جستجوی استاد راهنمای دانشگاهی

• Natural Language Processing
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Dr. Vimal Kumar Kumar serves as a Research Fellow at the University of Limerick's Department of Computer Science and Information Systems, affiliated with Lero – the Irish Software Research Centre. His academic career includes prior faculty positions as Assistant Professor at Jaypee University of Information Technology (2009-2022) and Lecturer at SRM Institute of Science and Technology (2007-2009), followed by a Postdoctoral Researcher role at University College Dublin (2022-2023). His educational background includes: PhD in A Novel sense based Hindi to Tamil machine translation system from Jaypee Institute of Information Technology (2019) Masters from Anna University Chennai (2005-2007) Bachelor from PSG College of Technology (2000-2004) Dr. Kumar's research centers on Natural Language Processing with specialized focus on machine translation for Indian languages (Hindi, Tamil), text summarization, and sentiment analysis. He extends this work into computer vision applications for medical imaging and activity recognition, plus deep learning implementations for IoT-based agricultural monitoring. His methodology frequently addresses challenges in low-resource language processing and develops optimized neural network architectures for mobile deployment. Analysis of his publication trends reveals consistent interdisciplinary innovation, particularly in healthcare applications (retinal disease detection), agricultural technology (crop yield prediction), and social media analytics. His work demonstrates strong emphasis on practical AI solutions for resource-constrained environments and underrepresented linguistic communities, often leveraging generative models and contextual embeddings. Dr. Kumar actively contributes to Lero's research initiatives, aligning his technical work with UN Sustainable Development Goals through software solutions addressing global challenges in health, food security, and digital inclusion.

Ritu Chaturvediمشاهده پروفایل

دانشیار

• Data Mining
• Predictive Modeling
• Educational Data Mining
• +۵ مورد دیگر

Ritu Chaturvedi is an Associate Professor at the School of Computer Science, University of Guelph. Her research focuses on data mining and predictive modeling with applications in educational technology, e-commerce, and social media analysis. She holds a faculty position emphasizing interdisciplinary approaches to computational challenges in learning systems and digital environments. Her research interests span Intelligent Tutoring Systems (ITS) Educational Data Mining Sentiment Analysis Recommendation Systems Natural Language Processing Machine Learning Methodologies Recent work includes developing algorithms for personalized learning systems, analyzing social media sentiment during global health crises, and enhancing cross-platform e-commerce recommendations. Her publications demonstrate expertise in data-driven solutions for instructional design, consumer behavior modeling, and privacy policy optimization. Advising focuses on graduate students exploring adaptive learning technologies and data integration strategies. Her lab's projects often involve open-source implementations of recommendation engines and educational analytics frameworks.

John Nordinمشاهده پروفایل

استاد مدعو

• Argumentation Theory
• Critical Thinking
• Public Speaking
• +۶ مورد دیگر

John Nordin is an Adjunct Professor in the Department of Communication Studies at the University of Minnesota, where he has taught for 16 years. His academic work spans Argumentation theory Critical thinking pedagogy Public speaking Ancient and modern Greek studies Biblical studies Project management . He holds a Ph.D. in Transportation Systems from MIT and an M.Div. from Pacific Lutheran Theological Seminary. His research interests focus on interdisciplinary connections between communication, religion, and technology. He has published analytical work on Iranian politics, theological treatises on worship practices, and technical papers on computer graphics and database design. His personal website maintains resources on Greece, including the Plaka website Philhellenic Perspective newsletter . Teaching is enriched by global experience: Led study abroad programs in Greece Worked as a Lutheran pastor in Iowa and Colorado Teaching Computer Science at University of Nairobi Project management in IT . He integrates travel, cultural analysis, and technical expertise into his courses. Publications include: Co-authored textbook on critical thinking Articles on Iranian politics Technical papers on sulfur emissions and airport operations Theological works on hymnody and worship reform . His creative writing includes a historical novel about ancient Greece and poetry.

