Find Academic Supervisors

Moawia EldowView profile

Associate Clinical Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +2 more

Dr. Moawia Eldow is a Clinical Associate Professor in the Department of Computer Science and Engineering at the University of North Texas. His research spans artificial intelligence, machine learning, and computer vision, with applications in healthcare, security, and education. His recent work includes COVID-19 detection algorithms and facial recognition systems. His publications demonstrate a consistent focus on neural networks, pattern recognition, and human-computer interaction. Recent articles explore AI diagnostics, educational technology, and cybersecurity, reflecting interdisciplinary collaboration with healthcare and linguistics domains. Dr. Eldow maintains an extensive publication record with contributions to IEEE, Springer, and ACM venues. His lab investigates adaptive algorithms for real-world challenges including medical diagnostics and network optimization.

Krzysztof PancerzView profile

Professor

• Machine Learning
• Computational Intelligence
• Knowledge Discovery from Data
• +3 more

Krzysztof Pancerz is a Professor at the Department of Computer Science within the Faculty of Philosophy at John Paul II Catholic University of Lublin (KUL), Poland. His institutional affiliation is formalized as "prof. KUL" with contact address at Al. Racławickie 14, 20-950 Lublin. He holds dual doctorates: PhD in Computer Science (2006) and DSc in technical sciences (2018) from the Polish Academy of Sciences in Warsaw. His research spans Computational Intelligence and Medical Diagnostics , with emphasis on machine learning for cancer detection using Raman/FTIR spectroscopy. Key areas include: Knowledge discovery from biomedical spectral data Ontology-based information systems Unconventional computing models Deep learning for disease biomarker identification Recent publications (2022-2025) demonstrate consistent focus on cross-disciplinary applications, particularly in oncology and reproductive health diagnostics. He actively organizes scientific workshops including: International Workshop on AI in Medical Applications (FedCSIS conferences, 2011-2018) Knowledge and Data Engineering in Medicine (KES conferences since 2018) His research leadership includes managing the Ministry of Science-funded project on non-invasive laryngeal disease diagnostics and contributing to the EU FP7 PhyChip project on slime mould computing. Current thesis supervision includes topics like fruit leaf recognition and financial deposit forecasting using neural networks. Professional engagement: Member of Polish Fuzzy Sets Society (POLFUZZ) ORCID: 0000-0002-5452-6310 Publications indexed in arXiv, dblp, PubMed

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +3 more

Mahdieh Zabihimayvan is an Assistant Professor in the Computer Science Department at Central Connecticut State University , specializing in Cybersecurity and Artificial Intelligence applications. Her research focuses on enhancing proactive cyber threat intelligence and privacy protection using applied machine learning and complex systems analysis . Areas of Expertise: Machine learning for Cybersecurity, Complex systems analysis Memberships: European Society for Fuzzy Logic and Technology, ACM-W Women in Network Science, Women in Machine Learning, WomenTech Network Courses Taught: Computer Security, Secure Software Development, Big Data Programming, Systems Programming, Secure Software Designs, Introduction to Internet Programming Research Interests include cybersecurity , machine learning , and complex systems . She has published extensively on topics such as darknet traffic classification , phishing attack detection , and web robot identification , often leveraging fuzzy logic and deep learning techniques. Scientific Awards include a nomination for the Undergraduate Research Competition in 2022. Her work intersects cybersecurity , machine learning , and privacy protection , particularly in analyzing Tor networks , dark web systems, and biomedical data .

• Artificial Intelligence
• Decision Making
• Rough Set Theory
• +3 more

Soma Dutta, Ph.D., is a researcher affiliated with the Department of Mathematical Methods in Computer Science at the Faculty of Mathematics and Computer Science, University of Warmia and Mazury in Olsztyn. Her work bridges mathematics and computer science, focusing on logical systems for artificial intelligence and decision-making frameworks. Research Themes: Deductive and inductive reasoning in AI, data reduction in decision-making, and rough/fuzzy/similarity-based reasoning. Supervision: Supervises 1 doctoral student and offers research topics in graded consequence theory and interactive granular computing. Scientific Profile: With 22 Web of Science/Scopus publications and over 300 citations, her work emphasizes meta-logical foundations, paraconsistent logics, and granular computing. She has an h-index of 10 (Google Scholar). Infrastructure: The department provides adequate resources for research, though no dedicated funding exists for her projects.

