi Fränti is a Professor of Computer Science at the University of Eastern Finland (UEF) since 2000, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. He holds MSc and PhD degrees from the University of Turku (1991 and 1994). His research focuses on machine learning, data mining, pattern recognition, and location-based systems, with notable contributions to clustering algorithms, image compression, and speech technology. Fränti leads the Machine Learning research group at UEF and has supervised 25 PhD students, covering topics like clustering, image/audio compression, and location-aware systems. His work bridges theoretical advances with practical applications in healthcare optimization, mobile services, and intelligent systems. He has published extensively, with over 79 journal articles and 167 conference papers, including 14 IEEE Transactions papers. Recent research highlights include optimizing health station locations via clustering algorithms, outlier detection improvements, and gamified location-based services (e.g., Mopsi). His interdisciplinary projects address challenges in urban planning, energy systems, and biomedical signal processing. Fränti actively critiques academic publishing practices, advocating for open science and peer review reforms. Key projects include the IMPRO initiative (2018–2023) supporting health/social services reform, and contributions to journals like Applied Computing and Intelligence . His lab develops tools for real-time data clustering and spatial analysis, with applications in mobile user behavior prediction and smart city infrastructure planning.

• Material Science
• Metrology Systems
• Manufacturing Engineering
• +۳ مورد دیگر

Bekim Gashi is an Assistant Professor at the CEES School of Engineering, specializing in measurement methods, metrology systems, and manufacturing engineering with a focus on sustainability. His research emphasizes advancing instrumentation for engineering systems, analyzing measurement systems' performance, and developing uncertainty budgets. He holds a PhD in Precision Machine Tools from Cranfield University (2009). Education: Doctorate in Precision Machine Tools (Cranfield University, 2005–2008). He serves as a Standard Committee Member at the British Standards Institute since 2017 and is an Associate Editor for the journal Measurement (2018–present). Research interests include polymer networks (epoxy, rubber), forensic materials, thermal analysis, and precision instrumentation. His work spans forensic applications like DNA-trapping coatings and high-temperature composite studies. Over 15 years, he has published on topics ranging from acoustic emission in composites to ISO standardization. His articles highlight trends in epoxy material science, forensic engineering, and metrology system optimization. Awards include an h-index of 64 and 4, citations. He advises PhD students and collaborates internationally on dimensional product specifications.

Isaac O. Afaraمشاهده پروفایل

دانشیار

• Applied spectroscopy
• Computational biophotonics
• Machine learning in biospectroscopy
• +۲ مورد دیگر

Isaac O. Afara is an Associate Professor at the Department of Technical Physics , Faculty of Science, Forestry and Technology , University of Eastern Finland . He leads the Biomedical Spectroscopy Lab (BSL) and the Biophysics of Bone and Cartilage (BBC) research group, focusing on translational spectroscopy and computational biophotonics for tissue diagnostics. Multi-disciplinary team of physicists and engineers Established 2018 (BSL), 1998 (BBC) His research bridges applied spectroscopy , machine learning , and biomechanics to develop optical methods for non-destructive assessment of biological tissues. Key areas include: Visible/NIR/Raman spectroscopy for cartilage/meniscus diagnostics Computational modeling of tissue optical properties Development of AI-driven preprocessing algorithms Characterization of viscoelastic properties in connective tissues Recent work demonstrates machine learning-enhanced spectroscopy for real-time cartilage assessment during arthroscopy, with applications in osteoarthritis detection, tissue engineering, and joint biomechanics. His 15 most recent articles (2025-2017) focus on: Cartilage/meniscus biomechanical properties Spectral differentiation of tissue pathologies Photon path length modeling Multi-sensor data fusion techniques Optical diagnostics for fibrosis and osteoarthritis Development of open-source preprocessing tools He collaborates with international institutions on computational biophotonics and has contributed to open-source Python modules for spectroscopic data analysis.

