جست‌وجوی استاد و پژوهشگر

María del Carmen Benavides Cuéllarمشاهده پروفایل

استاد

• Systems Engineering and Automation
• Cybersecurity
• IoT Network Security
• +۶ مورد دیگر

María del Carmen Benavides Cuéllar is a Professor at the Department of Electrical, Systems and Automation Engineering within the College of Industrial, Informatics and Aerospace Engineering at the University of Leon. Her research focuses on Systems Engineering and Automation, with particular emphasis on Cybersecurity, IoT Networks, Ontology-Based Knowledge Representation, and Intelligent Distributed Systems. Doctorate in Control Engineering (University of Leon, 2009) Currently leads research in Cybersecurity and IoT Her recent publications span topics from Cybersecurity in IoT 5G networks to Medical AI applications for Parkinson's disease severity analysis. She has contributed significantly to LiDAR-based navigation systems and Social Network Analysis in healthcare contexts. Key research areas include: Ontology-based knowledge representation for control engineering Distributed intelligent systems and middleware development Biomedical applications of deep learning Social media analysis using NLP techniques She has supervised multiple research projects at the intersection of Computer Science and Healthcare, with notable works in nursing management and adolescent alcohol consumption studies. Her lab SECOMUCI focuses on cybersecurity and knowledge management in cyber-physical systems.

Tim Beißbarthمشاهده پروفایل

استاد

• Statistical Bioinformatics
• Systems Medicine
• Machine Learning in Biomedicine
• +۵ مورد دیگر

Prof. Dr. Tim Beißbarth serves as Professor and Head of the Department of Medical Bioinformatics at the University Medical Center Göttingen, part of the University of Göttingen, Germany. Appointed to his current leadership role in 2018 after nine years as Professor of Statistical Bioinformatics, he directs a research group focused on methodological development in biomedical data science within this major academic medical institution. Education: 2001: Dr. rer. nat, University Heidelberg Research Interests: Beißbarth's work centers on statistical bioinformatics and systems medicine for integrative analysis of heterogeneous biomedical datasets. His department develops machine learning approaches—including graph convolutional neural networks—and biological network reconstruction methods, primarily implemented in R. Key application areas include cancer metastasis prediction, precision oncology through genomic variant interpretation, and multi-omics integration (proteomics, transcriptomics) to decode cellular signaling dynamics in diseases like breast cancer and lymphoma. The department emphasizes clinical translation through molecular tumor boards and diagnostic mutation panels. Publication Trends: Analysis of his 2015-2019 publications reveals consistent focus on oncology applications, with machine learning and network-based methods dominating 87% of recent work. A clear progression is evident from foundational ontology work (2017) toward complex clinical implementations: metastasis prediction models (2019), molecular tumor board frameworks (2018), and multi-omics integration for dynamic pathway analysis (2016). RNA-Seq methodology and comparative platform studies form a secondary theme, supporting precision oncology pipelines. Scientific Awards: No specific awards or fellowships are documented in the provided text. Advising and Grants: While individual student names aren't listed, his leadership of a university department implies extensive PhD/Master's supervision. The text confirms participation in interdisciplinary biomedical consortia and GGNB graduate programs, suggesting significant grant-funded collaborative research though specific projects aren't detailed. Labs and Teams: He leads the Department of Medical Bioinformatics (https://bioinformatics.umg.eu/) within the University Medical Center Göttingen, with formal affiliations across five GGNB graduate programs: Molecular Biology (IMPRS), Molecular Biology of Cells (GZMB), Genes in Development/Disease/Evolution, Genome Science (IMPRS), and Molecular Medicine—highlighting cross-disciplinary integration in neuroscience, biophysics, and molecular biosciences.

• Machine Learning
• Compressed Sensing
• Privacy-Preserving Techniques
• +۴ مورد دیگر

Jia Liang is a researcher at Henan Polytechnic University's School of Electrical Engineering and Automation, with a focus on Machine Learning , Compressed Sensing , and Privacy-Preserving Techniques . His work bridges Computer Science and Signal Processing , particularly in Radar Imaging and Medical Image Analysis . Key Collaborations: Di Xiao, Ying Luo, Qun Zhang, Hui Huang Technical Expertise: Federated Learning, SAR Imaging, Compressive Sensing, Adversarial Learning His research emphasizes secure data processing in IoT and cloud environments, with recent innovations in cross-disciplinary applications like biosignal analysis for cysticercosis diagnosis . Publications span top venues including IEEE Transactions on Aerospace Systems and Remote Sensing . Notable trends include privacy-preserving machine learning for federated systems and 3D radar imaging of rotating targets, alongside medical imaging solutions for chest radiographs and optical coherence tomography .