Veronique HosteView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +7 more

Veronique Hoste is a Full Professor of Computational Linguistics at Ghent University's Faculty of Arts and Philosophy , where she serves as Department Head of the Department of Translation, Interpreting and Communication and Director of the LT3 Language and Translation Technology Team . Her work bridges machine learning with natural language processing, focusing on semantics, discourse modeling, and practical applications in emotion detection, irony recognition, and customer service dialogue analysis. PhD in Computational Linguistics from University of Antwerp (2005) 2023-2024: Francqui Chair at Université Libre de Bruxelles 2024: Elected to Royal Flemish Academy of Belgium (KVAB) Research Interests: Specializes in machine learning approaches to coreference resolution, sentiment analysis, and multimodal emotion detection. Leads projects like FlandersAI (empathy in conversational agents), METRICS (emotion trajectories in service dialogues), and SENTiVENT (financial event extraction). Develops high-quality datasets (e.g., EmotioNL , ENCORE ) for broader NLP community use. Recent Publications span Dutch social media irony detection, Classical Chinese poetry sentiment analysis, and multimodal emotion datasets. Collaborates on interdisciplinary initiatives like NewsDNA (news recommendation) and SentEMO (commercial sentiment analysis). Scientific Awards Francqui Chair (2023-2024) KVAB Membership (2024) Outreach includes co-founding AlfaSent (LT3 spin-off for customer feedback analysis) and authoring the first Dutch-language NLP book Taaltechnologie Ontrafeld (2024). Coordinates AI education initiatives like the AI at School project.

• Reasoning under Uncertainty
• Decision-Making under Uncertainty
• Belief Functions Theory
• +5 more

Thierry Denoeux is a Full Professor (Exceptional Class) at the Department of Information Processing Engineering, Université de Technologie de Compiègne, France. He holds senior membership in the Institut Universitaire de France (since 2019, reconducted 2024) and serves as Editor-in-Chief of the International Journal of Approximate Reasoning . His research focuses on reasoning and decision-making under uncertainty, with major contributions to the theory of belief functions and its applications in machine learning, statistical inference, and information fusion. Denoeux has authored over 350 publications and supervised 30+ PhD theses. Education : Graduated from École nationale des ponts et chaussées (PhD from same institution). Roles : President of the Belief Functions and Applications Society, Associate Editor of Fuzzy Sets and Systems , and other journals. His research interests emphasize managing uncertainty in intelligent systems, with applications in medical imaging, data mining, and econometrics. Recent work includes evidential machine learning frameworks, uncertainty quantification in neural networks, and fusion of multimodal medical data. Publications span topics like evidential clustering, fusion of uncertain data, and applications in hydrology and materials science. Over 150 journal articles and conference papers since 2020 alone reflect his prolific output in belief functions theory and its practical implementations. Awards : Senior IUF Membership (2019–present). Grants/Advising : Extensive supervisory experience in PhD and postdoctoral research, with notable contributions to interdisciplinary projects involving healthcare and environmental applications. Denoeux collaborates internationally, leading initiatives in evidential AI and uncertainty-aware decision systems through his editorial and society leadership roles.