Tim Collinsمشاهده پروفایل

مدرس ارشد

• Medical Devices
• Machine Learning
• Applied Signal Processing
• +۵ مورد دیگر

Dr. Tim Collins is a Senior Lecturer and Programme Leader in Electrical and Electronic Engineering at Manchester Metropolitan University's Faculty of Science & Engineering. Holding a Ph.D. in Electronic & Electrical Engineering (1997) and a 1st class B.Eng. (1993) from the University of Birmingham, he specializes in applied signal processing across medical devices, 3D audio systems, and digital heritage preservation. His research bridges machine learning for physiological monitoring , virtual cuneiform tablet reconstruction , and acoustic interface design . Notable contributions include the Virtual Cuneiform Tablet Reconstruction Project and uncertainty-aware neural networks like DeepPulse for wearable heart rate estimation. He has served on editorial boards for MIT Press' PRESENCE: Virtual and Augmented Reality Journal and as guest editor for its special issue on user experience. As a Chartered Engineer and Senior Member of IEEE , he contributes to standards in wearable device reporting and collaborates with NVIDIA's Mixed Reality Technology Centre. His work appears in journals spanning health technology , acoustic engineering , and digital archaeology , with over 74,000 reads across 86 publications.

Oana Gogaمشاهده پروفایل

پژوهشگر

• AI and Society
• Security and Privacy
• Online Advertising
• +۳ مورد دیگر

Oana Goga is a Research Director at Inria, affiliated with the CEDAR team and Laboratoire d'Informatique de l'École Polytechnique (LIX). She holds a Ph.D. from Pierre et Marie Curie University. Her research focuses on AI and Society, addressing risks in online platforms, privacy, disinformation, and child protection. She leads interdisciplinary projects with economists, legal scholars, and social scientists. Awards & Grants: CNRS Bronze Medal (2024) Lovelace-Babbage Award (2023) ERC Starting Grant (2022) Andreas Pfitzmann Best Student Paper Award (2024) Advising & Tools: Guides Ph.D. students and postdocs on projects like CheckMyNews and AdAnalyst . Developed tools for auditing political ads and measuring platform risks. Labs & Collaborations: Active in the Inria CEDAR team and collaborates internationally on EU initiatives like the European Panel for Disinformation Monitoring .

Gregory Matthewsمشاهده پروفایل

دانشیار

• Statistical Disclosure Control
• Missing Data Methods
• Statistical Genetics
• +۳ مورد دیگر

Gregory Matthews is an Associate Professor in the Department of Mathematics and Statistics at Loyola University Chicago (LUC), serving as Director of the Center for Data Science and Consulting. He holds a Ph.D. in Statistics from the University of Connecticut (2011), with prior industry experience in predictive modeling (2004–2006) and postdoctoral research at the University of Massachusetts-Amherst (2011–2014). His research focuses on statistical disclosure control, missing data methodologies, statistical genetics, and sports analytics, with applications in anthropology, healthcare, and sports performance analysis. Dr. Matthews’ academic background includes a B.S. in Actuarial Science and M.S. in Applied Statistics from Worcester Polytechnic Institute. His work bridges theoretical statistics with practical applications, including developing imputation methods for aging curves in baseball, analyzing sports performance metrics, and addressing privacy challenges in data dissemination. He has contributed to open-source tools like the 'MixMAP' R package for genetic association studies and 'teamcolors' for sports data visualization. His publications span diverse topics such as dental morphology analysis in primates, predictive modeling for medical diagnostics, and methodological advancements in statistical disclosure limitation. He actively engages in interdisciplinary collaborations, particularly in paleontology, healthcare policy, and sports science. Dr. Matthews’ consulting work emphasizes ethical data practices, including privacy-preserving techniques for biomedical and social datasets. He has advised on projects ranging from emergency department protocols to optimizing NFL defensive strategies. His educational contributions include designing curricula for sports analytics and statistical methodologies.