Dongsheng Luoمشاهده پروفایل

استادیار

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

Dr. Dongsheng Luo is an Assistant Professor at the Knight Foundation School of Computing and Information Sciences at Florida International University (FIU). He holds a Ph.D. in Information Science and Technology from Pennsylvania State University (2022) and a B.Eng. in Computer Science and Technology from Beihang University (2017). His research focuses on developing interpretable AI tools for complex data analysis, particularly in graph mining, deep learning, and natural language processing, with applications in neuroscience, healthcare, and environmental systems. Education: 2022: Ph.D., Penn State University 2017: B.Eng., Beihang University His research interests emphasize explainable AI, robust graph neural networks, and multimodal integration. Recent work includes frameworks for medical plan generation, causal discovery, and flood forecasting. He investigates methods to enhance model reliability and transparency, such as confidence-aware explanations and fidelity evaluation. Collaborations span biomedical informatics, climate science, and IoT systems. Publications highlight contributions to graph explainability, time series analysis, and molecular modeling. His work bridges theoretical advances with practical applications in healthcare, environmental monitoring, and communication systems.

Denilson Barbosaمشاهده پروفایل

استاد

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +۳ مورد دیگر

Denilson Barbosa is a Professor of Computing Science at the University of Alberta's Faculty of Science. He holds a PhD and specializes in Natural Language Processing, Information Retrieval, and Machine Learning. His research focuses on knowledge extraction, entity recognition, and knowledge graph construction. Barbosa teaches courses like CMPUT361 (Information Retrieval) and CMPUT656 (Knowledge Graphs). He served as President of the Canadian Artificial Intelligence Association (CAIAC) from 2023 to 2024. His work includes developing tools like the NLP Workbench and contributing to open relation extraction, Wikipedia analysis, and low-resource language models. Education and Background: Barbosa's academic background aligns with his research interests in computational linguistics and AI applications. His teaching emphasizes practical skills in algorithms, data structures, and machine learning for text processing. Research Highlights: Barbosa's research spans semantic graphs, probabilistic language models, and defect prediction systems. He explores challenges in multilingual NLP, particularly for Indigenous and low-resource languages like Cree. His work often integrates machine learning with traditional IR methods to improve system efficiency and accuracy. Recent Contributions: Notable recent projects include improving Bengali/Hindi LLMs, evaluating QA systems via textual entailment, and developing Bayesian methods for crowdsourced truth discovery. His articles reflect his interdisciplinary approach, combining theory with real-world applications in education, software engineering, and social media analysis.

• Statistical learning in high dimensions
• Statistical learning on networks
• Functional data analysis
• +۱۲ مورد دیگر

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur, Nice, France. He holds a Chair in Artificial Intelligence and serves as Director of the Institut 3IA Côte d'Azur, while also leading the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. Université Côte d'Azur: Full Professor of Statistics (2019-present) Institut 3IA Côte d'Azur: Director (2019-present) Inria: MAASAI Team Leader (2019-present) His research focuses on statistical learning methodologies for high-dimensional data, network analysis, functional data modeling, and deep latent variable approaches. Key applications include medical diagnosis through radiomics, ecological monitoring, astrophysics data analysis, and humanities data science. Recent publications emphasize dynamic co-clustering techniques for count data streams, deep latent position models for network analysis, and discriminative clustering frameworks using generalized mutual information. His work integrates Bayesian sparsity, probabilistic PCA, and robust classification methods. Software development remains a core activity with tools like HDclassif, FunLBM, and Linkage.fr platform for network analysis with textual edges. He has mentored over 20 PhD/postdoc researchers in statistical learning and AI applications.

• Computer Science
• Information Systems
• Data Analytics
• +۳ مورد دیگر

Shamantha YAN Shiya is a Part-Time Lecturer at NUS Computing , National University of Singapore. Affiliated with the School of Computing, she contributes to academic programs in computer science and information systems. Her research interests align with broad areas of Computer Science and Information Systems , particularly in Data Analytics , Software Engineering , and Human-Computer Interaction . While no specific publications or awards are listed in the provided text, her role focuses on teaching and academic engagement within the computing disciplines.