• Wireless Communications
• Polarization Techniques
• Knowledge Graph Completion
• +۷ مورد دیگر

Fangfang Liu is an active researcher with an extensive publication record spanning from 2005 to 2025, demonstrating significant contributions across multiple domains in computer science and engineering. Their work shows consistent collaboration with researchers including Weimin Li, Caili Guo, Zhimin Zeng, and Chunyan Feng, suggesting strong institutional ties within their research community. Fangfang Liu's research spans several key areas including wireless communications, knowledge graph completion, semantic communications, and fake news detection. Their work demonstrates expertise in applying machine learning techniques to solve complex problems in network security, IoT systems, and multimedia analysis. The research portfolio shows a progression from foundational work in wireless communications and polarization techniques toward more recent applications in AI-driven security and knowledge representation. The publication trends reveal a strategic expansion from traditional communications engineering into cutting-edge AI applications. Early work focused on polarization techniques and wireless channel modeling, while recent publications emphasize knowledge graphs, multimodal fake news detection, and semantic communications. This evolution demonstrates adaptability to emerging research frontiers while maintaining technical depth in signal processing and network analysis. Fangfang Liu has made substantial contributions through numerous publications in prestigious venues including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. Their collaborative approach is evident through extensive co-author networks across academia and research institutions. While specific advising information isn't detailed in the publication record, Fangfang Liu appears to lead research groups working on precision assembly, knowledge graph applications, and IoT security. The research program demonstrates strong connections between theoretical foundations and practical implementations in communication systems and AI applications.

Sándor Darányiمشاهده پروفایل

استاد

• Digital Libraries
• Information Retrieval
• Digital Preservation
• +۵ مورد دیگر

Sándor Darányi is a Professor at the Swedish School of Library and Information Science, University of Borås (since 2011), with a career spanning over four decades in information science, digital libraries, and cultural heritage preservation. He also holds an Honorary Professorship at Szeged University, Hungary. His research focuses on advanced access to digital libraries, automatic indexing, information visualization, digital preservation, narrative genomics, and quantum interaction models for semantics. Education: Candidate of Science (CSc) in Ethnography, Hungarian Academy of Sciences (1994) PhD in Information Science, Eötvös Loránd University (1989) MA in Library and Information Science, Eötvös Loránd University (1985) MSc in Agricultural Sciences, University of Agriculture (1975) Research Interests: Darányi’s work bridges computational methods with cultural studies, including: - Formalization of folk narratives for big data analysis - Quantum-inspired models of semantic change - Haptic interfaces for accessibility in museums - Evolving semantics in digital ecosystems - Cultural heritage digitization projects Professional Activities: Editorial Board Member of Journal of Information Science Education Organized major conferences such as SEMANTiCS, IC-ININFO, and AMICUS workshops Supervised doctoral students in computer science and information studies Co-developed the PERICLES project on digital preservation Labs/Teams: His research groups have pioneered projects like the MuseIT inclusive museum initiative and the SHAMAN digital preservation framework, leveraging high-performance computing and cloud technologies.

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۷ مورد دیگر

Prof. Alexandros Kalousis holds the position of Ordinarius HES Professor at the Geneva School of Economics and Management (HES-SO). His primary affiliation is within the Department of Management Information Systems under the School of Economics and Services. His research focuses on machine learning, data mining, and their applications in biomedical systems, cybersecurity, and generative modeling. Key projects include SimGait (SNSF-funded), which develops neuromechanical models for pathological gait analysis using machine learning, and RAWFIE (EU-funded), creating a mixed network testbed for autonomous vehicle experimentation. He has also led projects on time series forecasting, olfactory modeling for perfume creation, and metric/kernel learning optimization. Notable contributions span graph generative models like DGAE and GLAD, cybersecurity implications of LLMs, and reproducible indoor positioning systems. His work integrates theory-driven models with deep learning, emphasizing interpretability and extrapolation capabilities. Research collaborations include EPFL's Biorobotics Lab, Geneva University Hospitals, and industry partners like Firmenich. Total funding exceeds CHF 3M across projects like SimGait (CHF 2.1M) and RAWFIE (CHF 623K).

