جست‌وجوی استاد و پژوهشگر

• Databases
• Big Data
• Data Storage
• +۱۲ مورد دیگر

Alberto Abello Gamazo is a full professor at the Facultat d'Informàtica de Barcelona (FIB), Universitat Politècnica de Catalunya (UPC), where he is affiliated with the Department of Services and Information Systems Engineering. He coordinates the Erasmus Mundus Doctoral Program in Information Technologies for Business Intelligence and leads research within the inSSIDE and inLab FIB groups. His work bridges computer science and healthcare, focusing on data-intensive systems and their real-world applications. Research Interests: His expertise spans databases, big data, NoSQL, OLAP, data storage, and data science. He investigates automated data pipelines, federated data management, feature selection, and AI-driven analytics. His work increasingly integrates healthcare applications, particularly in automated diagnosis using AI and digital microscopy for diseases such as malaria and schistosomiasis. Publication Trends: His recent articles (2023–2025) emphasize automated data science pipelines (e.g., Hyppo), fairness and interpretability in machine learning, efficient data management in document stores, and the application of AI to medical diagnostics. A strong trend is the development of low-cost, open-source robotic microscopy systems for global health challenges. Scientific Awards: Premiada (recognized activity) XXV Congreso Nacional de la Sociedad Española de Enfermedades Infecciosas y Microbiologia Clínica Advising and Grants: He has advised numerous PhD students and leads multiple competitive R&D+i projects funded by national and European programs (e.g., HORIZON 2020, Plan Nacional). His projects include 'Graph-driven federated data management,' 'EXPeriment driven and user eXPerience oriented analytics,' and health-focused initiatives like 'For an improvement of Health through rapid and economic diagnosis (iH-red).' He collaborates extensively with institutions like the Hospital de la Vall d'Hebron and Fundació PROBITAS. Labs and Teams: He is a core member of the inSSIDE (integrated Software, Services, Information and Data Engineering) and inLab FIB research groups, both affiliated with CIT-UPC. These groups focus on cutting-edge data engineering, information systems, and their societal applications.

Elodie Germaniمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Artificial Intelligence
• Healthcare Technology
• +۴ مورد دیگر

Elodie Germani is a Research and Teaching Associate in the MEDICIS research team at the University of Rennes' Laboratoire Traitement du Signal et de l'Image (Department of Signal, Image, and Language). She holds a PhD in Computer Science from the University of Rennes, where her thesis explored analytical variability in fMRI studies under supervision by Camille Maumet and Elisa Fromont. Previously, she completed a postdoc at Bonn University Hospital and a visiting research stint at Concordia University. Her work focuses on trustworthy AI in medical imaging, addressing variability and bias in neuroimaging analysis to improve healthcare accessibility and reproducibility. Affiliations : University of Rennes, Universitätsklinikum Bonn (postdoc), Concordia University (visiting) Education : PhD in Computer Science (Rennes), Master's in Bioinformatics Her research interests include mitigating analytical variability in fMRI studies, developing inclusive healthcare solutions, and advancing reproducible research practices. Key contributions include the HCP Multi-Pipeline dataset and style transfer techniques for neuroimaging standardization. She advocates for gender diversity in STEM through initiatives like the Lcodent Lcréent program and Pint of Science Festival. Awards : 2024 L'Oréal-UNESCO Young Talent Award for Women in Science Grants/Projects : Focus on AI-driven solutions for robust medical imaging analysis Elodie collaborates with interdisciplinary teams across Europe and North America, contributing to open science initiatives and bridging clinical needs with computational advancements.

