Find Academic Supervisors

• Machine Learning
• Big Data Analytics
• Computational Intelligence
• +4 more

Vahid Behbood is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He belongs to the Decision Systems and e-Service Intelligence Research Laboratory in the Centre for Quantum Computation and Intelligent Systems. Ph.D. in Software Engineering from UTS (2014) His research spans machine learning , big data analytics , computational intelligence , and transfer learning , with a focus on addressing data shortage challenges through fuzzy systems and domain adaptation. His work includes: Advancing fuzzy regression domain adaptation for cross-domain prediction Developing granular computing techniques for financial failure prediction Exploring SQL query error patterns in computer science education Designing IoT architectural frameworks via systematic reviews Publications demonstrate expertise in neural networks , fuzzy logic , Bayesian methods , and financial data analytics . He applies these techniques to banking ecosystems, real estate valuation, and educational data mining.

• Neuroimaging
• Artificial Intelligence
• Medical Imaging
• +5 more

Mariano Cabezas Grebol is a researcher affiliated with the Brain and Mind Centre at the University of Sydney . He earned a BSc in Computer Science (2009) and an MSc in Automation, Computation, and Systems (2010) from the University of Girona , followed by a PhD in Technology (2013) focused on atlas-based segmentation of multiple sclerosis lesions . His research bridges neuroimaging and AI models , with specific interests in image synthesis (GANs) , segmentation (cross-sectional/longitudinal) , classification (disease prediction) , uncertainty metrics , and model interpretability . His work has broad implications for Alzheimer's disease , multiple sclerosis , and brain tumor analysis. Scientific Awards : 2020: Understanding Alzheimer's disease with deep learning and neuroimaging grant from the Brain and Mind Centre. International Collaborations : University of Yamagata, Japan (with Dr. Yago in Data Science). VICOROB group, University of Girona, Spain (long-term relationships). His career spans postdoctoral work at VHIR (Hospital Vall d'Hebron) , a Juan de la Cierva-Incorporación fellowship at NIC-VICOROB, and current leadership in developing AI tools for brain image analysis alongside Prof. Michael Barnett in Sydney.

Petr HájekView profile

Professor

• Machine Learning
• Neural Networks
• Fuzzy Logic
• +7 more

Petr Hájek is a Professor at the University of Pardubice in the Institute of System Engineering and Informatics, Czech Republic. With 236 publications, 71,463 reads, and 5,595 citations, he has established himself as a prominent researcher in computational intelligence and machine learning applications. His research interests span multiple domains of computational intelligence, with particular focus on: Machine learning applications in financial forecasting and risk management Neural networks and fuzzy logic systems for time series prediction Sentiment analysis for financial markets and social media Fraud detection and fake news identification systems Cryptocurrency price forecasting and market analysis ESG analytics and sustainable finance applications Analysis of his recent publications (2023-2025) reveals an expanding research scope that increasingly integrates sustainability considerations with financial technology. His work demonstrates sophisticated methodological approaches, frequently employing hybrid neural network architectures, ensemble learning techniques, and advanced text mining methods. Professor Hájek's research shows strong international collaboration patterns with scholars across Europe, Asia, and North America. His scholarly contributions have focused on developing practical AI solutions for complex financial problems, with particular attention to handling class imbalance issues in financial datasets and creating interpretable models for financial decision-making. Professor Hájek maintains an active research program with consistent publication output across top venues in computational intelligence and financial technology. His work bridges theoretical advances in machine learning with practical applications in finance, demonstrating both academic rigor and real-world relevance.

