Find Academic Supervisors

Gigliola VagliniView profile

Professor

• Distributed Systems Verification
• Model Checking
• Machine Learning Applications
• +2 more

Gigliola Vaglini is Full Professor of Computer Engineering at the University of Pisa's Department of Information Engineering. She teaches Software Systems Engineering and Databases, with research focusing on formal specification and verification of concurrent/distributed systems. Her work integrates model checking methodologies with machine learning techniques for industrial anomaly detection and mobile security applications. She has participated in multiple research projects funded by the Italian Ministry of University and Research, National Research Council, and European Community. Research Focus: Vaglini's work bridges theoretical computer science and practical applications, developing verification frameworks for complex systems while creating AI solutions for real-world problems. Her current investigations span swarm intelligence coordination, structural monitoring via digital twins, and multimodal deep learning architectures. Recent publications demonstrate cross-disciplinary approaches combining formal methods with machine learning, particularly in environmental monitoring, structural engineering, and neural architecture design. This reflects a consistent pattern of translating theoretical computer science concepts into applied technological solutions.

• Human-Centered Computing
• Digital Thinking Pedagogy
• Crowdsourcing Mechanics
• +4 more

Lisa Posch, a Research Fellow at the Institute of Human-Centred Computing at Graz University of Technology, focuses on digital thinking pedagogy, crowdsourcing mechanics, and social media analytics. Her work bridges computational methods with sociotechnical systems. Education: Dipl.-Ing. (Master of Science), Dr.techn. (Doctorate in Technical Sciences), BSc Her research spans Human-Centered Computing , Natural Language Processing , and Digital Humanities , with publications on crowdworker motivation, social media monitoring, and metadata modeling. She has contributed to advancements in probabilistic ontology mapping and multilingual topic modeling. Her recent publications (2013-2023) demonstrate expertise in social media analytics , digital labor dynamics , and semantic data systems . She actively engages in educational initiatives through the Teaching Digital Thinking Project (TDT).

Melinda HodkiewiczView profile

Professor

• Maintenance Engineering
• Asset Management
• Data Science
• +6 more

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)

Jules WhiteView profile

Professor

• Cybersecurity
• Mobile/Cloud Computing
• Cyber-physical Systems
• +3 more

Jules White is a Professor of Computer Science and Senior Advisor to the Chancellor for Generative AI in Education at Vanderbilt University's School of Engineering. He also holds an appointment as Associate Professor of Biomedical Informatics. His research focuses on cybersecurity, mobile/cloud computing, and cyber-physical systems. White has led the online MS in Computer Science program to #1 ranking by Fortune and commercialized research into industry products like PAR Works. Education: Ph.D./M.S. in Computer Science (Vanderbilt University), B.A. in Computer Science (Brown University). Research Interests: Cybersecurity, mobile computing, cyber-physical systems, healthcare informatics, and AI applications. His MAGNUM Group explores these areas with over 160 publications and industry collaborations. Articles Overview: Recent work spans AI-driven prompt engineering, healthcare transparency via NLP, and UAV design optimization. Themes include LLM applications, cybersecurity frameworks, and medical technology integration. Awards: NSF CAREER Award, CES Innovation Award (2013), and multiple best paper awards. Recognized for entrepreneurship with $12M venture funding for startups. Advising & Grants: Directed over 100 students in MOOCs and research. Secured grants totaling ~$1.5M annually, including NSF, AFRL, and industry partnerships. Focus areas include secure IoT systems and blockchain in healthcare. Labs & Teams: Directs the Mobile Application computinG, optimizatoN, and secUrity Methods (MAGNUM) Group. Collaborates with industry (e.g., Siemens, Cloudpoint/PAR Works) and government agencies (NSF, ARO).

Marek ReiView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• planning and reasoning with LLMs models
• +4 more

Marek Rei is an Associate Professor of Machine Learning at Imperial College London and a Visiting Researcher at the University of Cambridge . He serves as an AI Advisor for Locai Labs and Esgrid Technologies , while providing consultancy through Perception Labs . PhD in Minimally supervised dependency-based methods for natural language processing (University of Cambridge, 2013) MPhil in Computer Speech, Text and Internet Technology (University of Cambridge Computer Lab, 2008-2009) Bachelor's in Creating a Model for Audiovisual Speech in Estonian (Tallinn University of Technology) His research spans Machine Learning and Natural Language Processing , focusing on planning and reasoning with LLMs , model explainability , medical data modeling , information extraction , and grammatical error correction . Recent publications highlight trends in LLM robustness , membership inference , medical NLP , and corporate sustainability analytics . Scientific accolades include the Best Paper Award at the IEEE Conference on Secure and Trustworthy Machine Learning (SaTML 2025) . Collaborative work with institutions like Microsoft (via SwiftKey acquisition) and contributions to neural sequence labeling frameworks demonstrate his technical and industry impact.