Lucia Rita Quitadamoمشاهده پروفایل

پژوهشگر ارشد

• Epilepsy Research
• Neurophysiology
• Neural Signal Processing
• +۳ مورد دیگر

Lucia Rita Quitadamo is an Honorary Senior Research Fellow in the School of Psychology , focusing on neurophysiological research and biomedical engineering. Her work intersects epilepsy studies, brain-computer interfaces (BCI), and signal processing. Key research areas include: High-frequency oscillations (HFOs) in epilepsy surgery planning Intracranial EEG and MEG analysis for seizure onset zone localization Development of BCI systems using SSVEP and P300 protocols Sensory technology applications in surgery skill evaluation and prosthetic control Her contributions include the EPINETLAB software for epilepsy diagnosis and innovative sensory glove systems for surgical training assessment. Research spans over 20 years with notable work in neuroimaging, machine learning for medical signals, and clinical neurophysiology. Labs/Teams: Collaborates with neurology and biomedical engineering groups on interdisciplinary projects involving clinical neurophysiology and assistive technologies.

• Digital Health
• Wearable Sensors
• Artificial Intelligence
• +۶ مورد دیگر

Olga Korostynska is a Professor at Oslo Metropolitan University within the Faculty of Technology, Art and Design, Department of Mechanical, Electrical and Chemical Engineering. Her research spans digital health, wearable sensors, and robotics, with a focus on AI-integrated medical devices and environmental monitoring systems. Digital Health Technology Implementation Wearable Sensor Development Biomedical and Environmental Applications Recent publications highlight her work on microwave sensors for blood lactate monitoring, robotic meat processing platforms, and gamified rehabilitation devices. Her projects often bridge interdisciplinary domains, including EQUALS-EU initiatives for women in technology and AI-driven healthcare solutions. Korostynska contributes to sensor technology conferences and collaborates with teams like ADEPT (Advanced Health Intelligence and Brain-Inspired Technologies) to advance non-invasive diagnostics and rehabilitation tools.

• Human-Computer Interaction
• Wearable Computing
• Sensor Technology
• +۳ مورد دیگر

Kazuya Murao is an active researcher in human-computer interaction , wearable computing , and sensor technology , affiliated with Osaka University's Department of Information and Computer Science . His work focuses on leveraging PPG sensors , accelerometers , and acoustic sensing for biometric authentication , gesture recognition , and physiological signal processing . Recent publications highlight his exploration of smart ring authentication (2025), PPG-to-EMG conversion (2023), and non-invasive sensor attacks (2023). His research combines signal processing , machine learning , and user perception manipulation through sEMG feedback and tactile stimuli (2022). Key subfields : Biometric authentication, Sensor data analysis, Wearable interface design, Human activity sensing, Gesture recognition, Physiological modeling

Claudia Solis-Lemusمشاهده پروفایل

استادیار

• Statistical Phylogenomics
• Microbiome Analysis
• Network Modeling
• +۵ مورد دیگر

Claudia Solis-Lemus is an Assistant Professor at the University of Wisconsin-Madison with dual appointments in the Department of Plant Pathology within the College of Agricultural and Life Sciences and the Wisconsin Institute for Discovery. Her work bridges statistical methodology development with biological applications, focusing on creating computationally efficient models that maintain biological interpretability and theoretical guarantees. Dr. Solis-Lemus earned her PhD in Statistics and MA in Mathematics, both from the University of Wisconsin-Madison. Her academic journey has positioned her at the intersection of statistics, computational biology, and evolutionary genomics. Her research program centers on three interconnected areas: statistical phylogenomics, microbiome analysis, and the application of deep learning in biological contexts. In statistical phylogenomics, she develops novel methods to reconstruct phylogenetic networks that can account for reticulate evolutionary processes like hybridization and horizontal gene transfer. Her work in microbiome analysis focuses on creating statistical frameworks to model microbial communities as networks and use these networks to predict host phenotypes. In deep learning applications, she addresses challenges in applying neural networks to biomedical data by developing methods with statistical guarantees and improved interpretability. Analysis of Dr. Solis-Lemus's recent publications reveals a strong emphasis on developing computationally efficient methods for analyzing complex biological networks. Her work spans from theoretical advances in phylogenetic network inference to practical software implementations for microbiome analysis. A notable trend is her focus on creating methods that balance statistical rigor with computational tractability, particularly for large-scale biological datasets. Her research increasingly integrates machine learning approaches with traditional statistical methods to address challenges in evolutionary biology and microbiome research. Dr. Solis-Lemus has received significant recognition through competitive grants including: NSF CAREER Award (DEB 2144367) for "Towards Scalable and Robust Inference of Phylogenetic Networks" USDA Individual Hatch grant for "Enhanced interaction and network statistical models for microbiome data" DOE Computational Tool Development award for "Harnessing the power of big omics data" USDA-NIFA hatch project 1023699 for "Novel interaction and network statistical models for microbiome data" NSF IntBIO Collaborative Research DEB Award 2316269 USDA NIFA award for "Unraveling The Microbial Mechanisms That Mediate Disease Resurgence In Plants" Dr. Solis-Lemus actively mentors a diverse research group including postdoctoral researchers, PhD students, MS students, and undergraduates. Her lab is supported by multiple grants totaling millions of dollars, enabling research that spans from theoretical statistics to practical applications in agriculture and medicine. She has successfully secured funding from NSF, DOE, USDA, and industry partners, demonstrating the broad relevance of her work across multiple domains. The Solis-Lemus Lab operates as an interdisciplinary team bringing together statisticians, biologists, computer scientists, and domain experts. The lab maintains strong collaborations with researchers across UW-Madison and nationally, particularly in plant pathology, microbiology, and evolutionary biology. They develop open-source software tools that are widely used in the scientific community, including PhyloNetworks for phylogenetic network analysis and CMiNet for microbiome network construction.