• Operations & Supply Chain Management
• Performance Evaluation & Optimization
• R&D and Innovation Management
• +3 more

Dr Pegah Khoshnevis is a Lecturer in Operations Management at the Management School of the University of Sheffield. She holds a BSc in Industrial Engineering, an MSc in Business Engineering (Supply Chain Management) from Universite Catholique de Louvain, and a PhD in Business and Economics from KU Leuven, Belgium. Her research focuses on performance evaluation, operations & supply chain management, R&D innovation management, and sustainable logistics. She has published in top-tier journals such as Regional Studies , Omega , and Annals of Operations Research . Her research interests emphasize optimizing supply chains, environmental performance assessment, R&D efficiency, and innovation systems. Recent work includes DEA-driven risk frameworks for oil supply chains and evaluating SME efficiency in transforming R&D investments into market outputs. Her articles often apply advanced methodologies like Data Envelopment Analysis (DEA) and fuzzy logic to real-world challenges in logistics, energy, and aerospace sectors. Prior to SUMS, she taught at De Montfort University, covering modules like Business Research & Analytics and Operations Management. She is actively involved in teaching MGT253 (Principles of Operations Management) and MGT138 (Analysis for Decision Making) at Sheffield. Her research has explored diverse sectors including space industry clusters, Indian agriculture, and Nigerian oil supply chains, highlighting her interdisciplinary approach. Her work on sustainable sourcing and environmental performance underscores a commitment to integrating sustainability into operational strategies. While no major awards are explicitly noted, her prolific publication record and methodological contributions reflect her academic standing. She supervises PhD research in operations management, innovation systems, and sustainable logistics, indicating active engagement in nurturing future scholars.

Jinglan ZhangView profile

Senior Lecturer

• Artificial Intelligence
• Big Data Analysis
• Computer Human Interaction
• +7 more

Dr. Jinglan Zhang is a Senior Lecturer at Queensland University of Technology (QUT), specializing in Artificial Intelligence and Information Systems. She holds a PhD from QUT and has over 15 years of research experience, focusing on multimedia processing, big data analysis, and human-computer interaction. Her work spans environmental monitoring, health informatics, and web applications. Dr. Zhang has published over 100 refereed papers and secured over $1M in research funding. She actively supervises PhD and Masters students and mentors colleagues through QUT's mentoring program. Education: Bachelor of Engineering (1987) and Master of Engineering (1990), Beijing University of Aeronautics and Astronautics (now Beihang University), China. Master of Computing (1998–1999), Macquarie University, Australia. PhD (2003), Queensland University of Technology. Research Interests: Artificial Intelligence: Visual/Acoustic Information Processing, Machine Learning. Big Data Analysis and Visualization. Computer-Human Interaction, including accessibility for people with disabilities. Software Engineering, Mobile/Web Applications. Environmental Monitoring using Acoustic Sensors. Academic Contributions: Dr. Zhang has led projects in bioacoustics, healthcare AI, and accessibility tools. She organizes international conferences (e.g., IEEE e-Science 2010) and collaborates with institutions in China and Australia. Her recent work explores transfer learning, data scarcity solutions, and AI ethics. Grants & Advising: Over $1M in research grants, including projects on assistive technologies for people with intellectual disabilities. Supervised 10 PhD and 2 Masters students to completion. Current mentor for 3 PhD students and collaborator on interdisciplinary teams. Labs & Teams: Core member of QUT's Centre for Data Science, leading the Environment and Natural Systems research theme. Active in collaborative initiatives with Chinese universities and research institutions.

Shivani SinghView profile

Research Fellow

• Uncertainty Modeling
• Data Analysis
• Fuzzy Rough Set Theory
• +3 more

Shivani Singh is a postdoctoral researcher at the Department of Computing Science , Umeå University. Her work focuses on interdisciplinary collaborations connecting AI, Mathematics, and Applied Computing. Research Interests Uncertainty Modeling Data Analysis Fuzzy Rough Set Theory

Salem ChakharView profile

Research Fellow

• Multicriteria Analysis
• Decision Support Systems
• Geographical Information Science
• +6 more

Dr. Salem Chakhar is a Senior Research Fellow at the University of Portsmouth, affiliated with the Faculty of Business and Law, Portsmouth Business School, and the Centre for Operational Research and Logistics. He holds a PhD in Computer Science from the University of Paris-Dauphine and has extensive academic and industry experience across multiple institutions in Tunisia, Canada, and France. His research focuses on decision support systems, geographical information science, multicriteria analysis, and fuzzy theory applications. Dr. Chakhar's work spans interdisciplinary areas including spatial modeling, database systems, and operational research. He has contributed to over 98 research outputs, including peer-reviewed articles, conference proceedings, and edited volumes. His recent projects include applications in maritime logistics, retail productivity enhancement via digital twins, and spam SMS mitigation using machine learning. He is actively involved in professional organizations such as the International Cartographic Association's commission on geospatial analysis. His research emphasizes practical solutions for complex decision-making problems in domains like healthcare risk assessment, crowdfunding analysis, and cybersecurity.

