جست‌وجوی استاد و پژوهشگر

Lawrence Cavedonمشاهده پروفایل

استاد

• Artificial Intelligence
• Spoken Dialogue Systems
• Biomedical Analytics
• +۳ مورد دیگر

Lawrence Cavedon is a Professor and Research Director at the RMIT Artificial Intelligence Hub within the School of Computing Technologies at RMIT University. He serves as the Associate Dean and Discipline Lead for Data Science and Artificial Intelligence. His career spans academia and industry, focusing on industry-relevant research in spoken dialogue systems, biomedical analytics, and AI. Current roles: Research Director, RMIT AI Hub; Professor, School of Computing Technologies Past positions: Stanford University (2002–2005), National ICT Australia (NICTA, 2005–2015), Verticalnet Inc. (Silicon Valley, 2000–2002) Research interests include spoken dialogue systems, healthcare analytics (collaborating with organizations like Alfred Health and Telstra Health), and AI applications. He has led projects with industry partners such as IBM Research, SEEK, and Elsevier. His work has resulted in commercial innovations, including the first Semantic Web Services platform and patented technologies. Awards and recognition include the NSW Engineering Excellence Award and multiple U.S. patents. He actively supervises PhD/Master’s students in areas like cross-modal information extraction, emotion recognition, and conversational search systems. Teaching focuses on advanced software engineering courses, such as Software Engineering: Process and Tools (COSC2299). He has secured over $3 million in ARC grants, including leadership roles in the ARC Industry Transformational Training Centre for Cognitive Computing in Medical Technologies. His research impact includes improving job search algorithms (with SEEK) and developing clinical decision aids for prostate cancer patients. He leads the RMIT AI Hub, fostering collaboration between academia and industry to address real-world challenges.

Manuel de Buenaga Rodríguezمشاهده پروفایل

استاد

• Natural Language Processing
• Biomedical Informatics
• E-Health Systems
• +۴ مورد دیگر

Manuel de Buenaga Rodríguez is a Professor at the Escuela Politécnica, Department of Computer Science at Universidad Europea de Madrid. He holds a PhD from Universidad Complutense de Madrid (1996), focusing on integrating natural language processing techniques for software component retrieval. His research spans natural language processing, biomedical informatics, eHealth systems, and information retrieval, with a focus on applying AI and big data to healthcare and education. He leads the TIFyC research group (Technologies for Training and Knowledge). Education PhD in Computer Science, Universidad Complutense de Madrid (1996) Research Interests Dr. Buenaga's work emphasizes NLP applications in healthcare, including systems like BioAnnote for biomedical document annotation, and projects like Naviga (an accessible platform for seniors and disabled individuals). His recent efforts include AI-driven educational tools using GPT models and IoT-based chronic patient monitoring systems. He also explores semantic relations in ontologies and interoperability in eHealth systems. Key Projects Medical-Miner: Integrating text mining with data analytics for translational medicine POESIA: Open-source internet safety platform using NLP and image analysis Virtual Cloud Carer: IoT and cloud-based health services for chronic patients Publications His recent work highlights trends in GPT-based educational systems, IoT healthcare monitoring, and cross-domain information integration. Notable contributions include frameworks for personalized health systems and semantic interoperability in biomedical contexts. Grants & Collaborations Active in EU-funded initiatives like Naviga and MAVIR network projects. Collaborates with institutions globally on eHealth and educational technology advancements. Labs/Teams Leads the TIFyC group, developing tools like TMT for clinical text navigation and MDFaces for cross-domain concept recognition.