Hugo Buddelmeijerمشاهده پروفایل

پژوهشگر

• Astronomy
• Astrophysics
• Scientific Visualization
• +۶ مورد دیگر

Hugo Buddelmeijer is an active Researcher at the Faculty of Science and Engineering , University of Groningen , affiliated with the Kapteyn Astronomical Institute . He specializes in astronomical data management, scientific visualization, and cosmological surveys. Research Interests : Galaxy evolution, photometric redshifts, data lineage, and visualization techniques for large-scale surveys like KiDS and Euclid. Technical Expertise : Development of simulation tools (e.g., ScopeSim), data catalog systems, and query-driven visualization frameworks. Contributions : Key participant in the KiDS and Euclid missions, focusing on optical/near-infrared imaging and dark matter analysis. His work emphasizes integrating machine learning with astronomical data. Collaborations : Extensive international partnerships with institutions like the European Southern Observatory and projects involving Very Large Telescope observations.

• Scientific Visualization
• Data Visualization
• Computer Graphics
• +۴ مورد دیگر

Pere Pau Vázquez Alcocer is an Associate Professor in the Department of Computer Science at Universitat Politècnica de Catalunya (UPC), affiliated with the ViRVIG research group and the School of Informatics (FIB). With a PhD in Software from UPC (2003), he specializes in Scientific Visualization , Data Visualization , and Virtual Reality applications. His work bridges Computer Graphics with Medical Data Visualization and Molecular Visualization , emphasizing AI applications to visualization . His research spans two decades, focusing on Interactive volume rendering VR-based biomedical analysis Perceptual optimization in visualizations Urban mobility data modeling with recent publications in Computer Graphics Forum , IEEE CG&A , and arXiv on AI-driven visualization frameworks and molecular interaction techniques. His 15 most recent publications (2025-2023) demonstrate trends in AI integration for data visualization, immersive analytics for biomedical applications, and optimization of rendering techniques on mobile and VR platforms, with keywords spanning Computer Science , Neuroscience , and Urban Planning . Awarded the Best PhD thesis (2003) and Best Paper at international conferences, he has supervised 8 PhD students including Pedro Hermosilla (now Assistant Professor at TU Wien) and Jesús Díaz (now Associate Professor at Universitat de Vic). Teaching for over 20 years at UPC across five schools, he currently leads courses in Data Visualization , Scientific Visualization , and Virtual Reality .