Serdar Bozdagمشاهده پروفایل

دانشیار

• Machine Learning
• Computational Biology
• Bioinformatics
• +۵ مورد دیگر

Serdar Bozdag is an Associate Professor in the Department of Computer Science and Engineering at the University of North Texas, with a joint appointment in Mathematics. He serves as Director of the Center for Computational Life Sciences, focusing on interdisciplinary research at the intersection of computational methods and biological systems. His research expertise spans machine learning applications in bioinformatics, multi-omics data integration, and network biology. Key areas include developing algorithms for disease biomarker discovery, integrating genomic, transcriptomic, and clinical data through graph neural networks, and creating computational tools for precision medicine. Notable contributions include frameworks like CanMod2 for co-regulatory module inference and CTDPathSim for pathway-based similarity analysis. Bozdag’s work bridges computer science and life sciences, addressing challenges in Alzheimer’s disease diagnosis, cancer systems biology, and plant-microbe interactions. He has pioneered methods for analyzing dynamic biological networks and temporal data patterns, with applications in drug response modeling and electronic health record analysis. His lab develops open-source software tools widely used in computational biology research. Current projects emphasize AI-driven approaches for multi-modal healthcare data fusion, predictive modeling of disease progression, and scalable solutions for big biological data. His research has implications for improving diagnostic accuracy, personalized treatment strategies, and understanding complex biological systems.

• Dentistry
• Orthodontics
• Radiology
• +۶ مورد دیگر

Assoc. Prof. Dr. med. Laura Neimane serves as the Dean of the Faculty of Dentistry and holds an academic appointment in the Department of Conservative Dentistry and Oral Health at Rīga Stradiņš University (RSU). She also directs the Dentistry study program. Her professional roles include leadership in dental education, research, and clinical innovation. Dr. Neimane’s research focuses on advanced imaging techniques (CBCT, 3D-printed surgical models), bone regeneration materials, and AI applications in dentistry. She leads clinical trials evaluating treatments for tooth agenesis, molar transplantation, and osteoporosis-related bone deterioration in oral contexts. Her work integrates interdisciplinary approaches, combining dental surgery, radiology, and biomaterials science. She oversees the RSU Institute of Stomatology, which supports her research on implant stability, maxillary sinus augmentation, and temporomandibular joint disorders. Recent studies emphasize improving diagnostic accuracy through AI-driven image analysis and optimizing surgical techniques using 3D-printed tools. Dr. Neimane’s contributions bridge clinical practice and academic research, contributing to advancements in dental education and patient care in Latvia and beyond.

Hung Tongمشاهده پروفایل

استادیار

• Cluster Analysis
• Classification
• Mixture Modeling
• +۳ مورد دیگر

Hung Tong is an Assistant Professor in the Department of Mathematics at Rowan University's College of Science & Mathematics. He specializes in applied statistics and computational methods, with a focus on model-based clustering and handling complex data challenges like missing values, skewness, and outliers. His work intersects interdisciplinary consulting projects in public health, ecology, chemistry, education, nutrition, and business. Education: Ph.D., Applied Statistics, The University of Alabama M.S., Statistics, San Jose State University B.S., Applied Mathematics, San Jose State University Research Interests: Dr. Tong develops flexible clustering techniques and statistical software for incomplete or high-dimensional datasets. He emphasizes robust methodologies capable of addressing real-world data imperfections, such as skewness and outliers, to improve accuracy in data analysis. His contributions bridge theoretical advancements with practical tools for researchers across disciplines. Publications & Software: Key works include the MixtureMissing R package for robust clustering with missing data and studies on directional outlier detection and computational strategies for model-based clustering. His publications span journals like the Journal of Classification and Advances in Data Analysis and Classification. Teaching & Consulting: In Spring 2025, he teaches 'CONCEPTS STAT DATA ANALYSIS' and 'PROBABILITY/RANDOM VARIABLES'. Office hours are MW 2:00–3:00pm, T 10:00–11:00am, or by appointment. He is affiliated with the Statistical Consulting Group, offering expertise to researchers and students.