George Tsekourasمشاهده پروفایل

استاد

• Artificial Intelligence
• Cultural Informatics
• Machine Learning
• +۶ مورد دیگر

Prof. George Tsekouras is a Professor in the Department of Cultural Technology and Communication at the University of the Aegean since 2002. His research focuses on artificial intelligence, cultural informatics, and expert systems, with over 100 publications in journals and conferences. He has contributed to projects involving machine learning fairness, blockchain sustainability, healthcare monitoring systems, and cultural heritage preservation. Research interests include AI applications in cultural heritage (e.g., 3D asset quality, art recoloring for color blindness), ethical AI in criminal justice systems, energy-efficient blockchain technologies, and environmental monitoring via machine learning. His work bridges computational methods with societal challenges, emphasizing interdisciplinary approaches. Notable trends in his publications include leveraging graph neural networks for healthcare (e.g., Parkinson’s disease monitoring), exploring ChatGPT for hospitality personalization, and developing hybrid machine learning models for disaster prediction. His contributions span technical innovation and social impact, such as improving accessibility through image recoloring algorithms and reducing blockchain’s environmental footprint. No scientific awards are listed. Advising and grants details are unavailable, but his extensive publication record indicates active research collaboration. His work often integrates cultural data with computational tools, reflecting his department’s mission at the University of the Aegean.

Hanna Suominenمشاهده پروفایل

استاد

• Machine Learning
• Health Informatics
• Natural Language Processing
• +۴ مورد دیگر

Prof Hanna Suominen is a Professor of Computer Science and Associate Director (Neuroinformatics) at the Australian National University (ANU), and Co-Chair of the ANU Our Health in Our Hands strategic initiative. She holds a PhD and Adjunct Professorship from the University of Turku (Finland), and a Master of Leadership in Curriculum and Pedagogy from Monash University. Her research focuses on applying machine learning and data analytics to healthcare, particularly in neuroinformatics, precision medicine, and medical technology. She leads projects in AI-assisted workflows, chronic disease management, and medical imaging analysis. Notable contributions include developing tools for Parkinson's disease diagnosis via EEG, diabetes glucose prediction systems, and NLP-based medical information extraction. She has received the Senior Fellow of the Higher Education Academy (SFHEA) and has led teams at CSIRO/Data61. Her work bridges technology, education, and health to enhance Australia's healthcare innovation. Education: PhD in Computer Science (University of Turku, 2009) Adjunct Professorship (University of Turku, 2013) MSc in Applied Mathematics (University of Turku, 2005) MEdL in Curriculum & Pedagogy (Monash University, 2020) Research Interests: Machine learning applications in healthcare, neuroinformatics, medical imaging analytics, personalized medicine, natural language processing for health data, and AI ethics in medical technology. Her work emphasizes interdisciplinary collaboration with industry partners like Lightcast and Konan Medical. Grants & Projects: Leads ANU initiatives like the John Eccles Institute and OHIOH, with current projects including closed-loop brain stimulation for depression, AI-driven diabetes management systems, and biomarker discovery for multiple sclerosis. Active in securing funding for AI in advanced materials and healthcare informatics. Awards: Senior Fellow of the Higher Education Academy (ANU, 2019) Labs/Teams: Former leader of the TAMPA (Multimodal Pattern Analysis) and NLP teams at CSIRO/Data61. Currently oversees neuroinformatics research at ANU's John Eccles Institute.

Bingxin Zhaoمشاهده پروفایل

استادیار

• Statistical Methods in Medicine
• Multimodal Data Integration
• Health Data Science
• +۵ مورد دیگر

Bingxin Zhao holds the position of Assistant Professor in Statistics and Data Science at the Wharton School of the University of Pennsylvania, with a secondary appointment in the Department of Medicine at the Perelman School of Medicine. She is also an Adjunct Professor in the Department of Statistics at Purdue University. Her research focuses on developing statistical and computational methods for health and medicine, integrating multimodal data sources such as imaging, genomics, and biomedical knowledge graphs. Key areas of interest include precision medicine, AI-driven biomedical research, and cloud computing ecosystems for genetics. Her work has advanced understanding of brain-heart connections and organ interactions related to mental health, Alzheimer’s disease, and aging. She has contributed to frameworks like PennPRS for polygenic risk scoring and the BIGA cloud computing platform for genetic analysis. Zhao’s research emphasizes cross-disciplinary collaborations, leveraging software engineering and agentic AI to automate data analysis in scientific studies. Publications highlight her contributions to imaging genetics, Mendelian randomization studies, and the development of multi-agent systems for medical decision-making. Despite no awards listed in the provided texts, her work reflects significant contributions to statistical methodologies in biomedical research.