Clare Chua-ChowView profile

Associate Professor

• Business Education
• Financial Data Analysis
• Statistical Methods
• +2 more

Dr. Clare Chua-Chow serves as Associate Professor in the Department of Global Management Studies at Toronto Metropolitan University's School of Business Management, contributing to both undergraduate and graduate business education programs. Her research spans critical business domains: Business Education Innovation: Investigating student adaptation to online learning during disruptive transitions like the Covid-19 pandemic Financial Data Science: Developing computational approaches for multiclass imbalance challenges in financial datasets Statistical Methodology: Enhancing data explainability through principal component analysis applications Recent publications reveal a consistent focus on practical business applications of data science, with particular attention to educational technology adaptations and financial analytics improvements. Her work bridges theoretical frameworks with real-world business challenges across multiple sectors. Contact Dr. Chua-Chow at cchua@torontomu.ca , office TRS 1-030, or by phone at (416) 979-5000 ext: 6753 for academic collaboration or supervision inquiries.

György KovácsView profile

Senior Lecturer

• Speech and Language Technology
• Automatic Speech Recognition
• Natural Language Processing
• +5 more

György Kovács is a Senior Lecturer at Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, working within the Embedded Intelligent Systems LAB. His primary research focus is on Machine Learning applications, particularly in speech and language technology. His research interests span multiple areas of speech and language processing, including: Automatic Speech Recognition across diverse conditions and languages Paralinguistic Speech Processing for emotion detection and speaker state analysis Audio event classification, including medical applications like cough sound analysis Sentiment analysis in written text, with particular interest in hateful language detection Bot detection in social media analysis Kovács has supervised numerous Master's theses since 2021, with topics ranging from accent classification and sentiment analysis to AI-generated code quality assessment. Many of these supervisions have led to co-authored publications with students. His recent publications demonstrate a strong focus on applying machine learning to diverse domains including remote sensing, healthcare technology, and natural language processing. The research shows a clear pattern of interdisciplinary work connecting machine learning with practical applications across environmental monitoring, healthcare, and social media analysis. His academic contributions include co-supervising PhD students such as Sana Al-Azzawi and Nosheen Abid, whose dissertation work on Unsupervised Curriculum Learning for Earth Observation represents significant contributions to the field. Notable publications include work on cloud detection using synthetic datasets, seagrass classification, and emotion classification using EEG in healthcare settings. Kovács also teaches courses including Introduction to Artificial Intelligence (D0032E) and Machine Learning and Pattern Recognition (D0033E), demonstrating his commitment to both research and education in the field of artificial intelligence.

Soma DattaView profile

Associate Professor

• Agile Software Development
• Applied Machine Learning
• Gamification in Education
• +3 more

Dr. Soma Datta serves as Associate Professor and Program Chair in the Software Engineering department at University of Houston-Clear Lake's College of Science and Engineering. Her academic leadership spans curriculum development, graduate supervision, and innovative teaching methodologies. Dr. Datta's research focuses on three interconnected domains: Agile software development frameworks and hybrid methodologies Applied machine learning for predictive modeling in education and IoT systems Gamification techniques for enhancing software engineering education Her work bridges theoretical research with practical applications through projects like virtual laboratory development for disaster-resilient education and ADHD learning tools. Her publication record from 2017-2020 shows consistent output in agile methodologies (40%), machine learning applications (30%), and educational innovation (30%), with notable contributions to hybrid agile processes and gamified learning environments. Professional recognition includes: Instructional Innovation Award Honorable Mention (2020) IEEE conference best presentation awards (2017, 2019) Featured as 'Professor to Know' in online software engineering education New Faculty Teaching Award (2018) Dr. Datta actively mentors graduate students in agile frameworks and software implementation, emphasizing conference presentation opportunities. She founded UHCL's Code for Girls Camp for middle-schoolers and has taught data science internationally to sixth-graders, demonstrating commitment to STEM pipeline development. Current research includes virtual labs for the MicroCURE network and machine learning applications for student recruitment.

