جست‌وجوی استاد و پژوهشگر

• Pattern Recognition
• Artificial Intelligence
• Music Information Retrieval
• +۷ مورد دیگر

Alicia Garrido Alenda is an Assistant Professor in the Department of Software and Computing Systems at the University of Alicante, affiliated with the Escuela Politécnica Superior. Her work is closely tied to the Pattern Recognition and Artificial Intelligence Group (gRFIA), where she contributes to interdisciplinary research at the intersection of computing and music technology. Her research interests include: Pattern Recognition and Artificial Intelligence Music Information Retrieval and Audio Processing Automatic Transcription of Music Scores Semantic Processing of Digital Music Feature Extraction and Classifier Fusion Machine Translation and Shallow Parsing Compiler Design and Programming Education The two publications attributed to her reflect a dual expertise: one in natural language processing and machine translation between Romance languages, and another in compiler design. These suggest a strong foundation in formal systems, parsing, and symbolic computation, which likely underpin her current work in music transcription and analysis. Her research bridges symbolic and audio-based representations, particularly in multimodal music processing. She supervises PhD students engaged in cutting-edge music technology research: Víctor Arroyo Martínez – Multimodal Transcription of Music Scores María Concepción Hontanilla Alfonso – Technologies for Interactive Music Learning She is actively involved in teaching, currently offering Programming 2, and maintains regular office hours, indicating an ongoing academic role. She participates in research projects related to music encoding, machine learning for music, and pattern recognition, contributing to international workshops and conferences in these domains.

Aristidis Likasمشاهده پروفایل

استاد

• Neural Networks
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Aristidis Likas is a Professor at the Department of Computer Science and Engineering, University of Ioannina, Greece. His research spans neural networks, machine learning, data mining, and intelligent systems with applications in medicine and biology. He has contributed to image processing, video analysis, and scientific computing, with a focus on algorithm innovation and biomedical applications. His work has been published in journals such as IEEE Transactions on Neural Networks , Pattern Recognition , and Bioinformatics . He has also served as Associate Editor for IEEE Transactions on Neural Networks (2008-2012) and co-chaired conferences including ECML/PKDD 2011 and SETN 2014. Key projects include VIDEOSUM: Video Summarization PRotEin S-Sulfenylation Server (PRESS) Global k-means algorithm Dip-means clustering

Richard Wicentowskiمشاهده پروفایل

استاد

• Natural Language Processing
• Computational Linguistics
• Computational Morphology
• +۴ مورد دیگر

Richard Wicentowski is a Professor of Computer Science at Swarthmore College. His research focuses on computational linguistics, including computational morphology, semantic disambiguation, and sentiment analysis. He teaches courses like CS 65 (Natural Language Processing) and CS 21 (Introduction to Computer Science). Ph.D. , Computer Science, Johns Hopkins University, 2002 M.S. , Computer Science, University of Pittsburgh, 1995 B.S. , Computer Science, Rutgers University, 1993 Wicentowski’s research spans Natural Language Processing , Computational Linguistics , Medical Informatics , and Computer Science Education . He has developed systems for sentiment classification using Twitter data, emotion detection in clinical texts, and cross-lingual lexical substitution. His work bridges machine learning with linguistics and health informatics. Recent publications highlight trends in sentiment analysis (2015–2025), medical text mining (2006–2012), and educational innovation (2005–2015). He has collaborated with students on NLP projects and explored diversity in computing education in 2025. Wicentowski advises students through co-authorship in projects like SWATCS65 and SWATAC. Notable collaborations include courses on machine learning and image processing (2005).