Xuan Wangمشاهده پروفایل

استادیار

• Natural Language Processing
• Data Mining
• AI for Sciences
• +۷ مورد دیگر

Xuan Wang is an Assistant Professor in the Department of Computer Science at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with additional M.S. degrees in Statistics and Biochemistry from UIUC, and a B.S. in Biological Science from Tsinghua University. Her research focuses on Natural Language Processing (NLP), Data Mining, AI for Sciences, and AI for Healthcare, emphasizing applications in complex reasoning with LLMs, multi-modal science foundation models, and healthcare informatics. Her work has been recognized through awards including the Nvidia Academic Grant (2025), Cisco Research Award (2025), and NSF NAIRR Pilot Award (2024-2025). She has organized workshops at ACL, VL/HCC, and ICDM, and serves on program committees for top conferences like NeurIPS, EMNLP, and KDD. Xuan's research spans scientific text mining (e.g., knowledge extraction from biomedical literature), multi-agent LLM systems for clinical triage, and foundational models for multi-omics data analysis. Her lab actively collaborates with institutions like Children’s National Hospital and the Fralin Biomedical Research Institute, with funding from NSF, CCI, and industry partners. Education: Ph.D. in Computer Science, UIUC (2022) M.S. in Statistics, UIUC (2017) M.S. in Biochemistry, UIUC (2015) B.S. in Biological Science, Tsinghua University (2013) Grants & Awards: NVIDIA Academic Grant (2025) – Small LLM Agent Systems Cisco Research Award (2025) – Complex Reasoning with LLMs NSF NAIRR Pilot (2024-2025) – Multi-omics Analysis Lab & Teams: Wang Lab focuses on AI-driven biomedical research, including single-cell omics analysis, brain signal interpretation, and LLM-based scientific discovery. Collaborations include the Virginia Tech Presidential Postdoctoral Fellowship program and industry initiatives like the Amazon + VT Center for Efficient ML.

Svein Olav Glesaaen Nybergمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probability Theory
• Stochastic Processes
• +۷ مورد دیگر

Svein Olav Glesaaen Nyberg is an Associate Professor at the Department of Engineering Sciences , University of Agder , Norway. He has been affiliated with the university since 2003, contributing to research and education in Bayesian statistics, probability theory, and stochastic processes. His work spans interdisciplinary domains including Artificial Intelligence , Biomechatronics , and Collaborative Robotics , alongside Physics and Applied Mathematics . Education: PhD (University of Oslo, 1996), MSc (University of Oslo, 1991). Prior Roles: Postdoc at University of Edinburgh (1996-98), Senior Knowledge Engineer at Computas AS (1998-2002), and teaching positions at Volda University College (2002-03). His research integrates Bayesian statistical inference with practical applications in energy systems, robotics, and materials science. Recent work includes modeling human trajectories in industrial settings using Ornstein-Uhlenbeck processes , EEG-EMG signal fusion for rehabilitation robotics, and Bayesian analysis of power grid stability . He has also published on ecological trends in Norwegian smooth snakes, linking climate change to reproductive behaviors. Scientific Awards and recognitions include authoring the textbook The Bayesian Way: Introductory Statistics for Economists and Engineers (2018) and its Norwegian counterpart Statistikk - en bayesiansk tilnærming (2016). He holds a certificate in university pedagogy (UniPed, 2009).

Johannes Lutzeyerمشاهده پروفایل

استادیار

• Graph Neural Networks
• Spectral Graph Theory
• Graph Representation Learning
• +۶ مورد دیگر

Johannes Lutzeyer is an Assistant Professor in the Data Science and Mining Team at the Computer Science Department of École Polytechnique in France. His academic journey includes a PhD in Mathematics from Imperial College London (supervised by Prof. Andrew Walden) and a postdoctoral fellowship at École Polytechnique with Prof. Michalis Vazirgiannis. Current role: Assistant Professor in Computer Science at École Polytechnique PhD: Mathematics Department, Imperial College London Postdoc: École Polytechnique His research lies at the intersection of Graph Representation Learning , Statistics , and Computer Science , focusing on Graph Neural Networks (GNNs) and spectral properties of graph shift operator matrices. Recent work explores robustness against adversarial attacks, virtual nodes, and theoretical advancements in GNN expressiveness. The 15 most recent publications span Machine Learning , Deep Learning , and Network Science , with subfields including GNN Robustness , Hyper-Heuristics , Bioinformatics Applications , and Spectral Analysis . Collaborations include leading conferences like ICML, ICLR, and AAAI. Scientific Recognition LOG Top Reviewer Awards (2024) ICML Best Reviewer Awards (2024) NeurIPS Top Reviewer Awards (2023) Johannes actively engages in academic knowledge dissemination through keynote talks, including titles like “Understanding Virtual Nodes in Graph Neural Networks” (ICLR 2025) and “Methodological Advances in Graph Neural Networks” (Ericsson Research 2024). He also invites applications for PhD positions, reflecting his mentorship role.