Teresa Dobsonمشاهده پروفایل

استاد

• Digital Literacy
• Literary Education
• Critical Literacies
• +۴ مورد دیگر

Dr. Teresa Dobson is a Professor in the Department of Language and Literacy Education at the University of British Columbia's Faculty of Education. Her research bridges literary education, digital literacy, and digital humanities, with a focus on developing innovative browsing interfaces for text visualization through interdisciplinary collaborations. Her research explores critical digital literacies, multimodal storytelling, and pedagogical applications of digital tools. Primary interests include digital research methodologies, online pedagogies, place-based literacies, and narrative inquiry in teacher education contexts. Dobson's publications demonstrate consistent focus on digital literacy development, educational technology integration, and visual methodologies in humanities research. Recent work examines observational modeling, visual literacy, and digital book production for early literacy across cultural contexts.

Mehrdad Oveisiمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Medical Imaging
• +۳ مورد دیگر

Mehrdad Oveisi is a Lecturer in the Department of Computer Science at the University of British Columbia (UBC), part of the Faculty of Science. His teaching focuses on courses such as Software Construction (CPSC 210), Introduction to Artificial Intelligence (CPSC 322), and Applied Machine Learning (CPSC 330), demonstrating expertise in foundational and advanced topics in computer science and AI. His research interests center on the application of machine learning and artificial intelligence in medical imaging and healthcare. Key areas include radiomics (analysis of high-dimensional imaging features), development of standardized evaluation frameworks for machine learning models, and federated learning with privacy-preserving techniques. His work frequently involves collaboration with medical institutions to address challenges in oncology, cardiology, and diagnostic imaging. Notable projects include the development of tools like ViSERA for reproducible radiomics workflows and studies on harmonizing imaging features across different modalities and institutions to improve reproducibility. His contributions span both methodological advancements (e.g., AllMetrics library) and clinical applications (e.g., survival prediction in cancer patients using MRI radiomics). While no specific grants or awards are listed, his extensive publication record reflects active engagement in interdisciplinary research at the intersection of computer science and healthcare. Office hours are held at ICCS 141, and professional links include LinkedIn.

• Machine Learning
• Public Health
• Psychiatry
• +۴ مورد دیگر

Andy Man Yeung Tai is a Postdoctoral Research and Teaching Fellow at the Department of Statistics, University of British Columbia. He focuses on interdisciplinary research at the intersection of machine learning, statistics, and healthcare, particularly addressing public health challenges like the opioid overdose crisis. His work also explores e-health technologies, medical education, and ethical implications of AI in academic writing. Research Interests: Andy’s research spans machine learning applications in disease modeling and risk management, public health policy, mental health care innovations, and addiction medicine. He investigates systematic approaches to healthcare interventions, leveraging big data and web-based platforms. His recent studies include analyzing Dark Web drug trade trends and evaluating medical students' perspectives on psychiatry across different cultures. Articles Trends: His publications emphasize evidence-based strategies for the overdose crisis, clinical decision support systems for addiction, and digital health solutions. Andy has consistently contributed to understanding opioid-related risks and improving adherence to e-health interventions through meta-analyses and systematic reviews. Advising & Grants: No formal advisees or grant details are listed in the provided texts. His collaborative efforts include co-authoring studies with researchers like Alcides Albuquerque and Roger S. McIntyre, focusing on therapeutic discovery in psychiatry. Awards: No scientific awards are explicitly mentioned in the texts. Labs/Teams: While no specific lab or team affiliations are noted, his work suggests involvement in interdisciplinary initiatives combining statistical methodology with health policy and medical education.