Jin Geمشاهده پروفایل

استادیار

• Liver Disease
• Liver Transplantation
• Cirrhosis
• +۶ مورد دیگر

Jin Ge, MD, MBA is an Assistant Professor in the Department of Medicine at the University of California, San Francisco (UCSF) School of Medicine. He serves as a gastroenterologist and transplant hepatologist in the Liver Transplant Program, with affiliations at the UCSF Bakar Computational Health Sciences Institute and the UCSF-UC Berkeley Joint Program in Computational Precision Health. After earning a bachelor's degree in engineering at Princeton University (2008), he obtained combined MD and MBA degrees from the University of Pennsylvania (2015) before completing residency in internal medicine and fellowships in gastroenterology and transplant hepatology at UCSF (2018-2022). His work focuses on applying clinical informatics, artificial intelligence, and data science to enhance outcomes for patients with advanced liver disease through innovative technologies and decision support systems. Liver disease management Transplant hepatology Clinical decision support systems Large language models Health equity in organ allocation Electronic health record analytics His research output spans 15+ peer-reviewed publications covering topics from AI-guided liver transplantation allocation to NLP-driven cirrhosis prognosis , with multiple first-author studies in Hepatology and Liver Transplantation . Recent work includes developing a liver disease-specific chat interface using retrieval-augmented generation and implementing clinical decision support systems through human-centered design. APASL 2023 Best Oral Presentation AASLD 2021 Anna S. Lok Award UCSF 2019 Clinical Fellow Award University of Pennsylvania 2015 Palmer Scholar 20+ professional recognitions across academia As a practicing clinical researcher, he directs the Division of Gastroenterology's Clinical AI initiatives and maintains active involvement with the National COVID Cohort Collaborative (N3C) Consortium. His clinical practice addresses complex hepatology cases requiring multidisciplinary management strategies.

• Computational Humanities
• Text Technology
• Quantitative Corpus Linguistics
• +۷ مورد دیگر

Prof. Dr. Alexander Mehler is a full professor of Computational Humanities and Text Technology at the Goethe University Frankfurt , where he leads the Text Technology Lab (TTLab) . He has held leadership roles in multiple academic societies including the German Society for Computational Linguistics & Language Technology and the German Society of Semiotics , and is a founding member of the German Society for Network Research (DGNet) . His research focuses on quantitative analysis of textual units in spoken/written communication, linguistic network modeling , and 4D text technologies integrating Virtual/Augmented Reality. Current projects include ENTAILab (DFG-funded infrastructure for social sciences), FACES (VR-based survey research), and C08/B05 modules in the Critical Online Reasoning (COR) framework. He has supervised numerous PhD candidates in areas spanning multilingual NLP , 3D scene generation from text , stemma analysis tools , and network-theoretic approaches to language classification . His lab's work intersects with machine learning , cognitive modeling , and digital humanities infrastructure . As principal investigator, he leads projects funded by DFG (SPP 2431, SFB 1629, FID 326061700) and LOEWE programs , with recent developments in mimodockerized NLP pipelines and VR-based learning systems . Key collaborations include ACM Conference on Hypertext and KONVENS NLP conferences . His methodological expertise spans multimodal data analysis , neural active learning , and cross-linguistic computational models . He has developed open-source infrastructures for digital humanities research and educational analytics , including the TextImager platform .

Miriam Rediمشاهده پروفایل

پژوهشگر

• Computer Vision
• Multimodal Machine Learning
• Knowledge Equity
• +۳ مورد دیگر

Miriam Redi is a Researcher at the Wikimedia Foundation , where she has worked since 2017. She focuses on Computer Vision and Multimodal Machine Learning solutions to improve knowledge equity across Wikimedia projects. Her work intersects Responsible AI , Citation Integrity , and Visual Knowledge Representation . Research Interests: Fair, interpretable machine learning systems Image recommendation algorithms for unillustrated content Quantifying knowledge gaps across Wikimedia platforms Citation analysis and verifiability in Wikipedia Impact of multimedia elements on knowledge understanding Key Publications demonstrate expertise in algorithmic assessment of Wikipedia content, cross-modal embeddings for image recommendations, and citation usage analysis. Her work includes development of open datasets like Citation Detective and tools for structured task implementation. Academic Leadership: Co-chair for the annual Wiki Workshop series at The Web Conference (2019-2021), and contributor to Wikimedia's research showcase events.

• X-ray Physics
• Synchrotron Radiation
• X-ray Free Electron Lasers
• +۱۲ مورد دیگر