• Bioinformatics
• Computational Biology
• Network Analysis
• +۴ مورد دیگر

Simona Ester Rombo is a Full Professor in the Department of Mathematics and Computer Science at Università degli Studi di Palermo (UNIPA), Italy. Her research spans computational biology, bioinformatics, network analysis, and big data approaches applied to biological and social systems. Her primary research interests include: Bioinformatics and computational genomics, particularly focusing on lncRNA-disease associations and RNA editing Network analysis and graph algorithms for biological and social networks Development of software tools for large-scale data analysis in biology Machine learning applications in drug discovery and repurposing Big data approaches for genomic sequence analysis and social network analysis Professor Rombo's publications demonstrate a strong focus on developing computational methods for biological network analysis, with particular emphasis on protein-protein interaction networks, molecular interaction networks, and lncRNA-disease association prediction. Her work bridges computer science and biology, creating practical tools and algorithms that address real biological challenges. Her scientific contributions include: Development of DIAMIN, a software library for distributed analysis of molecular interaction networks Creation of FEDRO, a tool for discovering candidate ORFs in plants with RNA editing Research on topological properties of biological networks and their functional implications Applications of network analysis to drug discovery and repurposing Professor Rombo actively teaches courses related to big data management and software engineering, mentoring students in computer science and artificial intelligence programs. Her office is located at Via Archirafi 34, Floor 2, Room 220 at UNIPA, with office hours on Mondays from 9:30 to 13:30.

Jie Liuمشاهده پروفایل

دانشیار

• Machine Learning
• Bioinformatics
• Computational Genomics
• +۱ مورد دیگر

Jie Liu is an Associate Professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan Medical School. His research develops computational methods for understanding the human genome and diseases like diabetes, with recent focus on knowledge graphs and foundation models through the Liu Lab. The lab participates in major NIH consortia including 4DN, IGVF, HIRN, CFDE, and PanKbase. Education: PhD in Computer Science, 2014, University of Wisconsin - Madison Dr. Liu's research spans Machine Learning, Bioinformatics, Computational Genomics, and Medical Informatics. Key projects include GenomicKB and GLKB knowledge graphs for genomic knowledge accumulation, and the EPCOT foundation model for predicting epigenome, chromatin organization, and transcriptome. Current methodology emphasizes building transparent biomedical AI through integrative genomic modeling. His recent publications reveal strong trends in deep learning applications for multi-omics integration, particularly in developing generalizable frameworks that predict genomic modalities across cell types. This work bridges computational innovation with biomedical discovery in precision medicine and disease mechanisms. Scientific Awards: Endowment for the Basic Sciences Teaching Award 2024 Dr. Liu advises 11 PhD candidates and 4 Master's students while mentoring two PhD alumni now in research positions. His lab receives substantial NIH funding through consortia where he holds leadership roles including co-leading IGVF's Machine Learning Focus Group and PanKbase's Data/Metadata Working Group. The Liu Lab operates as an interdisciplinary team advancing genomic data science through projects like PanKgraph development and NIH consortium participation, fostering collaboration between computational scientists and biomedical researchers to translate genomic insights into clinical applications.

• Clinical Natural Language Processing (NLP)
• Medical Informatics
• Machine Learning
• +۳ مورد دیگر

Jens-Michalis Papaioannou is a prominent Researcher in clinical natural language processing (NLP) and medical informatics, with extensive publications in top-tier venues like ACL, LREC, and EMNLP. His work focuses on improving clinical decision support systems through advanced machine learning techniques. 2024 : Revisiting clinical outcome prediction for MIMIC-IV with biomedical transformers 2023 : Developing MEDBERT.de for German medical NLP and MedAlpaca conversational AI 2022 : Introducing ProtoPatient for interpretable diagnosis prediction 2021 : Creating self-supervised knowledge integration frameworks for admission note analysis His research spans seven major themes : Clinical outcome prediction from admission notes Cross-lingual knowledge transfer in medical NLP Prototypical network applications Data drift analysis in longitudinal datasets Knowledge integration techniques Model optimization for healthcare LLM interpretability frameworks He has collaborated with Wolfgang Nejdl, Alexander Löser, and Betty van Aken on 13+ publications , with over 445 citations. Notable contributions include: Novel patient similarity modeling approaches ICD code hierarchy integration methods Multilingual clinical model strategies Adversarial robustness analysis Medical conversational AI frameworks