جست‌وجوی استاد و پژوهشگر

• Medical Image Analysis
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Jolita Bernatavičienė serves as a Senior Researcher at Vilnius University's Institute of Data Science and Digital Technologies within the Image and Signal Analysis Group. With a Doctorate in Technological Sciences (Informatics), she has established herself as a leading researcher in medical image analysis and artificial intelligence applications in healthcare. Her extensive research portfolio spans over 15 years of continuous contributions to the field. Dr. Bernatavičienė's research interests primarily focus on medical image analysis, particularly in ophthalmology and oncology applications. Her work integrates advanced machine learning techniques with medical diagnostics, specializing in eye fundus image analysis for glaucoma detection and prostate MRI analysis for cancer identification. She has made significant contributions to deep learning architectures, signal processing methodologies, and data analysis frameworks applicable to biomedical challenges. Her publication record demonstrates strong trends in applying cutting-edge AI techniques to solve concrete medical problems, with a noticeable shift toward more sophisticated deep learning architectures in recent years. The research spans multiple medical domains including ophthalmology, cardiology, oncology, and renewable energy systems monitoring, reflecting her interdisciplinary approach to data science applications. Leader of International Conference 'Data Analysis Methods for Software Systems (DAMSS)' 2015-2024 Member of IEEE Computer Society section (since 2022) Member of the Council of the Lithuanian Computer Association, Artificial Intelligence Section Member of the Lithuanian Operations Research Society Expert at the Science, Innovation and Technology Agency (MITA) (2020-2022) Dr. Bernatavičienė actively supervises doctoral and master's students, with current doctoral student Roman Surkant working on prostate MRI analysis. She leads multiple significant research projects including 'Developing Talents in Artificial Intelligence to Solve Disruptive Environmental Problems' and serves as scientific leader for the Research Council of Lithuania funded project on cardiac MRI texture analysis. Her work has been supported by various national and international funding mechanisms including COST activities, EuroHPC programs, and Lithuanian national research grants. She is principal investigator for the long-term project developing a database of depersonalized fundus images (2018-2030) and has led numerous projects related to medical image analysis, AI applications in healthcare, and data science methodologies. Her research group maintains strong international collaborations through COST actions and other European research networks.

Erik Štrumbeljمشاهده پروفایل

پژوهشگر

• Probability
• Statistics
• Machine Learning
• +۵ مورد دیگر

Erik Štrumbelj is a researcher specializing in probability, statistics, and machine learning. His work focuses on stochastic processes, Monte Carlo methods, Bayesian statistics, and applications in sports analytics (player evaluation, match simulation, statistical forecasting). He has contributed to advancing computationally intensive statistical analysis and has collaborated on interdisciplinary projects involving data mining, high-performance computing, and explainable AI. Key research trends include: Developing game theory-based explanations for machine learning predictions Modeling sports outcomes using Markov processes Analyzing forecasting reliability through betting odds Integrating data mining with medical modeling He has been involved in numerous research projects funded by ARRS and international bilateral agreements, addressing topics like imbalanced data analysis, AI explainability, and hypercomputing applications.

