جست‌وجوی استاد و پژوهشگر

Christoph F. Eickمشاهده پروفایل

دانشیار

• Knowledge Discovery and Data Mining
• Evolutionary Computing
• Data Science
• +۴ مورد دیگر

Dr. Christoph F. Eick is an Associate Professor in the Department of Computer Science at the University of Houston, where he directs the UH Data Analysis and Intelligent Systems Lab (UH-DAIS). His research spans multiple cutting-edge domains including knowledge discovery, evolutionary computing, spatial data analysis, and artificial intelligence, with recent projects focusing on disaster computing, computational frameworks against modern slavery, and threshold-based association analysis. His research interests center on developing advanced computational frameworks for spatial and spatio-temporal data analysis, supervised clustering algorithms, and extracting knowledge from social media. Dr. Eick's work frequently intersects with practical applications in environmental monitoring, disaster response, and social media sentiment tracking. Research trends in his recent publications show a strong focus on spatial data mining techniques, blockchain applications for disaster management, medical image analysis using deep learning, and innovative frameworks for emotion tracking in geospatial contexts. His work consistently bridges theoretical computer science with real-world problem-solving. Recipient of multiple competitive national and international fellowships Regular program committee member for IEEE ICDM and AAAI conferences He actively advises PhD and Master's students on projects ranging from supervised clustering to spatial association mining. His lab maintains collaborations with industry partners and has secured grants for research in multi-run clustering and geo-targeting frameworks. Dr. Eick leads the UH-DAIS research group which focuses on developing novel algorithms for data mining and intelligent systems, with particular emphasis on real-world applications in environmental science, healthcare, and social media analysis.

Marylyn D. Ritchieمشاهده پروفایل

استاد پژوهشی

• Translational Bioinformatics
• Genomics
• Electronic Health Records (EHR)
• +۴ مورد دیگر

Dr. Marylyn D. Ritchie is a Research Professor at the Perelman School of Medicine, University of Pennsylvania, and leads the Biomedical and Translational Informatics Laboratory. Her work focuses on integrating electronic health records (EHR) with genomics using advanced computational methods. BS, University of Pittsburgh at Johnstown (1999) MS, Vanderbilt University (2002) PhD, Vanderbilt University (2004) Dr. Ritchie's research centers on translational bioinformatics, developing meta-dimensional approaches to analyze multi-omics data, clinical information, environmental exposures, and social determinants of health. Her work aims to uncover the genetic architecture of complex diseases through statistical and AI-driven methods. Recent publications highlight her team's advancements in AI-driven medical imaging analysis, Alzheimer's disease risk prediction, and network-based multiomics studies. Her lab utilizes the EHR-linked Penn Medicine Biobank as a living laboratory for precision medicine implementation. Dr. Ritchie actively contributes to implementation science through pharmacogenomics research and learning health systems frameworks, with a focus on translating genomic discoveries into clinical practice.

• Graph-based learning
• Adaptation and learning in non-stationary environments
• Intelligence for embedded systems
• +۵ مورد دیگر

Cesare Alippi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), and also holds a professorship at Politecnico di Milano, Italy. He serves as a visiting Professor at Guangdong University of Technology (China) and Consultant Professor at Northwestern Polytechnic of Xi'an (China). His academic leadership extends to multiple international institutions where he has served as a visiting researcher including UCL (UK), MIT (USA), ESPCI (France), CASIA (China), A*STAR (Singapore), and University of Kobe (Japan). Professor Alippi's research interests center around graph-based learning, adaptation and learning in non-stationary environments, and intelligence for embedded, cyber-physical systems and IoT. His work bridges theoretical foundations with practical applications in sensor networks, environmental monitoring, and industrial processes. He has established significant research infrastructure including the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments for marine environment monitoring in Queensland, Australia and the Fiji Islands, as well as rockfall and landslide monitoring systems across Italy and Switzerland. His research output shows a clear evolution toward graph-based deep learning approaches for time series analysis, anomaly detection, and spatiotemporal forecasting, reflecting the growing importance of graph neural networks in handling complex relational data in non-stationary environments. Major Awards: IEEE CIS Enrique Ruspini Meritorious Service Award (2024) IEEE CIS Outstanding Computational Intelligence Magazine Paper Award (2018) Gabor Award from International Neural Network Society (2016) IBM Faculty Award (2013) IEEE Instrumentation and Measurement Society Young Engineer Award (2004) Professor Alippi has held significant leadership roles including Past Board of Governors member of the International Neural Network Society, Past member of the Administrative Committee of the IEEE Computational Intelligence Society, and Past Vice-President for Education of the IEEE Computational Intelligence Society. He has served as Associate Editor for Proceedings of IEEE and several other prestigious journals. His research has been supported through numerous grants including an IBM Faculty Award in 2013 specifically for research on Intelligent Embedded Systems working in non-stationary environments. His research infrastructure includes the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments including a sophisticated automatic, adaptive, sustainable and reliable wireless monitoring system for marine environments deployed in Queensland, Australia (2007) and under deployment at the Fiji Islands (2014-2015). He has also led several top-world deployments for rockfall and landslide monitoring across Italy and Switzerland since 2010, demonstrating the practical impact of his research in real-world harsh environments.