Paulo Félix Lamasمشاهده پروفایل

استاد

• Machine Learning
• Biomedical Signal Processing
• Probabilistic Reasoning
• +۳ مورد دیگر

Paulo Félix Lamas is a Full Professor at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He obtained his PhD in Physics from USC in 1999 and became an Associate Professor in 2002. As a foundational leader of CiTIUS (Research Centre in Intelligent Technologies), he served as Head (2010–2019) and Deputy Director (2019–2020). His research focuses on probabilistic learning , temporal abductive reasoning , and biomedical signal interpretation . Key applications include ECG analysis, glucose monitoring, and AI-driven healthcare solutions. His work bridges machine learning with clinical diagnostics, emphasizing interpretability and real-time systems. Recent publications (2015–2024) demonstrate interdisciplinary innovation, spanning: Healthcare AI : Personalized drug dosing, arrhythmia detection, and continuous glucose monitoring. Computational methods : Stochastic embeddings, kernel-based missing data handling, and adaptive clustering. Theoretical advances : Abductive reasoning frameworks for time-series interpretation. He leads major projects like: SOSFood (2024–2028): AI for sustainable food systems. XAI4SOC (2022–2025): Explainable AI for healthy aging. INSIDE (2022–2024): Predictive cardiac rehabilitation. At CiTIUS, he directs research on intelligent healthcare technologies, integrating sensor data with machine learning for chronic disease management.

Corina Dimaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Computational Linguistics
• Semantic Composition
• +۴ مورد دیگر

Corina Dima is a Researcher at the University of Stuttgart's Analytic Computing group (KI), specializing in natural language processing and computational linguistics. Her work bridges theoretical linguistics with practical applications in semantic composition, knowledge graphs, and biomedical entity linking, with strong collaborations at both the University of Stuttgart and University of Tübingen. Dr. Dima earned her PhD from the University of Tübingen in 2019 with a dissertation on composition models for nominal compounds. Her research focuses on semantic interpretation of multi-word expressions, distributional semantics, and German language processing, contributing foundational work on transformation weighting models and annotation schemes for compound-internal relations. Her publication trends reveal a strategic evolution from core NLP challenges (prepositional phrase attachment, noun compound interpretation) toward biomedical applications (WikiMed-DE) and knowledge graph evolution (Wikidated). Recent work emphasizes German-language biomedical entity linking and efficient composition models that balance performance with parameter reduction, demonstrating consistent innovation in dataset construction and model design. As part of the Analytic Computing research group, Dr. Dima contributes to the University of Stuttgart's artificial intelligence initiatives, particularly in semantic web technologies and knowledge-driven NLP systems. Her collaborative projects with Steffen Staab and Erhard Hinrichs highlight her integration within a leading European research ecosystem focused on scalable language understanding solutions.

Bettina Finzelمشاهده پروفایل

پژوهشگر

• Explainable Artificial Intelligence
• Neuro-Symbolic Artificial Intelligence
• Human-AI Interaction
• +۴ مورد دیگر

Bettina Finzel is a doctoral candidate and Teaching Assistant at the Cognitive Systems Group, Faculty of Information Systems and Applied Computer Sciences, Otto-Friedrich-Universität Bamberg. She specializes in Explainable Artificial Intelligence (XAI) with a focus on human-centered approaches for medical and educational applications.

Roman Hornungمشاهده پروفایل

پژوهشگر ارشد

• Random Forests
• Statistical Methodology for Cross-Lab Prediction
• Statistical Methodology for Multi-Omics Data
• +۵ مورد دیگر

Dr. Roman Hornung is a Research Fellow at the Institute of Medical Informatics, Biometry and Epidemiology (IBE) within Ludwig-Maximilians-Universität Munich (LMU), specializing in Biometry in Molecular Medicine. His work focuses on advancing statistical and machine learning methodologies for biomedical applications. Academic Rank: Research Fellow Key Research Areas: Random Forests, Multi-Omics Data Integration, Interpretable Machine Learning, Cross-Validation Techniques, Biomedical Informatics, and Meta-Research on Prognostic Modeling Teaching: Leads seminars on applied statistics in epidemiology and contributes to clinical epidemiology tutorials Software Contributions: Maintainer of R packages like diversityForest and prioritylasso His research bridges statistical theory and practical biomedical applications, with a particular emphasis on reproducibility and robustness in predictive modeling. He has developed novel random forest variants for multi-class outcomes and multi-omics data. Hornung’s recent publications and submitted work highlight trends in benchmarking machine learning methods, improving cross-study prediction, and understanding the impact of data preparation on model performance. Notably, he has contributed to methodologies for generalization error estimation, variable importance measures, and diversity-enhanced forest algorithms. As a member of the Biometry in Molecular Medicine working group, he collaborates on interdisciplinary projects integrating clinical and omics data. His habilitation at LMU Munich (2024) and editorial roles at Biometrical Journal and Scientific Reports underscore his academic leadership in statistical methodology for health sciences.