• Artificial Intelligence
• Constraint Satisfaction Problems
• Optimization
• +3 more

Dr. Hatem Ahriz is a Professor at Robert Gordon University's School of Computing, Engineering & Technology, with a distinguished career spanning over 25 years in artificial intelligence and cybersecurity research. His academic journey began with a BSc in Computer Science (1987-1992), followed by an MSc (1993-1994) and PhD in Artificial Intelligence (1994-1998). Dr. Ahriz's research has evolved from constraint satisfaction and optimization problems to cutting-edge cybersecurity applications, with particular expertise in Advanced Persistent Threats (APTs). His work combines artificial intelligence, machine learning, and security frameworks to develop innovative solutions for threat detection and cyber defense systems. His recent publications (2019-2024) show a clear shift toward cybersecurity applications, with multiple papers on APT detection frameworks, machine learning approaches for threat analysis, and cyber physical systems security. This represents a natural progression from his earlier foundational work on distributed constraint satisfaction problems. Active supervision of PhD students in cybersecurity Current research project on AI-powered vulnerability detection systems Teaching expertise in database systems and information security Dr. Ahriz maintains active collaborations with industry partners, as evidenced by his project with CyberShell Solutions to develop a SaaS platform for Cyber Threat Intelligence. His research bridges theoretical computer science with practical applications in critical infrastructure protection and enterprise security.

• Machine Learning
• Computer Vision
• Deep Learning
• +7 more

Professor Eyad Elyan is a leading academic and researcher at Robert Gordon University's School of Computing, Engineering and Technology, where he serves as a Professor in Machine Learning and Computer Vision. He is the founder and head of the Machine Vision Research Group, driving innovative research in applied computer vision and deep learning with significant industry impact. Professor Elyan's research focuses on converting complex and unstructured data into knowledge and actionable insights, with particular emphasis on learning from images, videos, and other forms of unstructured data. His work spans engineering diagrams processing, remote inspection for oil and gas installations, intelligent condition monitoring of offshore assets, predictive maintenance, biometric applications, and medical datasets analysis. His expertise in ensemble-based learning and learning from unstructured and imbalanced datasets has been successfully implemented in various real-world applications. Professor Elyan was awarded the UK Knowledge Transfer Partnership Academic of the Year Award in 2023 for his transformative work in developing pioneering AI solutions for the oil and gas sector, and was a finalist for the Scottish Knowledge Exchange Award in 2024. These recognitions highlight his exceptional ability to bridge academic research with practical industry applications. His research has been supported by various public funding bodies including Innovate UK, the Data Lab Innovation Centre, Oil and Gas Innovation Centre (OGIC), NetZero Technology Centre (NTZ), and Historic Environment Scotland. Professor Elyan has supervised twelve PhD students to completion and examined more than fifteen others. He plays an active role in the academic community as a Fellow of the British Higher Education Academy and The International Neural Network Society, and serves as the Scotland Data Lab Innovation Centre Ambassador. Under Professor Elyan's leadership, the Machine Vision Research Group has developed innovative solutions including an end-to-end system for processing Piping and Instrumentation Diagrams (P&ID), AI-driven inspection systems for oil and gas assets, and defect recognition technologies. His work demonstrates a consistent commitment to translating cutting-edge research into practical tools that address real-world challenges, particularly in the energy sector.

• Pattern Recognition
• Computer Vision
• Medical Image Analysis
• +6 more

Dr. Carlos Francisco Moreno-Garcia is an Associate Professor in Computing at Robert Gordon University (RGU) in Aberdeen, Scotland, UK, affiliated with the School of Computing, Engineering & Technology and the Machine Vision Research Group. His academic journey began with a Bachelor's in Electronic Engineering from Tecnologico de Monterrey, Mexico, followed by a Master's and PhD in Spain at Universitat Rovira i Virgili. Dr. Moreno-Garcia's research spans Pattern Recognition, Computer Vision, Medical Image Analysis, Document Image Analysis, and Systematic Review Automation. His work bridges theoretical AI development with practical applications, particularly in digitizing complex engineering drawings for the Oil & Gas sector, developing medical diagnostic tools for cardiovascular diseases and neonatal pain assessment, and automating systematic literature reviews in healthcare. His recent publications demonstrate strong expertise in attention mechanisms, symbol recognition in technical diagrams, and NLP applications for medical literature analysis. His publication record shows consistent output with 77 documented research outputs, including significant recent contributions in 2024-2025 across high-impact journals and conferences. His work often addresses imbalanced datasets, few-shot learning challenges, and the integration of domain knowledge into AI models. General Chair of BMVC 2023 (elevated to CORE A Conference) Associate Editor of IEEE Transactions on Neural Networks and Learning Systems Co-leader of the Cluster of Machine Learning, AI and Data Science Leader of the Science, Technology and Innovation Pillar and Red Global MX Dr. Moreno-Garcia actively supervises PhD students working on document image analysis, medical applications of AI, and systematic review automation. His research is supported by collaborations with institutions including Universidad Nacional Autonoma de México (UNAM), Jiva.ai, NHS Grampian, and the University of Aberdeen. His lab focuses on real-world applications of computer vision and machine learning, with particular emphasis on healthcare and engineering documentation.