• Probabilistic Machine Learning
• Causal AI
• Imprecise Probabilities
• +۲ مورد دیگر

Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.

• Biomedical Ontologies
• Clinical Natural Language Processing
• Health Informatics
• +۳ مورد دیگر

Dr. Vipina K. Keloth is an Associate Research Scientist in the Department of Biomedical Informatics & Data Science at Yale School of Medicine. She holds a PhD in Computer Science from New Jersey Institute of Technology (2021) and has prior postdoctoral experience at Yale BIDS and the University of Texas Health Science Center. Her expertise includes biomedical ontologies, clinical natural language processing (NLP), and social determinants of health (SDOH). Education & Training: Postdoctoral Associate, Yale University (2024) Postdoctoral Research Fellow, University of Texas Health Science Center (2023) PhD, New Jersey Institute of Technology (2021) MS, National Institute of Technology Karnataka (2014) MSc, Mahatma Gandhi University (2010) BS, Kannur University (2007) Research Focus: Dr. Keloth develops AI-driven solutions for clinical text analysis and EHR optimization. Her work emphasizes entity recognition, ontology integration, and large language model applications in healthcare. Recent projects include: LLM-based detection of gastrointestinal bleeding in EHRs Unified ontologies for SDoH and clinical data Federated frameworks for multi-institutional healthcare analytics Collaborations: Frequently collaborates with Prof. Hua Xu, Dennis Shung, and Qingyu Chen on biomedical NLP and health informatics projects. Active in AMIA and journal review roles. Grants & Labs: Engaged in National Institutes of Health-funded projects on EHR mining and functional status ascertainment. Leads Yale BIDS initiatives in AI-driven clinical decision support systems.

• Computational Modeling
• Mathematical Modeling
• Neurostimulation
• +۲ مورد دیگر

Edward T. Dougherty is a Professor at Roger Williams University's College of Arts and Sciences with an active research career spanning computational modeling applications across neuroscience, biomedical informatics, and environmental science. His work demonstrates interdisciplinary expertise connecting mathematical theory with practical solutions to complex scientific problems. Primary research domains include neurostimulation modeling, knowledge graph development for clinical research, and computational approaches to environmental challenges Recent work focuses on enhancing medical question-answering systems using BioBERT and developing interactive platforms for exploring COVID-19 research landscapes Longitudinal research shows consistent contributions to mathematical modeling of biological systems with applications in pest management and soil science Dr. Dougherty's publication trajectory reveals evolving research interests from agricultural modeling toward biomedical applications, with increasing focus on computational neuroscience and health informatics in recent years. His collaborative approach is evident through partnerships across multiple disciplines. While no specific awards are documented in the available materials, his sustained publication record in reputable venues demonstrates scholarly recognition. Dr. Dougherty actively mentors students as evidenced by frequent co-authorship with junior researchers on complex computational projects. His research methodology combines advanced mathematical techniques with domain-specific knowledge, creating bridges between theoretical computation and practical applications in healthcare, neuroscience, and environmental management.