Aleš Smrdelمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Usability Testing
• User Interface Design
• +۳ مورد دیگر

Aleš Smrdel is an Assistant Professor at the Faculty of Computer and Information Science, University of Ljubljana. He is affiliated with the Laboratory for Biomedical Computer Systems and Imaging. Research Interests: Designing and implementing user interfaces Usability testing and human-computer interaction Biomedical signal analysis, particularly ECG and ischemia detection Selected Publications: Focus on algorithm development for cardiovascular diagnostics, including ST segment analysis and open-source tools for ECG evaluation. Laboratory: Active member of the Laboratory for Biomedical Computer Systems and Imaging.

Mickey Scheinowitzمشاهده پروفایل

استاد

• Exercise Physiology
• Cardiovascular Physiology
• Sports Technology
• +۳ مورد دیگر

Prof. Mickey Scheinowitz serves as Director of the Sylvan Adams Sports Institute at Tel Aviv University within the Iby and Aladar Fleischman Faculty of Engineering's Department of Biomedical Engineering. His research integrates Exercise Physiology , Sports Technology , and Biomechanics to enhance athletic performance and recovery. Current affiliations: Tel Aviv University (Faculty of Engineering, Biomedical Engineering Department) Research domains: Thermal physiology, Anaerobic power prediction, Cycling biomechanics, Fatigue monitoring, Mental-physiological performance interactions Email: mickeys@tauex.tau.ac.il Research Trends in his 2023-2025 publications show: Advancements in dynamic SPECT imaging for cardiovascular diagnostics Development of machine learning algorithms for exercise stress test interpretation Exploration of microbiome-exercise interactions in endurance athletes Innovations in non-invasive temperature monitoring during high-intensity activity Laboratory Operations focus on multi-disciplinary approaches to athletic performance optimization, located in the Multi-disciplinary Center (room 412) with integration of thermal imaging , EMG analysis , and SportsTech innovations .

• EEG
• Quantitative EEG
• sLORETA
• +۸ مورد دیگر

Dr. Eng. Piotr Schneider serves as an Assistant Professor at the Department of Neuroinformatics and Biomedical Engineering, Faculty of Mathematics, Physics and Computer Science, University of Maria Curie-Skłodowska (UMCS). His academic profile includes active research in neuroinformatics and biomedical engineering, with office hours held Wednesdays and Thursdays in Room D513. Position: Assistant Professor Department: Neuroinformatics and Biomedical Engineering Faculty: Mathematics, Physics and Computer Science University: Maria Curie-Skłodowska University (UMCS) Email: piotr.schneider@mail.umcs.pl Office Hours: Wednesday 11:00-12:00, Thursday 13:30-14:30 Research interests center on EEG-based analysis of cognitive processes, particularly in credibility evaluation and psychiatric disorders. His work combines neuroscience with machine learning tools to model brain activity patterns, focusing on ERP components like P300, source localization techniques (sLORETA), and biomarker identification. Recent publications demonstrate expertise in neural signal processing for credibility assessment, machine learning applications in EEG classification, and brain-computer interface development. Key areas include message/source credibility neural correlates, psychiatric disorder EEG patterns, and practical implementations using Azure machine learning and MATLAB tools. With an ORCID ID 0000-0002-9666-9408 and 11 total publications, Dr. Schneider maintains a strong presence in computational neuroscience and biomedical signal processing. His research combines quantitative EEG analysis , artificial intelligence , and neuropsychiatric applications to advance understanding of brain processes.