Pablo Villanueva Perez is a Senior Lecturer and Associate Senior Lecturer in the Department of Physics at Lund University’s Faculty of Engineering (LTH). He is a Principal Investigator in Synchrotron Radiation Research and actively contributes to key interdisciplinary initiatives including NanoLund, the LTH Profile Areas in Nanoscience and Photon Science, and the LU Light and Materials research environment. He is also affiliated with eSSENCE: The e-Science Collaboration, highlighting his role in computational and data-intensive science. His research focuses on developing novel X-ray imaging methods and instruments that leverage the unique properties of diffraction-limited synchrotron-radiation facilities and X-ray free-electron lasers, such as MAX IV Laboratory and the European XFEL. His work spans advanced imaging techniques including ptychography, phase contrast imaging, and crystal optics, with applications in materials science, nanotechnology, and structural biology. The research contributes to UN Sustainable Development Goals related to innovation, industry, and sustainable infrastructure. The recent publications highlight a strong trend in high-resolution, time-resolved, and multi-modal X-ray imaging, particularly using next-generation synchrotron and XFEL sources. These works emphasize technical innovation in instrumentation, reconstruction algorithms, and experimental design for in-situ and operando studies. Pablo Villanueva Perez is involved in significant research projects, including AI-Twin (funded by the Swedish Research Council and eSSENCE), which integrates physics-informed AI for imaging reconstructions, and XUVPM (funded by Horizon Europe), focusing on extreme ultraviolet ptychographic microscopy. These projects reflect his leadership in merging advanced computational methods with experimental physics. He supervises research students, including Gunnarsson J. K., and collaborates extensively with national and international teams. His work is supported by major grants from the Swedish Research Council, eSSENCE, and the European Commission, underscoring the strategic importance and impact of his research. He is part of collaborative networks involving institutions across Europe and contributes to the development of beamlines and imaging infrastructure. His role as a supervisor and co-investigator positions him at the forefront of next-generation X-ray science and technology.

• Computational Imaging
• X-ray Imaging
• Phase Retrieval
• +۱۳ مورد دیگر

Manuel Guizar Sicairos is an Associate Professor of Physics at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Institute of Physics (IPHYS), and leads the Computational X-ray Imaging group at the Paul Scherrer Institut (PSI). He has held these joint positions since January 2023, following a progression from Postdoctoral Fellow (2010) to Senior Scientist (2021) at PSI. B.Sc. in Physics Engineering, Tecnológico de Monterrey, Mexico (2002) M.Sc. in Electronic Systems, Tecnológico de Monterrey, Mexico (2005) M.Sc. in Optics, University of Rochester, USA (2008) Ph.D. in Optics, University of Rochester, USA (2010) His research centers on computational imaging, particularly for synchrotron X-ray sources, with a focus on phase retrieval, ptychography, coherent diffractive imaging, holography, tomography, and scanning small-angle X-ray scattering (sSAXS). He has co-developed key techniques such as 3D nanoscale ptychography, magnetization vector nanotomography, and small-angle scattering tensor tomography (SASTT). His work emphasizes experimental design, novel imaging configurations, and algorithm development for hyperspectral and dynamic nanotomography. The recent articles highlight a consistent trend in high-resolution 3D imaging of complex materials using correlative X-ray techniques. His publications span topics from integrated circuits and magnetic materials to hierarchical composites, demonstrating expertise in both algorithmic innovation and experimental application. The integration of ptychography with sSAXS and vector tomography enables multiscale, multimodal investigations across materials science and biology. Innovation Award on Synchrotron Radiation (2014, 2021) ICO Prize (2019) Fellow of The Optical Society (2021) Fellow of SPIE SPIE Community Champion (2019) Multiple Optics & Photonics Education Scholarships (2004–2009) He advises PhD students including Fang Wenxuan and Karabay Aknur at EPFL. He has secured institutional support for advancing imaging research at both PSI and EPFL. His group develops open-source algorithms such as those for subpixel registration, Hankel transforms, and tomographic reconstruction (e.g., GridrecMS). He is a confidential advisor for the Respect@PSI campaign, promoting diversity and inclusion in scientific research. His leadership supports large-scale facility research at PSI and academic training at EPFL. He leads the Computational X-ray Imaging group at PSI, which collaborates closely with the cSAXS beamline and focuses on advancing computational methods for synchrotron-based imaging. The team integrates algorithm development with experimental validation, fostering interdisciplinary research across physics, materials science, and bioimaging.

• Information Retrieval
• Bibliometrics
• Scientific Search
• +۴ مورد دیگر

Birger Larsen is a Professor at Aalborg University with a distinguished research career spanning over two decades in information retrieval, bibliometrics, and open data applications in education. His scholarly work demonstrates sustained contributions to the fields of information science and digital libraries, with particular expertise in polyrepresentation of information needs and scholarly impact measurement. His research interests focus on Information Retrieval , Bibliometrics , Scientific Search , Polyrepresentation in Information Needs , Open Data in Education , and Author Impact Measurement . Larsen's work bridges theoretical information science with practical educational applications, particularly in developing open data competencies in school settings. His recent publications demonstrate an evolution from foundational bibliometric research to contemporary applications of open data in elementary and secondary education. Larsen's scholarly output shows consistent trends toward practical applications of information retrieval systems in educational contexts, with increasing focus on open data literacy and digital competencies for young learners. His work with collaborators like Rikke Magnussen and Ingrid Mulder has established frameworks for integrating open government data into school curricula. Larsen has made significant contributions to major information retrieval conferences including SIGIR, CHIIR, and CLEF, with particular involvement in evaluating research impact and scholarly communication practices. His collaborative research approach is evident through extensive co-authorship networks, particularly with Christina Lioma (35 publications) and Peter Ingwersen (28 publications), demonstrating long-standing research partnerships that have shaped his scholarly trajectory.