Jun Liuمشاهده پروفایل

استاد

• Artificial Intelligence
• Trustworthy AI
• Explainable AI
• +۴ مورد دیگر

Professor Jun Liu is a Professor of Artificial Intelligence and Director of the Artificial Intelligence Research Centre (AIRC) at the School of Computing, Ulster University. With over 270 publications and more than £18 million in research funding, he is a leading figure in artificial intelligence, particularly in trust and explainable AI systems and logic-based reasoning methods. Dr. Liu received his BSc and MSc degrees in Applied Mathematics, and PhD degree in Information Engineering from Southwest Jiaotong University, Chengdu, China, in 1993, 1996, and 1999, respectively. Prior to joining Ulster University, he held postdoctoral positions at The University of Manchester, UK (Feb. 2002 - Dec. 2004) and the Belgian Nuclear Research Centre (SCK*CEN) (Mar. 2000 - Feb. 2002). Professor Liu's research focuses on trust and explainable data-knowledge integrated AI decision models with applications in safety and risk analysis, policy decision making, security/disaster management, and healthcare; and logic and automated reasoning methods for intelligent systems, including resolution-based automated reasoning and lattice-valued logics for handling incomparability, inconsistency, and imprecision. His work spans theoretical foundations to practical applications in smart homes, healthcare, and industrial settings. His recent publications demonstrate a strong trend toward developing more trustworthy and explainable AI systems, with particular emphasis on belief rule-based approaches for handling uncertainty in decision-making. The research spans multiple domains including smart home activity recognition, medical imaging, food quality analysis, and environmental monitoring, showing the versatility and applicability of his methodologies. Ulster University best computer science paper award for 2016 IEEE Senior Member including IEEESMC and IEEECI Fellow of the UK Higher Education Academy Associate Editor of IEEE Transaction on Fuzzy Systems Current Chair of IEEE CIS Emergent Technologies Technical Committee As Director of the Artificial Intelligence Research Centre, Professor Liu has secured significant research funding as principal investigator and co-investigator. His current projects include "The use of Agentic AI in judicial decision-making" funded by EPSRC and "Adaptive Modeling Method for Deep Belief Rule Base" for smart home applications. He serves on editorial boards of multiple high-impact journals and organizes international conferences including the 23rd UK Workshop on Computational Intelligence. The Artificial Intelligence Research Centre under Professor Liu's leadership focuses on developing cutting-edge AI methodologies with practical applications. The center collaborates extensively with industry partners including BT through the BTIIC Phase 2 initiative and PwC through their Advanced Engineering and Research Centre, ensuring research has real-world impact across multiple sectors.

• Machine Learning
• Process Mining
• Contrastive Learning
• +۲ مورد دیگر

Bilal Ahmed Lodhi is a Lecturer in Computer Science at the School of Computing , Faculty Of Computing, Engineering and the Built Environment , Ulster University. He is based at the Belfast campus and can be reached via his office email. His research focuses on advanced topics in computer science, including: Learning Frameworks for data modeling Process Mining and business analytics Contrastive Learning and deep learning techniques Handling imbalanced datasets in machine learning His work aligns with broader efforts in Artificial Intelligence and Data Science, contributing to sustainable development goals through computational innovation.

Jonathan Wallaceمشاهده پروفایل

استاد

• Innovation & Entrepreneurship
• Connected Health
• Usability & UX Design
• +۶ مورد دیگر

Professor Jonathan Wallace serves as Professor of Innovation at Ulster University's School of Computing. With over 99 peer-reviewed publications, he is recognized as a subject matter expert in Usability, UX Design, Knowledge Engineering, Digital & Social Media, Innovation & Entrepreneurship, and Big Data in Healthcare. His research interests include: Connected Health and user-centered product development Usability, UX Design, and Knowledge Engineering Digital & Social Media applications Big Data in Healthcare Innovation & Entrepreneurship Co-creation and Business Model Development Professor Wallace's recent publications demonstrate a strong focus on applying machine learning and data analytics to healthcare challenges, particularly in mental health for older adults and marine engineering applications. His work bridges computer science with practical healthcare and industrial solutions. Scientific awards and recognitions: Distinguished Business Fellowship Distinguished Teaching Fellowship Professor Wallace has generated significant consultancy income for Ulster University, with personal funded income exceeding £4.2M. He has served as Director of Knowledge and Technology Transfer for the Faculty of Computing and Engineering for 16 years. He chairs the Northern Ireland Science Festival Board and is co-founder of OPEN-NI promoting open source software. He leads the TRAIL Living Lab at Ulster University and has significant involvement in Connected Health research, including the EC-funded H2020 PCP MAGIC Project and the H2020 PM18 MIDAS Project for 'Big Data Supporting Public Health Policies'.