Liane HaakView profile

Professor

• Enterprise Systems
• ERP Systems
• E-Business
• +6 more

Prof. Dr. Liane Haak is a Professor of Business Informatics at the Faculty of Management, Culture and Technology at Osnabrück University of Applied Sciences. She holds a PhD in Computer Science (Dr. rer. nat.) from Carl von Ossietzky University Oldenburg. Her research focuses on Enterprise Systems in Higher Education (ESHE), ERP systems, E-Business, and semantic integration of data warehouses and knowledge management systems. She has led international research projects like DEEBIS-Net, a bi-national PhD program with Cuba. Her work bridges academic research and practical IT system implementation in business contexts. Education: Industrial Management apprenticeship (1994-1997), Business Administration studies at Oldenburg University (1997-2001), followed by research roles at Oldenburg University's Department of Computer Science (2001-2010). Became professor in 2011. Active in developing educational frameworks for ERP system learning environments and adaptive learning concepts using technology-enhanced learning (TEL). Research interests include enterprise architecture, semantic interoperability, and leveraging technology for competence-building in higher education. Key contributions span enterprise resource planning (ERP), supply chain management (SCM), and web intelligence. Collaborates with international partners on doctoral education programs and system integration challenges.

• Data Science
• Business Analytics
• E-commerce
• +3 more

Dr. Ibukun Afolabi is an Assistant Professor in Computer Science at Northeastern University London, where she focuses on applying data science to solve business problems, particularly in e-commerce. Previously, she held a Senior Lecturer position at Covenant University for over 15 years. Her research integrates text analytics, network analytics, and data mining to address challenges in business analytics and return on investment optimization. She authored Data Science and Analytics for SMEs , a guide for small businesses on leveraging analytics. Education: PhD (Computer Science, 2012) from Covenant University; MSc (Computer Science, 2005) from the University of Ibadan; BSc (Engineering Physics, 2001) from Obafemi Awolowo University. Research interests include data-driven solutions for SMEs, semantic web mining, and competitive analysis. Her work spans retail, banking, and education sectors, with a focus on customer behavior prediction, decision support systems, and knowledge management. Recent publications emphasize SNA, machine learning models for business success, and ontology-based systems. Teaching experience includes courses like Database Management Systems, Data Science, and Data Mining at both Northeastern and Covenant University. She is affiliated with Northeastern’s CoMENS Faculty and contributes to global academic networks.

Guy De TréView profile

Associate Professor

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +7 more

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

• Computer Vision
• Machine Learning
• Natural Language Processing
• +3 more

Dr. Suman Kumar Ghosh is a Lecturer in Data Science and Computer Science at York St John University, affiliated with the York Business School in London. His research focuses on computer vision, machine learning, and natural language processing, with a decade of experience across diverse industries. He has contributed to advancements in handwritten character recognition, scene text understanding, and segmentation-free word spotting techniques. His work spans foundational research in document analysis, including contributions to datasets like the Versailles-FP for ancient floor plan analysis. He has published extensively in top-tier conferences such as ICDAR and CVPR, addressing challenges in word spotting, bi-gram indexing, and visual attention models for text recognition. His research bridges theoretical advancements with practical applications in multilingual systems and adaptive feature extraction. No scientific awards are explicitly mentioned in the profile. His teaching responsibilities include modules on machine learning and artificial intelligence concepts.

Ekaterina A RapinchukView profile

Assistant Professor

• Machine Learning
• Graph-Based Algorithms
• Data Classification
• +5 more

Ekaterina A Rapinchuk is an Assistant Professor in the Departments of Mathematics and Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. Her research focuses on developing graph-based algorithms and spectral methods for machine learning applications, particularly in small data classification, molecular science, and hyperspectral imaging. She holds a joint appointment across two departments, reflecting her interdisciplinary work at the intersection of mathematics and computational science. Research Interests : Graph algorithms for data classification and clustering Spectral graph theory and Laplacian-based methods Machine learning for molecular and biomedical data Optimization in high-dimensional spaces Applications of topological data analysis Publications Trends : Recent work emphasizes addressing data scarcity through graph-based techniques, integrating transformer networks with spectral methods, and correcting labeling errors in training datasets. Her research bridges theoretical mathematics with practical applications in sensor data analysis, chemical informatics, and biomedical imaging. Grants & Advising : While specific grant details are not listed, her work aligns with NSF-funded research areas in computational mathematics and data science. No advising information is currently available.

Sumaiya SuraveeView profile

Researcher

• Natural Language Processing
• Machine Learning
• Ontology Development
• +3 more

Sumaiya Suravee is a researcher at the Institute of Data Science, University of Greifswald, focusing on natural language processing, machine learning, and ontology development. She contributes to the BMBF-funded project eDEM-Connect, aiming to create AI-based tools for dementia caregivers. Education: M.Sc. in Electrical Engineering (Information Technology) from University of Rostock Experience: Researcher at University of Greifswald (2020–present), with prior work at PlanetAI GmbH and University of Rostock's Cognitive Methods group Research Interests: She specializes in extracting semantic structures from informal documents, developing annotation schemes, and creating ontologies for healthcare applications. Her work bridges NLP, machine learning, and medical informatics. Publications: Her recent work (2023–2025) focuses on named entity recognition, relation extraction, and chatbot systems for dementia care. Earlier contributions (2017–2021) include studies on biomedical modeling and semantic learning frameworks. Contact: Email: sumaiya.suravee@uni-greifswald.de