• Natural Language Processing
• Legal Informatics
• Greek Language Processing
• +۴ مورد دیگر

Ion Androutsopoulos is a Professor in the Department of Informatics at Athens University of Economics and Business (AUEB), where he leads the AUEB NLP Group. With over 180 publications spanning three decades, he is a prominent figure in Natural Language Processing research, particularly known for his work bridging NLP with legal informatics, Greek language processing, and biomedical applications. His research interests focus on several interconnected areas of Natural Language Processing: Legal Informatics : developing NLP systems for legal document analysis, legal judgment prediction, and legal reasoning, with recent work including GreekBarBench and Archimedes-AUEB systems Greek Language Technology : creating specialized tools for Modern Greek processing, including GR-NLP-TOOLKIT and Greeklish transliteration systems Biomedical Text Mining : working on diagnostic captioning and medical image analysis through participation in ImageCLEFmedical challenges Financial NLP : developing systems like EDGAR-CRAWLER for financial document analysis and XBRL tagging His recent publications (2023-2025) show an increasing focus on Greek-specific NLP resources and practical applications of large language models in legal reasoning. His work often combines theoretical NLP advances with practical implementations, particularly through his leadership of the AUEB NLP Group which regularly participates in international evaluation campaigns. Professor Androutsopoulos has supervised numerous PhD students who have become active researchers in their own right, including John Pavlopoulos, Prodromos Malakasiotis, and Ilias Chalkidis. His collaborative network spans multiple institutions and disciplines, reflecting the interdisciplinary nature of modern NLP research.

• Semantics & Concepts
• Grounded / Embodied Cognition
• Functional MRI
• +۳ مورد دیگر

Dr. Philipp Kuhnke is a postdoctoral researcher affiliated with the Lise Meitner Research Group 'Cognition and Plasticity' in cooperation with Leipzig University, Germany. His work bridges cognitive neuroscience, linguistics, and neuroimaging methodologies. Ph.D. in Cognitive Sciences (2021), University of Potsdam / Max Planck Institute for Human Cognitive and Brain Sciences M.Sc. in Cognitive Science (2016), University of Osnabrück B.Sc. in Cognitive Science (2014), University of Osnabrück Dr. Kuhnke's research focuses on semantics and conceptual processing, grounded cognition, and neuroimaging techniques like fMRI and TMS. His studies explore how the brain retrieves semantic knowledge, adapts to language tasks, and recovers from neurological impairments. His recent publications highlight interdisciplinary approaches combining neurostimulation and neuroimaging to understand reading networks, semantic cognition, and post-stroke recovery. He investigates neural plasticity mechanisms in dyslexia and aphasia, analyzing effective connectivity patterns in language networks.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۷ مورد دیگر

Professor Julie Weeds is a faculty member at the School of Engineering and Informatics , University of Sussex. Her academic journey includes a DPhil in Natural Language Processing (2003), MPhil in Computer Speech and Language Processing (2000), and MA in Computer Science (1998). She has held positions including Postdoctoral Research Fellow (2003-2005, 2012-2016, part-time), Lecturer in Data Science (2016-present), and Professor of Artificial Intelligence (2023-present). MA Computer Science - Trinity Hall, Cambridge University (1995-1998) MPhil Computer Speech and Language Processing - Cambridge University (1999-2000) DPhil Natural Language Processing - University of Sussex (2000-2003) Her research spans Natural Language Processing , Machine Learning , and Computational Linguistics , focusing on semantic compositionality , vector representation analysis , and linguistic variation . Recent work applies NLP to dream reports , wildlife conservation , and mental health support . She leads grants related to hate speech analysis , suicide prevention , and illegal wildlife trade detection . Publications reveal trends in semantic entailment modeling , dream analysis using LLMs , and ecological informatics applications . Key themes include syntax-driven compositionality , multilingual modeling , and structure-aware paraphrase identification . Professor Weeds supervises research projects and teaches courses including Advanced Natural Language Engineering, Natural Language Processing, and Data Science methods. Collaborators include David Weir, Luca Bertolini, and Qi Peng, with affiliations to CASM Consulting LLP and Innovate UK projects.