Haohui Luمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Graph Machine Learning
• Health Informatics
• +۱۲ مورد دیگر

Haohui Lu is a Research Fellow at the Molly Wardaguga Research Centre within the Faculty of Health. He is registered to supervise postgraduate research and holds a PhD from The University of Sydney, awarded in March 2024. His work bridges artificial intelligence and public health, with a focus on ethical and explainable AI systems. Research Interests: Artificial Intelligence and Machine Learning Graph Machine Learning and Knowledge Graphs Health Informatics and Clinical Decision Support Bias Mitigation and Fairness in AI Large Language Models for Healthcare Statistical Modeling and Real-World Data Analysis His recent publications demonstrate a strong trend in applying machine learning—particularly graph-based and language models—to public health challenges such as disease progression, policy analysis, and undernutrition. He frequently publishes in high-impact journals like PLoS One , Scientific Reports , and Neurocomputing , and presents at top venues such as EMNLP. His work emphasizes fairness, interpretability, and practical impact for policymakers and communities. Scientific Contributions and Recognition: Active contributor to UN Sustainable Development Goals, particularly in health and well-being. Presented research at EMNLP 2024 on LLMs for disease prediction. Research disseminated through news outlets and widely shared on academic platforms like Mendeley. Advising and Leadership: Registered as a postgraduate research supervisor, indicating a role in mentoring future researchers. Provides technical leadership in AI tool development, research data platforms, and LLM applications, supporting multidisciplinary and international collaborations. Laboratories and Teams: Based at the Molly Wardaguga Research Centre, a hub for health research, where he contributes to projects integrating AI with real-world health data and policy.

• GeoInformatics
• Geographic Information Systems (GIS)
• 3D Geographic Information Systems (3D-GIS)
• +۱۲ مورد دیگر

Ismail Rakip Karas is a Professor of Computer Engineering and Head of the 3D GeoInformatics Research Group at Karabuk University, Turkey. He holds a BSc from Selcuk University (1997), MSc from Gebze Institute of Technology (2001), and PhD from Yildiz Technical University (2007). His career includes roles as Visiting Researcher at Universiti Teknologi Malaysia (2010–2014) and Research Assistant at Gebze Institute of Technology (2000–2009). Currently, he serves as Deputy Rector of Karabuk University and has held administrative positions including Dean of Safranbolu Fine Art and Design Faculty, Director of Safranbolu Vocational School, and acting Dean of the Faculty of Architecture. Research interests span GeoInformatics, 3D GIS, network analysis, spatial data structures, and intelligent transportation systems. He has led over 20 national/international projects, including EU-funded initiatives and collaborations with institutions like Nara Institute of Science and Technology and University of Szeged. His work emphasizes smart city applications, indoor navigation, and emergency evacuation models. Publications include over 100 peer-reviewed articles and book chapters, focusing on GIS applications, machine learning in geospatial analysis, and 3D modeling. He has organized conferences like Geo-Advances 2017 and serves on editorial boards of journals such as the International Journal of Geo-Spatial Knowledge and Intelligence.

• Internet of Things (IoT)
• Sensor Networks
• Automated Learning
• +۶ مورد دیگر

Marimuthu Palaniswami is a Professor in the Department of Electrical and Electronic Engineering at the University of Melbourne, specializing in IoT, sensor networks, and computational intelligence. He leads large-scale research initiatives, including the ARC Research Network ISSNIP and the Centre of Excellence for Networked Decision Systems (CENDS). His work spans infrastructure, smart cities, healthcare, and environmental monitoring, with over 400 publications in top-tier journals and conferences. Education Ph.D., University of Newcastle, Australia Masters (Research), University of Melbourne Masters (Coursework), Indian Institute of Science Bachelor of Engineering (Honours), University of Madras Research Focus His research translates into deployable technologies for societal impact, including smart city projects (e.g., European Union's FP7 Smart Santander), healthcare diagnostics (e.g., CSIRO/Microsoft fetal monitoring tools), and environmental monitoring (Great Barrier Reef initiatives). He emphasizes interdisciplinary collaboration, leveraging IoT and AI for healthcare, transport, and governance. Recent Projects Developing Non-Invasive Diagnostics to Prevent Stillbirth (2019–2025) Hydrogen Storage Using Concrete Composites (2022–2025) Academic Centres of Cyber Security Excellence (2024–2026) Awards & Recognition Fellow of IEEE (2013–2015 Distinguished Lecturer) Knowledge Transfer Excellence Awards (2008, 2008, 2010) Recipient of multiple government and industry grants totaling millions in funding Professional Contributions Co-founder of the European IoT Forum, former co-director of CENDS, and advisor to EU and U.S. projects. Has organized over 10 IEEE conferences and served on NSF and EU panels. Labs & Teams Leads interdisciplinary teams in IoT, healthcare technology, and sensor networks, fostering collaborations with industry partners like Arup and Microsoft Research.