Melinda Hodkiewiczمشاهده پروفایل

استاد

• Maintenance Engineering
• Asset Management
• Data Science
• +۶ مورد دیگر

Professor Melinda Hodkiewicz is a leading academic at the University of Western Australia (UWA), affiliated with the School of Engineering's Mechanical Engineering department. Her work focuses on advancing maintenance, asset management, and safety practices through data-driven approaches, particularly in Technical Language Processing (TLP) and industrial ontologies. She has held significant roles such as the BHP Fellow for Engineering for Remote Operations (2015–2022) and leadership in the $8.8M Australian Government-funded Training Centre for Transforming Maintenance through Data Science. Research interests include applying TLP to maintenance and safety texts, ontology development for industrial interoperability, and advancing standards like ISO 23726. Awards include the MESA Medal (Lifetime Achievement in Asset Management) and Fellow of the Australian Academy of Technology and Engineering (ATSE). She has contributed to international standards (ISO 55000 series) and served on boards of organizations like NOPSEMA and MRIWA. Her work aligns with UN SDG 4 (Quality Education) and SDG 9 (Industry, Innovation & Infrastructure). Key contributions include developing the Industrial Ontology Foundry's Maintenance Working Group, creating open-source tools like OpenBoltRF for wind turbine analysis, and advancing semantic quality assurance frameworks for maintenance procedures. Her research bridges technical language processing with industrial applications, enhancing safety and operational efficiency in sectors like mining, energy, and offshore engineering. Education: BA(Hons) in Metallurgy (University of Oxford, 1985), PhD in Engineering (UWA, 2014) Grants: ARC Training Centre for Transforming Maintenance (2019–2025), ARC Hub for Offshore Floating Facilities (2014–2021) Advisory Roles: Standards Australia EL-069 (Automation Systems), ISO TC184/SC4 (Industrial Data)

Amir H. Razaviمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing (NLP)
• Cybersecurity
• +۳ مورد دیگر

Amir H. Razavi is a part-time Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a PhD in Machine Learning and NLP from the same institution (2012) and a Master's in Computer Software Engineering from the University of Tehran. His career spans over a decade of industry experience as a senior data scientist and team lead in Big Data analytics across cybersecurity, telecommunications, and financial sectors with companies like Bell Canada and BlackBerry. His research focuses on applying ML and NLP to cybersecurity (e.g., threat detection, DLP), behavioral analysis, fraud detection, and maritime risk mining. His academic contributions include over 25 peer-reviewed publications in venues like IEEE conferences and journals, with a focus on DNS-based cybersecurity, social network analysis, and NLP applications in healthcare and psychology. He has also managed large-scale enterprise projects in fraud management and customer care within telecommunications. Education: PhD in Machine Learning & NLP, University of Ottawa (2012) MSc in Computer Software Engineering, University of Tehran Research interests span: • ML-driven cybersecurity solutions (e.g., DNS exfiltration detection) • NLP applications in healthcare diagnostics and dream analysis • Privacy-preserving data models and risk assessment frameworks • Hybrid machine learning systems for adaptive learning and recommendation engines Labs/Teams: Collaborations include BlackBerry and Bell Canada on CTI projects, and interdisciplinary work with psychologists on dream sentiment analysis. Active contributor to ResearchGate with 17,304 reads and 1,668 citations.

Rosa Filgueiraمشاهده پروفایل

مدرس

• Distributed Systems
• Data-Streaming Optimizations
• Serverless Computing
• +۱۱ مورد دیگر

Rosa Filgueira is a Lecturer at the School of Computer Science, University of St Andrews, and Group Leader of the Systems Research Group (SRG). She holds an Honorary Research Fellow title and is a member of the UK Young Academy. Her career includes roles as Assistant Professor at Heriot-Watt University, Vice President Applied Researcher at JPMorgan Chase, and Senior Data Scientist at the British Geological Survey. She earned her BSc and MSc from the University of Deusto (Spain) and a PhD from University Carlos III Madrid (Spain). Her research focuses on advanced information processing technologies to accelerate scientific discovery, emphasizing distributed systems, data-streaming optimizations, and reproducible frameworks. Key areas include serverless computing, adaptive communication techniques, and AI-driven software analysis. She teaches CS3031 Databases (undergraduate) and CS5020 Principles of Computer Communication Systems (postgraduate), supervising multiple student projects. Rosa’s work addresses challenges in health, earth sciences, and digital humanities. Notable projects include cultural analytics frameworks for the creative industries and AI tools for historical text mining. She leads initiatives like the Laminar serverless framework and the frances NLP tool for heritage texts. Her awards include the UK Young Academy membership and National Librarian's Research Fellowship. Current grants include the British Academy-funded Decoding Democracy project. She actively collaborates internationally, contributing to IEEE eScience conferences and open-source projects like dispel4py and Inspect4py. Labs/Teams: Systems Research Group (SRG), leading interdisciplinary collaborations in HPC and data science. Her work bridges computer science with domain-specific applications, emphasizing real-world impact through reproducible software solutions.