• Artificial Intelligence
• Data Management
• Health Informatics
• +۶ مورد دیگر

María Elena García Barriocanal is a Professor in the Computer Science Department at the University of Alcalá, Spain. She specializes in Artificial Intelligence, Data Management, and Health Informatics, leading the I4 research group focused on intelligence, innovation, internet, and information systems. Her work integrates semantic web technologies, blockchain, and quantum computing with interdisciplinary applications in healthcare, education, and social media analysis. Education: She holds a Doctorate from the University of Alcalá (2004) with a thesis on knowledge models for usability evaluation using questionnaires. Her research spans over two decades, with a focus on developing practical frameworks for data-driven decision-making and educational technology. Research Interests: Her work bridges theoretical computer science with real-world problems. Key areas include AI ethics, biomedical decision support systems, blockchain-based metadata, quantum software engineering, and social media-driven epidemiological modeling. She also explores semantic integration in disaster management and learning analytics. Publications: Her recent work emphasizes AI tool development (e.g., madmpy library), trustworthy AI practices, and innovative applications like quantum code analysis. She frequently publishes in data science, cybersecurity, and health informatics domains. Labs/Teams: As part of the I4 group, she collaborates on projects like decentralized persistent identifiers and ontology-driven educational frameworks. Her work often involves cross-disciplinary teams addressing societal challenges through technological innovation.

• Data Science
• Machine Learning
• Health Informatics
• +۷ مورد دیگر

Ivan Olier-Caparroso is a Senior Lecturer in Data Science within the Department of Applied Mathematics at Liverpool John Moores University, United Kingdom. He has previously held academic and research positions at Keele University and the University of Manchester, and completed a postdoctoral fellowship at Universitat Autònoma de Barcelona. His academic foundation includes a PhD and MSc in Computer Science from Universitat Politècnica de Catalunya. Current Affiliation: Liverpool John Moores University, Department of Applied Mathematics, School of Engineering and Technology Previous Positions: Lecturer in Health Informatics (Keele University), Research Associate (University of Manchester), EU Marie Curie Fellow, Postdoctoral Researcher (Universitat Autònoma de Barcelona) Education: PhD and MSc in Computer Science, Universitat Politècnica de Catalunya Dr. Olier’s research centers on the application of data science and machine learning to healthcare challenges, particularly in cardiovascular disease, critical care, and mental health. His work emphasizes predictive modeling, explainable AI, digital twin technologies, and clinical decision support systems. He frequently applies these methods to atrial fibrillation, stroke risk, sepsis, and pneumonia severity. His interdisciplinary approach bridges computer science, statistics, and clinical medicine. His recent publications (2023–2025) reveal a strong trend in developing and validating machine learning models for clinical risk prediction, patient phenotyping, and digital health solutions. Notably, he contributes to large-scale European initiatives such as the TARGET consortium, focusing on virtual twin technologies for stroke management in atrial fibrillation. His work spans high-impact journals in cardiology, medical informatics, and data science. His scientific contributions include advancements in: Machine learning for cardiovascular phenotyping and risk stratification Explainable AI in clinical settings Digital twin applications in personalized medicine Mental health assessment using network analysis AI in sports cardiology and oncology Dr. Olier actively supervises PhD students, including V Rajapura Sreedhar, R Bellfield, and D Mistry, and collaborates extensively with researchers such as Sandra Ortega-Martorell and Gregory Y.H. Lip. He has contributed to research funded through international collaborations and consortia, though specific grants are not detailed in the provided text. He does not appear to lead a named lab but is embedded in a research-active group at Liverpool John Moores University focused on applied data science in health.

Rob Beadlingمشاهده پروفایل

مدرس

• Tribology
• Corrosion
• Biomaterials
• +۳ مورد دیگر

Dr. Rob Beadling is a Lecturer in Mechanical Engineering at the School of Mechanical Engineering, University of Leeds, Faculty of Engineering and Physical Sciences. His research focuses on tribology, corrosion, and biomaterials, with applications in total joint replacement. His research interests include: Tribology and wear mechanisms in biomedical implants Corrosion behavior of engineering materials Surface functionalization and engineering for improved performance Biomaterials for orthopedic applications His work is associated with the Institute of Functional Surfaces, a leading research group in surface engineering and tribology. He currently supervises postgraduate researchers, including Rob Elkington. Dr. Beadling holds a PhD and MEng (Hons) in Mechanical Engineering from the University of Leeds and is a professional member of IOM3, IMechE, and NACE. While specific publications and awards are not listed in the provided text, his ongoing research suggests active engagement in the field of mechanical and biomedical materials engineering. Professional Memberships: Institute of Materials, Minerals and Mining (IOM3) Institution of Mechanical Engineers (IMechE) National Association of Corrosion Engineers (NACE) He is involved in postgraduate research supervision and welcomes PhD enquiries from qualified candidates worldwide. His work contributes to advancing the durability and performance of engineering materials, particularly in medical applications.