• Manufacturing Analytics
• Process Optimization
• Machine Learning
• +۵ مورد دیگر

Dr. Rajesh Ransing is an Associate Professor in Mechanical Engineering at Swansea University's School of Aerospace, Civil, Electrical and Mechanical Engineering. His research focuses on manufacturing analytics, process optimization, and computational modelling of gait analysis and prosthetic design. He has contributed to risk-based manufacturing strategies and uncertainty quantification, with over 20 years of academic and industry collaboration. Expertise: Nanoscale fluid dynamics, thermal systems, and industrial sustainability Leadership: Supervised 10+ postgraduate researchers Affiliations: Faculty of Science and Engineering, Morgan Advanced Studies Institute His work bridges computational methods with real-world applications, including Industry 4.0 integration in foundries and prosthetic biomechanics. Recent publications emphasize data-driven approaches in heat transfer and fluid dynamics.

Karen Raffertyمشاهده پروفایل

استاد

• Virtual Reality
• Mixed Reality
• Machine Intelligence
• +۳ مورد دیگر

Professor Karen Rafferty is a prominent academic and Head of the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. She holds the rank of Professor and specializes in Virtual Reality (VR), Mixed Reality (MR), and Multi-sensory Interaction technologies, with a focus on their applications in Health & Training and Industry & Automation. Her research emphasizes disruptive technological practices, leveraging software engineering, sensor fusion, and real-time systems. Prof. Rafferty has led or contributed to numerous national and EU-funded projects, including roles as Principal Investigator (PI) and Co-Investigator (CoI) in grants such as the ARISE project on steel erection robotics and the PWC Research and Development Centre. She is actively involved with professional bodies like the IET, serving as a registration interviewer and evaluator for EU research programs (H2020, FP7, FP6). Her awards include the Best Paper Award at the 33rd International Manufacturing Conference and recognition for contributions to Digital Enterprise Technology. She teaches Engineering Entrepreneurship and VR/AR technologies, emphasizing interdisciplinary innovation. Key research themes span AI-driven systems, blockchain for sustainable decision-making, and haptic feedback integration in immersive environments. Her work often bridges academia and industry, with practical applications in manufacturing automation and healthcare training.

• Toxicogenomics
• Nanomaterials
• Atopic Dermatitis
• +۴ مورد دیگر

Dario Greco is a Professor in Biomedicine, specializing in toxicogenomics, nanomaterials, and adverse outcome pathways. His research integrates interdisciplinary approaches to address challenges in chemical safety, sustainable materials innovation, and disease mechanisms. Key projects include the CompSafeNano initiative for safe-by-design nanomaterials and the INSIGHT framework for chemical/material assessment. He collaborates globally on studies involving transcriptomics, epigenetics, and systems biology. Research Contributions: Greco has published extensively on nanomaterial toxicity, fibrosis mechanisms, and multi-omics integration. He developed tools like the MUUMI R package for meta-analysis and contributed to datasets on interstitial lung diseases and atopic dermatitis. His work emphasizes translating omics data into actionable insights for drug discovery and regulatory science. Collaborations & Impact: Active in EU-funded projects, Greco bridges academia and industry through initiatives like the PINK project, advancing sustainable materials markets. His research aligns with UN SDGs, particularly addressing health and environmental sustainability. Collaborations span institutions worldwide, focusing on nanoinformatics, toxicogenomics, and systems toxicology.