Helena GalhardasView profile

Associate Professor

• Databases
• Data Cleaning
• Data Integration
• +4 more

Helena Galhardas is an Associate Professor at the Department of Computer Science and Engineering at Instituto Superior Técnico, University of Lisbon , and a researcher at INESC-ID . Her work focuses on data cleaning, data integration, information extraction, and ETL systems , with applications in clinical data management and big data frameworks. Academic Affiliation: Associate Professor, Department of Computer Science and Engineering, Instituto Superior Técnico, University of Lisbon Research Unit: INESC-ID (Information and Decision Support Systems) Contact: helena.galhardas@tecnico.ulisboa.pt | +351 21 423 32 46 Her research spans theoretical and applied domains, including clinical data systems like Umedicine for structured medical data management and PiCaSSo for pediatric palliative care. She has developed frameworks like AJAX for data cleaning, REEL for relation extraction, and ∂u∂u for distributed duplicate detection. Her work integrates machine learning with database systems , particularly in workflow optimization and one-to-many data transformations. Scientific contributions include: Best Student Paper Award (2009) Nomination for Best Paper Award (2009) Research projects: PiCaSSo (2025-26): Pediatric Palliative Care Support System LAIfeBlood : AI for Blood Management Data2Help : Data Science for EMS Optimization DataStorm : Cloud-based Data Management

WolffgangView profile

Professor

• Artificial Intelligence
• Deep Learning
• Natural Language Processing
• +7 more

Professor Wolffgang is an academic at the University of Münster, affiliated with the Institute of Customs and International Trade Law. He coordinates international conferences on protectionism and tariff reforms, and has made significant contributions to artificial intelligence applications in medical diagnostics and economic modeling. His research spans two primary domains: Medical Informatics: Applying AI/ML to prostate cancer diagnosis and EHR data extraction Economic Simulation: Developing multi-agent systems and modeling frameworks Recent publications demonstrate his work in: Interpretable AI for histology image analysis Automated data extraction from medical records Model-driven web application development Professor Wolffgang actively participates in international academic discourse, most recently moderating panels on customs reform during global economic shifts.

Sergio Rodriguez MendezView profile

Research Fellow

• Knowledge Graphs
• Ontology Engineering
• Linked Data & Semantic Web
• +7 more

Sergio Rodriguez Mendez is a Research Fellow in Knowledge Graph Engineering at the School of Computing, Australian National University. His work focuses on advancing ontology engineering, linked data, semantic web technologies, and their applications in domains like astronomy, brain-computer interfaces, and IoT. He is a member of the Australian Government Linked Data Working Group (AGLDWG) and the W3C Knowledge Graph Construction Community Group, and holds a senior role at the Software Innovation Institute (SII). His research interests include Knowledge Graphs, Ontology Engineering, Linked Data, Data Science, Machine Learning, Natural Language Processing, Brain-Computer Interfaces, IoT, Cloud/Fog/Edge Computing, and Software Engineering. He has contributed to frameworks like pathfinder for astronomical literature review and Doc-KG for document-to-KG conversion. Recent work emphasizes integrating large language models (LLMs) with knowledge graphs, such as AstroLLaVA for astronomical data unification and hybrid frameworks for entity linking. His publications span conferences like WWW, JCDL, and the ACM/IEEE Joint Conferences. He actively supervises research students and is involved in initiatives like the ASKG project for enriching scholarly knowledge graphs. His work addresses challenges in semantic data representation, automated query processing, and syntactic complexity reduction, with a focus on domain-specific applications in science and cultural heritage.

Varish MulwadView profile

Researcher

• Information Extraction
• Knowledge Graph Population
• Semantic Web
• +4 more

Varish Mulwad is a Senior Scientist at GE Research with 14 years of experience in algorithm development and knowledge graph construction from structured/unstructured data. He holds a Ph.D. and M.S. in Computer Science from the University of Maryland, Baltimore County (UMBC), where he worked under Prof. Tim Finin and collaborated with Prof. Anupam Joshi, and a B.E. in Computer Engineering from University of Mumbai. Ph.D. & M.S. in Computer Science (UMBC) B.E. in Computer Engineering (University of Mumbai) His research focuses on semantic interpretation of tabular data through linked data frameworks, information extraction from unstructured text, and knowledge graph population using probabilistic reasoning and graphical models. He has pioneered domain-independent systems for table interpretation and developed novel methods for cloud SLA automation and cybersecurity threat detection via social media analysis. Recent work trends include: Context-aware web table annotation Relational table representation learning Linked data generation from spreadsheets Pre-training/fine-tuning paradigms for web tables Application of semantic web standards to cybersecurity He has led 3-4 member project teams in developing production-ready solutions, contributed to 19 peer-reviewed publications, and secured 7 patents with 900+ citations. During his academic tenure at UMBC's Ebiquity Research Lab, he co-developed the TABEL framework for table semantics inference and produced the first interactive system for meta-analysis report generation from linked data.