• Social Network Analysis
• Natural Language Processing
• Data Science
• +۶ مورد دیگر

Prof. Dr. Jana Diesner holds the Human-Centered Computing professorship at the TUM School of Social Sciences and Technology, Technical University of Munich. Her research focuses on computational social science, network analysis, and ethical data practices. She explores topics such as gender bias in AI, crisis communication networks, and the societal impact of scientific research. Her work bridges computer science and social sciences, addressing challenges in natural language processing, data ethics, and interdisciplinary collaboration. Notable areas include analyzing coauthorship networks, detecting biases in large language models, and improving disaster response through network data. Recent publications highlight her contributions to understanding structural balance in signed networks, cross-lingual alignment for LLM evaluation, and the societal impact of documentaries. She emphasizes methodological rigor in computational impact detection and ethical considerations in data usage.

• Topological Data Analysis
• Dynamical Systems
• Network Science
• +۳ مورد دیگر

Sarah Tymochko is a Hedrick Assistant Adjunct Professor in the Department of Mathematics at the University of California, Los Angeles (UCLA), where she collaborates with Mason Porter. She earned her Ph.D. in Computational Mathematics, Science, and Engineering (CMSE) from Michigan State University under the guidance of Liz Munch. Her research focuses on topological data analysis, dynamical systems, network science, and opinion dynamics. She is set to join the College of the Holy Cross as an assistant professor in Fall 2025 and is actively involved in interdisciplinary projects, including the WiSDM workshop at UNC Chapel Hill in August 2025. Her work emphasizes applying topological methods to diverse datasets, such as time series analysis, image resolution impacts, and emergency resource evaluation. She has developed the TeaspoonTDA Python package, published in the Journal of Open Source Software, which provides tools for topological signal processing. Her research spans applications in climate science (hurricane diurnal cycles), healthcare (sleep stage classification), and manufacturing (chatter diagnosis in milling).

Heba El-Fiqiمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Swarm Intelligence
• +۷ مورد دیگر

Dr. Heba El-Fiqi is a Lecturer at the School of Engineering and Information Technology (SEIT), UNSW Canberra. Her research focuses on artificial intelligence, swarm intelligence, machine learning, and computational linguistics. Notable contributions include a novel AI-based solution for air traffic control using swarm robotics and the development of the Weighted Gate Layer Autoencoders (WGLAE) for EEG signal recovery. She holds an IEEE active membership, co-chaired Women in Artificial Intelligence (WAI) activities, and served on conference program committees (AAAI, IJCAI, ECAI). Education: PhD in Computer Science (2013): UNSW, Thesis: "Detection of Translator Stylometry using Pair-wise Comparative Classification and Network Motif Mining" Master of Computer Science (2009): Cairo University, Thesis: "An Intelligent System for Tracking Network Attacks" Research Interests: Swarm robotics, autonomous systems, and AI for air traffic management Machine learning applications in signal recovery and medical diagnostics Computational linguistics and translator stylometry Awards: Dell EMC Award of Distinction (2017) IBM Big Data Developer - Instructor Award for Educators (2017) Teaching & Supervision: Course Coordinator for ZEIT4150 (Artificial Intelligence) and ZEIT4151 (Machine Learning) Supervised over 10 undergraduate engineering projects, including work on deep reinforcement learning for autonomous combat and humanoid robot navigation Labs & Teams: Active in UNSW's AI research community, contributing to initiatives like Canberra AI Week and Women in Engineering (WIE) leadership roles.