Noura Al MoubayedView profile

Associate Professor

• Explainable Machine Learning
• Natural Language Processing
• Optimisation
• +6 more

Dr. Noura Al Moubayed serves as an Associate Professor in the Department of Computer Science at Durham University, specializing in Explainable Machine Learning, Natural Language Processing, and Optimisation for high-dimensional data challenges. Education PhD from Robert Gordon University Post-doctoral positions at University of Glasgow and Durham University Research Focus Her work develops machine learning solutions for healthcare, social signal processing, cyber-security, and Brain-Computer Interfaces, specifically targeting noisy and imbalanced datasets through advanced NLP and optimization techniques. Current projects integrate deep learning with explainability frameworks to enhance real-world applicability. Publication Trends Recent publications (2020-2022) demonstrate consistent focus on improving question answering systems via bilinear pooling and paraphrasing techniques, alongside novel deep learning approaches for probabilistic topic modeling. These works bridge computer vision, NLP, and multimodal learning to address data imbalance challenges across healthcare and social computing domains. Academic Service PeerJ Computer Science Editorial Board Member 335 authorship points and 30 reviewer points on PeerJ platform

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +3 more

Marcin Sawiński is a Researcher and Teaching Assistant at the Department of Information Systems within the Institute of Informatics and Quantitative Economics at Poznań University of Economics and Business. With expertise spanning computer and information sciences (75%) and management and quality studies (25%), his work focuses on developing AI tools for addressing misinformation and enhancing fact-checking processes. His primary research interests include: Artificial Intelligence applications for fake news detection Natural Language Processing techniques for misinformation analysis Machine learning approaches to credibility assessment Persuasion techniques detection in social media content Development of robust language models for fact-checking systems Analysis of political narratives during crisis events like pandemics Dr. Sawiński's recent publications (2023-2025) demonstrate a concentrated research trajectory focused on applying transformer-based models to misinformation challenges, particularly in Slavic languages. His work spans multiple dimensions of the fake news problem, from detection of persuasion techniques to cross-lingual transfer learning for check-worthiness assessment. He has been actively involved in international competitions like CheckThat! Lab at CLEF, where his team (OpenFact) has developed innovative approaches to fact-checking challenges, including adversarial text generation to test model robustness. His scientific contributions include 15 publications with over 50 citations, reflecting his growing impact in the field of AI for misinformation detection. His research often involves collaboration with colleagues including Krzysztof Węcel, Witold Abramowicz, and Ewelina Księżniak.