Mathias Géryمشاهده پروفایل

مدرس

• Structured Information Retrieval
• Social Information Retrieval
• Multimedia Information Retrieval
• +۱ مورد دیگر

Mathias Géry is a Lecturer in Computer Science at Jean Monnet University, affiliated with the Computer Science Department within the Faculty of Science and Technology. He conducts research as a member of the Data Intelligence Group at the H. Curien Laboratory (UMR CNRS 5516) in Saint-Étienne, France. Research Focus: His work centers on advanced Information Retrieval systems, with four core pillars: Structured IR (specializing in textual IR, XML, hypertexts, and structured documents), Social IR (analyzing social networks and relationships), Multimedia IR (developing text-image fusion models and representation frameworks), and Web Mining (including usage analysis, corpus collection, and web analytics). His methodologies consistently integrate user profiles and social context to enhance personalized information access. Publication Trends: Recent publications (2015-2024) reveal a strong trajectory in personalized and social IR, with significant contributions to query expansion, language modeling, and social bookmarking systems. His interdisciplinary reach extends to health informatics (2024 RCT on sedentary adults), sports science (2023 gender-based endurance analysis), quantum physics (2021 turbulence study), and literary analysis (2019 Russian literature), demonstrating exceptional versatility across computational and humanities domains. Collaborative Environment: As an active researcher within the CNRS-affiliated H. Curien Laboratory, he contributes to the Data Intelligence Group's mission of advancing data-driven methodologies through structured, social, and multimodal approaches.

François Jacquenetمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Natural Language Processing
• +۵ مورد دیگر

François Jacquenet is a Professor of Computer Science at the University of Saint-Etienne, where he is a member of the Machine Learning Team at the Hubert-Curien Laboratory. His research focuses on machine learning and data mining applications for natural language processing, with significant contributions to privacy-preserving systems including Hippocratic Multi-Agent Systems and Automata-Based Sequence Mining. His research interests span Machine Learning , Data Mining , Natural Language Processing , and Privacy-Preserving Systems . Jacquenet has led multiple research projects including the PASCAL II Network of Excellence (2008-2012), the Bingo2 project (2008-2010), and the Web Intelligence project (2006-2008), where he focused on ethical web design and privacy protection techniques. His work bridges theoretical foundations with practical applications in areas like fraud detection, meeting summarization, and video tag correction. Analysis of his recent publications reveals a consistent research trajectory in deep learning applications , privacy-preserving techniques , and cross-modal learning . His work shows a progression from foundational research in grammatical inference and automata theory to contemporary applications in neural networks and self-organizing systems. The publications demonstrate strong interdisciplinary connections between computer science, physics, and security applications. Best AI Paper Award at Conference (2006) Professor Jacquenet has supervised numerous PhD students including Maria Galvan, Hoang-Tung Tran, Ludivine Crépin, and Stéphanie Jacquemont. His research has been supported by significant grants including the French Research Agency (ANR), the Rhône-Alpes region, and international networks like PASCAL. He has organized multiple conferences including Privacy on the Web at the ACM Symposium on Applied Computing and the PASCAL Workshop on Teaching Machine Learning. He is actively involved with the Machine Learning Team at Hubert-Curien Laboratory , contributing to the PASCAL Network of Excellence and the REWERSE Network. His research group focuses on developing practical applications of machine learning while addressing fundamental theoretical questions in pattern mining and language learning.

• Social Computing
• Natural Language Processing
• Dialogue Systems
• +۵ مورد دیگر

Gerasimos (Jerry) Spanakis is an Assistant Professor at the Department of Advanced Computing Sciences and Maastricht Law+Tech Lab, Faculty of Law, both at Maastricht University in the Netherlands. His research focuses on Social Computing, including computational social media modeling, dialogue systems, information retrieval, and multimodal pattern discovery using Large Language Models. Member of Cognitive Systems research area at DACS/UM Member of Maastricht Law and Tech Lab Principal Investigator for EU project VOXReality Researcher for NSMD, HumanAds, RegTech4AI Technical expert for European Commission E-enforcement academy project Coordinator of MaastrichtNLP reading group Jerry leads the supervision of bachelor's and master's theses across various computational topics and has advised numerous ongoing and completed students. His work intersects AI ethics, legal tech, and behavioral modeling, with a focus on responsible AI deployment and social bias detection in language models. Technical expert for European Commission Co-organizer of the Natural Legal Language Processing (NLLP) Workshop Active in Open Science Community Maastricht