Elisabeth BRUNETمشاهده پروفایل

مدرس

• Parallel Computing
• High-Performance Computing
• Computer Architecture
• +۳ مورد دیگر

Elisabeth BRUNET is a Lecturer at Telecom SudParis, part of the Institut Mines-Télécom. Her research focuses on parallel computing, high-performance computing systems, and communication optimization. She has contributed to projects involving GPU porting, MPI communication protocols, and fault-tolerant checkpointing strategies. Her interdisciplinary work extends to biophysics and stochastic processes, with notable publications in journals like Physical Review Fluids and Scientific Reports . She has also explored applications in sports biomechanics and medical imaging analysis. Key collaborations include work on NewMadeleine communication libraries and unified checkpointing models for extreme-scale systems. Her technical contributions span across both theoretical and applied domains, addressing challenges in distributed computing architectures and performance optimization. Education: While specific academic degrees are not detailed in the provided text, her extensive publication record indicates advanced expertise in computer science and mathematics. Research Interests: Parallel computing architectures, stochastic processes in biological systems, high-performance communication networks, and GPU-accelerated algorithms. Awards: No specific awards are mentioned in the text, though her contributions have been recognized through collaborative projects and peer-reviewed publications.

Yifu Liمشاهده پروفایل

استادیار

• Data-driven modeling
• Graph AI
• Natural Language Processing
• +۶ مورد دیگر

Yifu Li is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Oklahoma's College of Engineering. His research focuses on integrating data-driven modeling techniques like spectral theory-based graph AI with multi-modality datasets in smart manufacturing, cybersecurity, and healthcare. His work emphasizes solutions for large-scale data analysis with enhanced performance and interpretability. Education: Ph.D., Industrial and Systems Engineering, Virginia Tech B.S., Industrial and Systems Engineering, Virginia Tech Research Interests: Li's domains include data-driven multi-modality modeling, spectral graph AI, and natural language processing applied to aerospace systems, healthcare systems, and industrial engineering challenges. His federally funded projects involve NSF, USDA, DARPA, and DoD grants. Key Grants: National Science Foundation (NSF) USDA Defense Advanced Research Projects Agency (DARPA) Department of Defense (DoD) Labs/Teams: Active in the Industrial & Systems Engineering department's research initiatives, with a lab webpage documenting ongoing projects.

Yang Zhangمشاهده پروفایل

استادیار بالینی

• Artificial Intelligence
• Natural Language Processing
• Recommender Systems
• +۷ مورد دیگر

Dr. Yang Zhang is a Clinical Assistant Professor in Data Science at the University of North Texas (UNT) and holds an Honorary Lecturer position at Macquarie University, Australia. He specializes in interdisciplinary AI applications, including fraud detection, brain-computer interfaces, and carbon emission forecasting. His research bridges natural language processing, recommender systems, and AIoT technologies. Education: PhD in Computer Science (Macquarie University, 2023) and PhD in Information Science (Wuhan University, 2024). Recipient of the Vice-Chancellor’s Commendation for his thesis. Extensive teaching experience includes courses on data science and AI, with roles as a Course Examiner and Sessional Lecturer. Research focuses on AI-driven solutions for complex challenges, including fraud detection via hypergraph learning, brain-computer interface advancements using geometric learning, and scalable time series forecasting for climate systems. Recent work includes contributions to blockchain consensus algorithms and carbon emission modeling. Professional Activities: Organized special issues on 'Generative AI and Computational Linguistics' and 'Text Mining and Analytics', and co-chaired workshops at IEEE ICDM and ADMA. Served as a Senior PC member at ADMA2024 and PC member at ACM Multimedia 2025 and WWW2025. Labs: Leads the IDEA Lab (Interdisciplinary Data & AI Exploration Lab), focusing on cutting-edge AI applications in interdisciplinary domains.