• Stream Reasoning
• Semantic Web
• Knowledge Graphs
• +۴ مورد دیگر

Pieter Bonte serves as an Assistant Professor (tenure track) in the Department of Computer Science at KU Leuven's Faculty of Engineering Science, based at the Kulak Kortrijk Campus. He holds dual institutional affiliations as a core member of Leuven.AI - KU Leuven Institute for Artificial Intelligence and participates in academic governance through the Council of the Faculty of Engineering Science, Computer Science Department Council, and Science, Engineering and Technology Group Council at Campus Kulak Kortrijk. His research program centers on Stream Reasoning and Semantic Web technologies, with specialized expertise in Knowledge Graph embeddings , neurosymbolic reasoning , and edge analytics for IoT data streams . Current investigations focus on resource-optimized stream processing, cross-context learning architectures, and ontological frameworks for heterogeneous streaming data. His work bridges theoretical foundations with healthcare and communications network applications, emphasizing practical deployment in distributed environments. Recent publications (2023-2025) reveal dominant themes in RDF stream processing systems, benchmarking methodologies for knowledge graph embeddings, and ontology engineering for streaming linked data. His output consistently targets high-impact venues including VLDB Journal, Semantic Web journal, and DEBS conference, demonstrating leadership in stream reasoning standardization through comprehensive surveys and vision papers. The research trajectory shows increasing integration of symbolic AI with neural approaches for edge-constrained environments. As principal investigator, Bonte leads three major funded projects through 2028: Stream Reasoning for efficient Edge Processing , Neurosymbolic Stream Reasoning , and Edge analytics of heterogeneous IoT data streams . His teaching portfolio includes core computer science courses in Operating Systems (X0A48A), Declarative Languages (X0C99A), Computer Systems Organization (X0E31A), and Scientific Training (X0D49A), reflecting his commitment to foundational and emerging topics in data-intensive systems. Bonte's research group operates within KU Leuven's Leuven.AI ecosystem and Kulak campus infrastructure, maintaining extensive international collaborations evident in multi-institutional publications. The team specializes in developing semantic stream processing frameworks for real-world applications in healthcare monitoring and communications networks, with active partnerships across European research institutions.

• Image Processing
• Neuroscience
• Adaptive Smoothing Methods
• +۴ مورد دیگر

Dr. Karsten Tabelow is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, Germany, affiliated with the group Stochastic Algorithms and Nonparametric Statistics . His work bridges mathematical statistics and biomedical imaging through advanced computational methods. PhD in physics (2001) and Diplom in physics (1997) from Freie Universität Berlin Key projects: Matheon grants F10 and A3 on biomedical imaging Current focus: adaptive smoothing , quantitative MRI , and nonparametric statistical modeling His research has produced foundational contributions in neuroimaging software , including R packages adimpro , dti , fmri , and qmri , as well as SPM toolboxes like ACID-Toolbox and aws4SPM . He extends these methods to inverse problems in magnetic resonance and electron microscopy. Collaborations span neuroscience (Charité), semiconductor physics (Leibniz Institute), and biomedical engineering (Max Planck Institute). Recent work includes physics-informed data assimilation for intracranial pressure estimation and mathematical research data infrastructure through the MaRDI initiative under Germany's NFDI. Publications emphasize open science principles using R/knitr workflows. Grants include Matheon projects and MaRDI development. Software contributions are distributed under GNU GPL within R and SPM ecosystems. Talks highlight applications in presurgical planning , in-vivo histology , and cross-disciplinary data management .

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Tomasz Kajdanowicz serves as an Associate Professor in the Department of Artificial Intelligence at Wrocław University of Science and Technology's Faculty of Information and Communication Technology. His research bridges theoretical machine learning with practical cybersecurity and natural language processing applications. His primary research interests focus on graph neural networks , large language model optimization , and interpretable cybersecurity systems . Notable contributions include the MIRAD framework for ransomware detection and PUGG dataset development for Polish language processing. His work demonstrates consistent innovation in graph representation learning, particularly through multiplex graph fusion techniques and knowledge graph enrichment strategies. Analysis of his recent publications reveals a strong trajectory toward trustworthy AI systems with emphasis on hallucination detection (FactSelfCheck), latent space optimization (LatentPrompt), and synthetic data generation for medical applications. His cybersecurity research shows increasing sophistication in interpretable threat detection mechanisms. His academic supervision includes multiple doctoral and master's students evidenced through co-authorship patterns, particularly in graph-based learning and NLP projects. Current collaborations span biomedical signal processing (fNIRS analysis) and efficient LLM deployment strategies. His laboratory work centers on the AI Security Lab within the Department of Artificial Intelligence, focusing on adversarial robustness testing and real-time network monitoring systems. Key initiatives include the development of Polish-language NLP benchmarks and cross-domain graph embedding frameworks.