Xin ChenView profile

Associate Professor

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +7 more

Xin Chen is an Associate Professor of Computer Science at the University of Nottingham, School of Computer Science. He is an IEEE Senior Member with expertise in medical image analysis, computer vision, and machine learning. His educational background includes BEng and PhD degrees, though specific institutions are not mentioned in the provided text. Chen's research focuses on medical image processing with particular emphasis on image segmentation, registration, and motion analysis. His work applies computer vision and machine learning techniques to solve medical challenges in areas such as wrist injury analysis, diabetes care, breast cancer screening, and MRI reconstruction. His team develops algorithms for 2D/3D image segmentation, image registration, statistical shape/motion modeling, and CT & MRI image reconstruction. His recent publications (2023-2025) show a strong focus on deep learning approaches for medical image analysis, particularly segmentation and registration tasks. These works often incorporate innovative techniques like fuzzy systems, transformer networks, and uncertainty modeling to address challenges in medical image analysis. IEEE Senior Member Chen has successfully supervised multiple PhD students to completion and currently mentors several PhD candidates. His research is supported by significant grants from Wellcome Leap, MRC, ERC, NIHR/NHSx, Weston Brain Institute, British Heart Foundation, and Precision Imaging Beacon. His team, the Intelligent Modelling & Analysis Group, focuses on translating computer vision and machine learning research into practical medical applications. The group maintains active collaborations with medical researchers and clinicians, developing software tools for 2D/3D interactive image segmentation and automatic nerve fiber quantification in corneal confocal microscopy images.

• Algebra
• Number Theory
• Mathematical Logic
• +4 more

Akın Osman Atagün is a Professor in the Department of Mathematics at Kırşehir Ahi Evran University's Faculty of Arts and Sciences. With a career spanning since 2007, he has held full-time academic roles at Bozok University (2007-2012) and Ahi Evran University (2019-present). His leadership includes serving as Department Head (2022-) and Division Chair (2009-2013, 2020-). Education: PhD in Mathematics, Erciyes University (2006); MSc in Mathematics, Kırıkkale University (2001); BA in Mathematics, Erciyes University (1999). His research focuses on Algebra and Number Theory , Foundations of Mathematics , and Soft Set Theory , particularly in near-rings, fuzzy sets, and decision-making systems. Collaborative work with colleagues has advanced soft group theory, semigroup structures, and computational applications. Recent publications highlight trends in Soft Set Operations , Rough Set Applications , and Decision Systems , including novel characterizations of soft substructures and their use in uncertainty modeling. His work has been cited extensively, with an h-index of 25 (Google Scholar). Scientific Award: Scientific Publication Award, Bozok University (2012). He has supervised 14 thesis students, including PhD and MSc candidates, focusing on topics like Intuitionistic Fuzzy Sets , Soft Vector Spaces , and Decision Algorithms . His projects, such as "A New Approach to Semigroup Theory via Soft Sets" (2013-2014), underscore his contributions to mathematical frameworks.

• Theoretical Computer Science
• Logic in Computer Science
• Artificial Intelligence
• +3 more