Shanghua Gaoمشاهده پروفایل

پژوهشگر ارشد

• Foundation Models
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Shanghua Gao serves as a Research Fellow at Harvard Medical School's Department of Biomedical Informatics, Harvard University, where he develops foundation models and universal representation learning methods to advance artificial general intelligence across computer vision, biomedical applications, and time series analysis. His research focuses on creating generalizable AI techniques including AI agents, generative modeling, and self-supervised learning frameworks. Gao emphasizes resource-efficient architectures that maintain high performance while minimizing computational demands, with notable contributions in salient object detection and biomedical AI systems. His work bridges theoretical innovation and practical deployment, with several projects transitioning into real-world applications. Analysis of his recent publications reveals a strategic shift toward foundation models for biomedical reasoning (e.g., TxAgent for therapeutic applications) and unified time-series frameworks (UniTS), while maintaining expertise in efficient computer vision systems. His research consistently prioritizes model generalizability across domains, evident in cross-disciplinary applications from medical diagnostics to creative AI systems.

Giovanna Maria Dimitriمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۷ مورد دیگر

Giovanna Maria Dimitri is a tenure-track Assistant Professor in Artificial Intelligence at the Università degli Studi di Milano (Statale), with strong affiliations at the University of Siena (DIISM) and the University of Cambridge. She conducts research in deep learning, machine learning, and AI applications across domains such as computer vision, bioinformatics, climate modeling, and healthcare. She also lectures in Business Intelligence at the University of Siena and serves as a Guest Lecturer in Data Science at the University of Cambridge's Institute of Continuing Education. PhD in Artificial Intelligence, University of Cambridge MPhil in Advanced Computer Science (Distinction), University of Cambridge Master's and Bachelor's in Computer and Automation Engineering (110/110 cum laude), University of Siena Her research spans foundational AI models and their applications in diverse fields including neuroscience, environmental science, and medical diagnostics. She develops methodologies in deep learning, graph neural networks, and multimodal data analysis, with a growing interest in sustainable AI and ethical implications of machine learning systems. Her recent publications reflect a strong trend in applying AI to real-world challenges: from detecting synthetic images and analyzing brain signals to modeling climate impacts and assessing sustainable development goals. Her work bridges theoretical innovation with practical implementation across healthcare, environmental monitoring, and digital humanities. Scientific Awards and Editorial Roles: Ai-Net Fellows Scholarship (DAAD, 2023) Associate Editor, Neurocomputing (Elsevier) Associate Editor, IEEE Transactions on Technology and Society (since May 2024) Giovanna has extensive experience in teaching, supervising, and science communication. She has delivered seminars at top institutions like the University of Cambridge and has been featured in Italian media for her AI expertise. She mentors students and collaborates internationally, including with Prof. Gemma Roig at the DAAD-sponsored Ai-Net program. She is actively involved in academic service, conference organizing, and public engagement, contributing to both technical and societal aspects of AI advancement. She is a life member of Clare Hall College, University of Cambridge, and maintains active research collaborations across Europe. Her lab and research group focus on developing robust, interpretable, and sustainable AI systems, with ongoing projects in medical AI, climate informatics, and ethical machine learning.

Mai Oudahمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۱۰ مورد دیگر

Mai Oudah is an Assistant Lecturer in the Computer Science program at New York University Abu Dhabi (NYUAD), part of the Division of Science. She holds a PhD in Interdisciplinary Engineering (Computing and Information Science – Bioinformatics) from Masdar Institute in collaboration with MIT, an MSc in Informatics (Knowledge and Data Management) from The British University in Dubai (in association with the University of Edinburgh), and a BSc in Software Engineering from Al Ain University. Her research lies at the intersection of artificial intelligence, bioinformatics, and natural language processing. She leads the Computational Biology and Bioinformatics Lab at NYUAD, where her team develops machine learning models for disease screening using gut microbiome data, including applications for Alzheimer’s, depression, autism, multiple sclerosis, and various cancers. She also works on biomedical text mining and Arabic natural language processing, particularly in named entity recognition and machine translation. The recent publications reflect a strong trend in applying machine learning to microbiome-based diagnostics and health informatics, with additional contributions to Arabic NLP and sociological text analysis. Her work spans interdisciplinary domains, combining computational methods with biological and medical insights. Scientific Awards: No scientific awards explicitly mentioned in the provided texts. Advising and Grants: Mai Oudah actively supervises undergraduate research students in her lab, guiding projects on disease detection, microbiome analysis, and AI applications in healthcare. She has advised numerous students who have contributed to peer-reviewed publications. While no specific grants are listed, her collaborative research with institutions like MIT and NYUAD suggests active involvement in funded research initiatives. Labs and Teams: She leads the Computational Biology and Bioinformatics Lab at NYUAD, which focuses on three core areas: (A) Comparative Genomics/Metagenomics and Metatranscriptomics, (B) Biomedical Text Mining, and (C) Healthcare Research. The lab integrates AI, ML, and data analysis to address challenges in computational biology and public health.