• Computer Science
• Knowledge Graph
• Representation Learning
• +۷ مورد دیگر

Huijuan Xu is an Assistant Professor in the Department of Computer Science and Engineering. Her research spans artificial intelligence, computer vision, and knowledge representation, with a focus on temporal modeling, semantic reasoning, and multimodal learning. She has contributed to advancements in virtual reality streaming, knowledge graph completion, and weakly-supervised video analysis. Research output: 32 publications (2015-2025), including 15 peer-reviewed articles and conference contributions Core research areas: Representation Learning (100% match), Knowledge Graph (100% match), Temporal Action Detection (86% match), and Motion Feature Learning (73% match) Her recent work explores: 2025 : Bandwidth-optimized VR streaming for edge devices 2024 : Neural concept reasoning for image retrieval and avatar generation from sparse data 2023 : Zero-shot scene graph generation and bias mitigation in visual QA

Ben Cardoenمشاهده پروفایل

پژوهشگر ارشد

• Biomedical image analysis
• Topological analysis
• Long tail/imbalanced machine learning
• +۴ مورد دیگر

Dr Ben Cardoen is a Research Fellow at the School of Mathematics, University of Birmingham, specializing in algorithm development for biomedical imaging analysis at multiple scales—from diffuse optical tomography to superresolution microscopy. His work focuses on recovering minimal causal signatures for pathologies in ageing and inflammation using sparse high-dimensional data. His educational background includes: BSc in Computer Science, University of Antwerp (2015) MSc in Computer Science, University of Antwerp (2017) PhD in Computing Science, Simon Fraser University (2024) Following postdoctoral work at the University of British Columbia (2025), he joined the University of Birmingham. Dr Cardoen designs scalable, interpretable algorithms leveraging belief theory, graph algorithms, and structural causal discovery to push beyond empirical resolution limits in imaging modalities. His research targets degenerative diseases (Alzheimer, ALS, ageing), metabolic disorders (diabetes), and infectious diseases, with emphasis on robustness to complex noise models. Key methodologies include weakly supervised learning for protein-organelle interaction analysis in volumetric point cloud data, utilizing distributed computing on SLURM-based supercomputers. No scientific awards are documented in the provided materials. Regarding academic mentorship and funding, the text indicates no formal advising roles or grant details beyond his current fellowship position. Dr Cardoen's active projects involve extending probabilistic learning algorithms for nested label recovery in multichannel superresolution microscopy, with applications spanning genomic analysis, viral infection tracking, and cellular drug response modeling.

Manuela Pettiمشاهده پروفایل

استادیار

• Precision Medicine
• Biological Network Inference
• Network Neuroscience
• +۳ مورد دیگر

Manuela Petti is an Assistant Professor of Biomedical Engineering at Sapienza University of Rome's Department of Computer, Control, and Management Engineering since 2019. She is affiliated with the Bioengineering and Bioinformatics Lab and the National Bioengineering Group (GNB), and serves as Associate Editor for Frontiers in Medical Engineering (Computational Medicine section). Her academic background includes a cum laude M.Sc. in Biomedical Engineering from Sapienza University of Rome (2011) and a Ph.D. from the University of Bologna (2016), with a visiting stint at Indiana University Bloomington (2015). M.Sc. Biomedical Engineering, Sapienza University of Rome (2011, cum laude) Ph.D. Biomedical Engineering, University of Bologna (2016) Dr. Petti's research pioneers network-based computational approaches for precision medicine, integrating molecular data analysis, biological network inference, and brain connectivity estimation. Her work bridges bioinformatics and biomedical engineering to develop advanced methods for cancer research (focusing on sexual dimorphism and ethnic disparities), network neuroscience, and bioelectrical signal processing, with significant applications in oncology and neurology. Analysis of her 56 Scopus-indexed publications (1282 citations, H-index 13) reveals dominant trends in network medicine for cancer diagnostics, including immunological network signatures for immunotherapy response, miRNA regulation networks for sex disparities, and multi-omics integration for biomarker discovery. Her recent work emphasizes radiomics, drug repurposing, and patient stratification through network-based approaches. As an educator, she teaches "Bioinformatics and Network Medicine" and "Digital Epidemiology and Precision Medicine" in Sapienza's Data Science MSc program, having supervised over 10 Master's theses in Data Science, Computer Science, and Bioinformatics. She actively contributes to the Bioengineering and Bioinformatics Lab at Sapienza University of Rome, where her team develops computational frameworks for translating network-based discoveries into clinical applications, particularly in cancer therapeutics and neurological disorders.