Steve Cassidyمشاهده پروفایل

دانشیار

• Research Data Management
• Linguistic Annotation Standards
• Speech Analysis Technology
• +۳ مورد دیگر

Steve Cassidy is an Associate Professor in the School of Computing at Macquarie University. His research focuses on research data discovery, reproducibility, and accessible tools for language sciences. He specializes in linguistic annotation standards, speech analysis technologies, and interoperability infrastructure for human communication science. He holds a PhD in Computer Science from Victoria University of Wellington, an MSc in Knowledge-Based Systems from the University of Edinburgh, and a BSc in Physics with Astrophysics from the University of Leicester. He leads major projects such as FAIMS3 (Field Acquired Information Management Systems) and the Alveo Virtual Laboratory, which support digital data capture and management in diverse research contexts. His work emphasizes FAIR data principles and interdisciplinary collaboration, with applications in linguistics, archaeology, and speech technology. He has contributed to initiatives like the AusTalk corpus and Signbank, advancing open science practices and accessibility in research. Recent research highlights include machine learning models for military performance prediction and temporally rich deep learning approaches for magnetoencephalography analysis. His work has been recognized through awards including the 2024 Teaching Excellence Highly Commendation and the 2023 Inter-School Collaboration Award. Education: PhD in Computer Science (Victoria University of Wellington, 1993) MSc in Knowledge-Based Systems (University of Edinburgh, 1984) BSc in Physics with Astrophysics (University of Leicester, 1983) Postgraduate Certificate in Higher Education (Macquarie University, 2008) Key Projects: FAIMS3: Mobile field data collection platform Alveo Virtual Lab: Linguistic data repository AusTalk Corpus: Large-scale Australian English speech database His publications span machine learning applications, speech technology, and open research infrastructure. He actively collaborates across disciplines to develop tools that enhance reproducibility and accessibility in scientific research.

Xuyun Zhangمشاهده پروفایل

دانشیار

• Scalable Machine Learning
• Big Data Privacy
• Cyber Security
• +۵ مورد دیگر

Dr. Xuyun Zhang is an Associate Professor at the School of Computing, Macquarie University, Australia. He holds a PhD from University of Technology Sydney (2014), and Master's/Bachelor's degrees from Nanjing University (China). His research focuses on scalable machine learning, big data privacy, cyber security, edge computing, and IoT. He has led 11 research projects including Next-Gen Banking (2024-2028) and Scalable Document Intelligence (2022-2027). His work spans secure AI, privacy-preserving techniques, and edge-cloud collaboration. He has received awards such as the Chancellor's Best PhD Thesis (2015) and Early-Career Research Excellence (2019). His recent publications address areas like metaverse service offloading, hypergraph trust prediction, and federated learning frameworks. He actively organizes conferences like the Australasian Information Security Conference (2017) and chairs industry-research initiatives in cloud computing and mobile networking. Education: PhD (UTS, 2014), MEng (Nanjing University, 2011), BSc (Nanjing University, 2008). Teaching includes Machine Learning in Cybersecurity, Data Science, and Software Engineering. Key research contributions include privacy-preserving publishing, edge computing offloading methods, and blockchain-based secure access management. His projects often integrate AI with cloud-edge systems to address real-world challenges in finance, healthcare, and smart manufacturing.