• Comparative Neuropathology
• Ophthalmic Pathology
• Non-Human Primate Pathology
• +۱۳ مورد دیگر

Caroline Zeiss is a Professor of Comparative Medicine and Ophthalmology and Visual Science at Yale School of Medicine, where she also serves as Chief of Pathology in the Department of Comparative Medicine. She co-directs the Alzheimer's Disease Research Center at Yale and previously led the Pathology Research Core from 2005 to 2019. Her work bridges veterinary pathology, neuroscience, and translational medicine. University: Yale University School: Yale School of Medicine Department: Department of Comparative Medicine Joint Appointment: Department of Ophthalmology and Visual Science Leadership: Chief of Pathology, Co-Director, Alzheimer's Disease Research Center Dr. Zeiss earned her veterinary degree from the University of Pretoria, completed an internship at the University of Pennsylvania, and earned her PhD and completed residency training at Cornell University. She is board certified in Veterinary Anatomic Pathology and Laboratory Animal Medicine. BVSc, University of Pretoria, 1990 Internship in Medicine and Surgery, University of Pennsylvania, 1991 Residency in Anatomic Pathology, Cornell University, 1995 PhD, Cornell University, 1999 Fellowship, National Institutes of Health, 2015 Her research centers on improving the translation of animal studies to human therapies, particularly in neurodegenerative diseases like Alzheimer’s and Parkinson’s. She utilizes non-human primate and rodent models, integrating neuropathology, ophthalmic pathology, and biomedical informatics. Her recent work includes modeling the endemic transition of SARS-CoV-2 and studying cross-protection between influenza and COVID-19 in hamsters. She has published extensively on ocular diseases in animals and mechanisms of retinal degeneration. The 15 most recent publications reflect a strong trajectory in Alzheimer’s disease research, with increasing use of informatics, primate models, and network analysis. Themes include biomarker discovery, chemical entity normalization for drug development, and modeling infectious disease dynamics. There is a clear interdisciplinary trend combining pathology, neuroscience, genomics, and computational methods. Dr. Zeiss has received several honors for her contributions: Master of Arts Privatum (honorary), Yale University, 2012 Charles Louis Davis Award, 2006 Arnold Theiler Award, 1990 She has been actively involved in academic governance and education, serving as Chair of the American Veterinary Medical Association Council on Education and as a member of the Institute for Laboratory Animal Research at the National Academy of Sciences. She teaches a graduate course in comparative neuroanatomy and mentors students and postdocs. Her grants include funding for Alzheimer’s research, COVID-19 modeling, and ophthalmic pathology studies, often in collaboration with industry and academic partners. She has led NIH-funded projects and contributed to major center grants such as the Alzheimer's Disease Research Center. Dr. Zeiss leads or is a key member of several research teams, including the Pathology Research Core (2005–2019), the Alzheimer's Disease Research Center, and interdisciplinary teams focusing on neurodegeneration, infectious disease modeling, and ophthalmic therapeutics. Her lab has specialized in comparative pathology, particularly of the nervous system and eyes, using advanced techniques in histopathology, molecular analysis, and informatics.