• Quality Management
• Organizational Excellence
• Supply Chain Management
• +۷ مورد دیگر

Paulo Sampaio is an Assistant Professor at the Department of Production and Systems, School of Engineering, University of Minho. As Senior Researcher at ALGORITMI Research Centre's IEM group, he focuses on Quality and Organizational Excellence. He holds a PhD in Industrial Engineering (2008) and has authored 138 publications with an h-index of 26. Born in Braga, Portugal (1978) Graduated in Industrial Engineering (2002) PhD in Industrial Engineering (2008) Visiting Scholar at MIT (2015) His research spans Quality 4.0, Supply Chain Management, Risk Analysis, and Integrated Management Systems, with particular emphasis on Industry 4.0/5.0 transformation and agile organizational models. Articles demonstrate increasing focus on machine learning applications, sustainability taxonomy development, and cross-cultural SME studies. Scientific recognition includes: Feigenbaum Medal (ASQ 2012) Best Paper Awards (2006-2009) International Academy for Quality Academician (2014) Top reviewer recognition (2015) As an advisor, he has successfully mentored 6 PhD candidates and over 100 Master's students while leading major EU-funded projects like INNOVCAR (€12.75M) and iFACTORY (€9.25M). Current leadership roles include Department Head, Doctoral Program Director, and Scientific Council member.

• Machine Learning
• Big Data Analytics
• Computational Intelligence
• +۴ مورد دیگر

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.

• Artificial Intelligence
• Brain-Computer Interfaces
• Fuzzy Neural Networks
• +۸ مورد دیگر

Distinguished Professor Chin-Teng Lin is a leading academic at the University of Technology Sydney (UTS) , where he serves as Co-Director of the Australian AI Institute (AAII) and Director of the Computational Intelligence and Brain Computer Interface Lab . With a career spanning decades, he has pioneered advancements in artificial intelligence (AI) and brain-computer interfaces (BCI) , focusing on human-machine collaboration, wearable EEG systems, and neuroergonomics. School of Computer Science, UTS Co-Director, Australian AI Institute Director, Computational Intelligence and BCI Lab Lin’s research interests are deeply rooted in machine-intelligent systems , cognitive neuroscience , and human-centric AI . He has developed groundbreaking technologies like fuzzy neural networks (FNNs) in 1992, which revolutionized AI by integrating human-like reasoning. His work extends to multi-agent reinforcement learning for cybersecurity, wearable EEG devices for real-world applications, and neurofeedback interventions for chronic pain management. His research outputs include over 950 peer-reviewed publications , with a focus on deep learning , transformer models , and clustering algorithms . Articles like the MGRW-Transformer (2025) and Autonomous Clustering (2025) highlight his leadership in interpretable AI and parameter-free methods. Scientific awards and recognitions include: IEEE Fellow (2005) IFSA Fellow (2012) IEEE Fuzzy Systems Pioneer Award (2017) Outstanding Achievement Award, Asia Pacific Neural Network Assembly Lin has supervised 72 PhD candidates , 30 postdoctoral fellows , and 237 research Masters students since 1992, mentoring notable alumni such as Dr. Zehong Cao (ARC DECRA Fellow) and Prof. Chia-Feng Juang (IEEE Fellow). His funding portfolio includes $10M+ from the US Army Research Lab , $3.8M from the Australian Defence Innovation Hub , and $30.2M in industry collaborations .

• Neuroimaging
• Artificial Intelligence
• Medical Imaging
• +۵ مورد دیگر

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 Hájekمشاهده پروفایل

استاد

• Machine Learning
• Neural Networks
• Fuzzy Logic
• +۷ مورد دیگر

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-Chowمشاهده پروفایل

دانشیار

• Business Education
• Financial Data Analysis
• Statistical Methods
• +۲ مورد دیگر

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 Kovácsمشاهده پروفایل

مدرس ارشد

• Speech and Language Technology
• Automatic Speech Recognition
• Natural Language Processing
• +۵ مورد دیگر

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.