• Machine Learning
• Operations Research
• Supply Chain Management
• +7 more

Hany Osman is an Associate Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, holding a PhD in Industrial Engineering from Concordia University and a Professional Engineer (PEng) license in Ontario. His academic-industrial career bridges theoretical research with practical applications across multiple sectors. Dr. Osman's research spans three interconnected domains: Machine Learning & Data Analytics : Specializing in logical analysis of data, cost-sensitive learning, and ensemble techniques for industrial applications Operations Research : Developing nature-inspired metaheuristics (cuckoo search, ant colony optimization) for NP-hard problems in manufacturing and logistics Supply Chain Management : Focusing on sustainable optimization of lot sizing, production planning, and inventory control under stochastic conditions His recent publications (2023-2024) reveal a strategic pivot toward AI-integrated manufacturing systems, notably the CAPP-GPT framework for generative AI in process planning and emission-aware lot sizing models. This work demonstrates consistent translation of theoretical advances into industrial solutions for rail, oil, and smart manufacturing sectors. Professional credentials include: IBM Mastery Certificate in Predictive Data Analytics Professional Engineer (PEng) license from Ontario Dr. Osman leverages extensive industrial experience in supply chain logistics, oil industry optimization, and education technology to inform both research and teaching. His supervision in the Master of Data Analytics program emphasizes hands-on application of machine learning to real-world operational challenges, with students contributing to publications in Manufacturing Letters and related journals. While no formal lab is specified, his research group operates at the intersection of data science and industrial engineering, maintaining strong industry partnerships that drive applied projects.

Lina GongView profile

Associate Professor

• Intelligent Software Engineering
• SE4AI
• Empirical Software Engineering
• +3 more

Lina Gong is an Associate Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. She holds a Ph.D. in Computer Software and Theory from China University of Mining and Technology (2020) and completed a research visit at Queen's University's Software Analysis and Intelligence Lab (SAIL) under Prof. Ahmed Hassan (2019-2020). Her research focuses on leveraging machine learning to extract insights from software repositories, with emphasis on: ML-enabled defect prediction techniques Code pre-trained models for vulnerability detection Identifier normalization and issue classification Empirical studies of software quality attributes Her recent publications (2023-2025) demonstrate strong trends in applying transformer architectures to code analysis, with increasing focus on supply chain security and cross-platform UI translation. Key venues include IEEE TSE, ACM TOSEM, and ASE. Scientific recognition includes: National Natural Science Foundation of China (2022-2025) Natural Science Foundation of Jiangsu Province (2022-2025) Key National Laboratory Foundation (2022-2023) Excellent Ph.D. Student Award (CUMT) She actively mentors graduate students (14 advisees: 1 doctoral, 13 master's) and serves on program committees for ASE, APSEC, and SANER. Her research is supported by multiple competitive grants focusing on ML applications in software engineering.

Xiao YuView profile

Researcher

• LLMs Data Governance and Evaluation
• Intelligent Software Engineering
• Software Security and Reliability
• +2 more

Xiao Yu is a Research Fellow (Assistant Research Professor) at the State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China. Previously, they were a Postdoctoral Researcher at Huawei under Prof. Xin Xia. They hold dual PhD degrees: from Wuhan University's School of Computer Science (December 2020) supervised by Prof. Jin Liu, and from City University of Hong Kong's Department of Computer Science (March 2021) supervised by Prof. Qing Li and Prof. Jacky Wai Keung. Research focuses on three interconnected domains: LLMs Data Governance and Evaluation addressing hallucination phenomena and task-specific LLM evaluation in software engineering; Intelligent Software Engineering leveraging deep learning for code generation, annotation, and maintenance; and Software Security and Reliability investigating vulnerability detection, log anomaly identification, and security bug classification. Their work bridges theoretical advancements with industrial applications, particularly in blockchain and data security contexts. Recent publications demonstrate strong trends in realistic LLM evaluation (RealisticCodeBench), vulnerability detection using semi-supervised learning, and industrial anomaly detection. Key thematic areas include effort-aware defect prediction, code smell detection, and the practical application of large language models in software engineering tasks, with increasing emphasis on data quality and privacy considerations. Xiao Yu actively contributes to the academic community through extensive service roles including journal reviewing for ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Dependable and Secure Computing, and serving on program committees for major conferences like APSEC 2025 and ASE 2025. They have supervised numerous graduate students as evidenced by authorship patterns in publications. Based at Zhejiang University's State Key Laboratory of Blockchain and Data Security, their research operates at the intersection of academic rigor and industrial relevance, with strong collaborations spanning multiple institutions including Huawei, Wuhan University, and City University of Hong Kong.