Sabine FRITTELLA is a Lecturer with HDR at INSA Centre Val de Loire, affiliated with the Laboratoire d'Informatique Fondamentale (LIFO). She is an active researcher in theoretical computer science, focusing on logic and privacy-preserving technologies. Her work bridges formal methods and practical applications in data privacy and reasoning under uncertainty. Her primary research areas include paraconsistent logic, Dempster-Shafer theory, and privacy-enhancing technologies. She investigates how to model contradiction and uncertainty in evidence, and develops formal frameworks for data minimization and informed consent in administrative forms. Her work often combines logical foundations with real-world privacy challenges. Analysis of her recent publications (2020-2025) reveals a strong focus on privacy-preserving technologies, particularly data minimization and consent management, alongside theoretical work in non-classical logics. She has made significant contributions to paraconsistent reasoning and the application of belief function theory to concept representation. Her research consistently addresses the challenge of handling inconsistent information in practical settings. She is a member of the LIFO laboratory (Laboratoire d'Informatique Fondamentale d'Orléans), a joint research unit of the University of Orléans and INSA Centre Val de Loire, where she collaborates on projects in theoretical computer science and its applications.

Sunny RaiView profile

Researcher

• Natural Language Processing
• Computational Social Science
• Mental Health Informatics
• +6 more

Sunny Rai is a postdoctoral researcher at the Department of Computer and Information Science at the University of Pennsylvania , where he works under advisors Dr. Lyle H. Ungar and Dr. Sharath C. Guntuku . He holds a Ph.D. from the University of Delhi, India , with a thesis on Computational Metaphor Processing for English Language . Current Research : Focuses on culturally aware NLP, AI in health, cross-cultural variations in depression markers, chatbots for vaccine counseling, and bias/risk analysis in LLMs. Grants : Awarded Leonard Davis Institute grant (USD 19,500) and PMCRI grant (USD 50,000) in 2024 for mental health and vaccine counseling projects. Key Research Themes include culturally aligned AI, behavioral health assessment, social emotions across cultures, and LLM bias detection. His work intersects NLP, computational social science, and public health, with a focus on cross-cultural applicability of models. Publication Trends highlight applications of NLP in healthcare (depression markers, pain analysis), cross-cultural AI (cultural alignment lexica, emotion modeling), and societal impact studies (pandemic needs, radicalization vulnerability). Methodological approaches include RoBERTa fine-tuning, VOSviewer analysis, and metaphorical semantic shifts via diachronic embeddings. Scientific Awards : Best Paper Award at WASSA'23 , grants from Leonard Davis Institute and PMCRI . Conference Participation : Presented at SPSP 2025 , NIDA 2024 , ICWSM 2023 , and NAACL / ACL / WWW conferences. At UPenn, Sunny contributes to interdisciplinary research groups combining computer science and medical perspectives , with collaborations extending to institutions like The Alan Turing Institute and Aalto University . His technical expertise spans machine learning frameworks like RoBERTa , word2vec , and jekyll development, with prior experience in Facebook AI's RoBERTa and HuggingFace transformers .

Roman SlowinskiView profile

Professor

• Operational Research
• Artificial Intelligence
• Decision Aiding
• +7 more

Roman Slowinski is a Professor and Founding Chair of the Laboratory of Intelligent Decision Support Systems at Poznan University of Technology, Poland, and also holds a professorship at the Systems Research Institute of the Polish Academy of Sciences in Warsaw. His research integrates Operational Research and Artificial Intelligence for Decision Aiding, focusing on robustness in decision analysis, preference learning, and uncertainty modeling. He is a Fellow of IEEE, IRSS, INFORMS, IFIP, IFORS, IAITQM, AAIA, and AIIA, and a full member of Academia Europaea and the Polish Academy of Sciences. Recipients of Honorary Doctorates: Faculte Polytechnique de Mons (Belgium, 2000), Université Paris Dauphine (France, 2001), Technical University of Crete (Greece, 2008), Nanjing University of Aeronautics and Astronautics (China, 2018), Hellenic Mediterranean University (Greece, 2022), and University of West Attica (Greece, 2024) His seminal contributions include dominance rough set theory and methodologies for preference learning, applied across medicine, industry, economics, and environmental domains. He has co-authored 16 books and over 550 scientific articles, achieving an h-index of 100 (Google Scholar). As Editor-in-Chief of the European Journal of Operational Research since 1999, he has shaped editorial directions in operations research. Awards include the EURO Gold Medal (1991), Richard Price Prize (2021), and Humboldt Research Award (2023).