Find Academic Supervisors

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Answer Set Programming
• +5 more

Zeynep Gözen Saribatur Yaman is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Technische Universität Wien (TU Wien). Her role is supported by the Austrian Science Fund (FWF) as a Projektassistentin (Dr.in techn.). She is affiliated with the DBAI group and contributes to multiple research projects including AURA (2022–2026), DynaCon (2017–2020), AI4EU (2019–2021), and HumanE-AI-Net (2020–2024). Her primary affiliation is with TU Wien’s Faculty of Informatics, where she focuses on advancing explainable AI through abstraction techniques in logic-based systems. Zeynep holds a Doctorate in Technical Sciences (Dr.techn.) from TU Wien (2019), where her dissertation addressed Abstraction for reasoning about agent behavior with answer set programming . She also holds an MSc in a relevant field, though its specifics are not explicitly detailed in the text. Her research spans multiple funded initiatives, emphasizing both theoretical contributions and applied work in robotics and agent systems. Her research interests revolve around abstraction mechanisms in Answer Set Programming (ASP), argumentation frameworks , and their applications to explainable AI , robotics planning , and agent behavior modeling . She explores techniques to reduce complexity in logic-based systems while preserving critical reasoning aspects, with a focus on making AI systems more transparent and understandable. Zeynep has contributed to several projects aiming to enhance AI reasoning through abstraction. Her work bridges formal methods and practical AI challenges, such as reasoning about dynamic environments and multi-agent systems. She actively participates in international conferences and workshops, including KR, AAMAS, ICAPS, and EPIA, where she presents advancements in knowledge representation and reasoning. Her advising record is not explicitly stated in the provided texts. She has collaborated on grants from FWF, EU Horizon 2020, and other competitive funding bodies. Her research also intersects with cognitive factories and hybrid reasoning systems for robotics applications. As part of the DBAI group at TU Wien, she contributes to the development of AI tools and methodologies that prioritize comprehensibility and scalability. Her lab affiliations include the Knowledge-Based Systems Group (DBAI), where she works on theoretical and applied AI challenges.

Anne-Marie GeorgeView profile

Associate Professor

• Preference Learning
• Preference Modeling
• Computational Social Choice
• +4 more

Anne-Marie George is an Associate Professor at the University of Oslo, affiliated with the Scientific Computing and Machine Learning department. Her research focuses on handling user preferences through modeling, elicitation, inference, and fairness in group decision-making systems. Academic Rank: Associate Professor Department: Scientific Computing and Machine Learning Email: annemage@ifi.uio.no Her work spans computational social choice, preference learning, and algorithmic fairness, with recent publications addressing dynamic resource allocation, robust recourse in binary problems, and fair voting procedures. Collaborations with researchers like Christos Dimitrakakis highlight interdisciplinary efforts in AI ethics and decision theory. Recent trends in her publications include: Dynamic allocation fairness Preference-based decision systems Explainable AI for resource distribution Ontology engineering applications Robustness in allocation algorithms Multiwinner voting optimization

• Big Data
• Data Storage Technology
• Spatial Data Management
• +9 more

Thomas Heinis is a Professor in Computing at Imperial College London. He leads the SCALE Lab and conducts research in DNA data storage and high-performance data analytics. His academic journey includes a Ph.D. and M.Sc. in Computer Science from ETH Zürich, a Postdoctoral fellowship at EPFL’s DIAS Lab, and a Fulbright Scholarship at Purdue University. He specializes in scalable data management techniques for scientific and spatial datasets, novel hardware optimization, and interdisciplinary applications such as medical data analysis. Education: Ph.D. and M.Sc. in Computer Science, Swiss Federal Institute of Technology in Zürich (ETH Zürich) Postdoctoral Fellow, DIAS Lab, EPFL Fulbright Scholarship, Purdue University (2002–2004) Research Interests: Big Data and Distributed Processing Spatial Data Indexing and Visualization High-Performance Computing (HPC) Data Analytics Data Management on Novel Hardware (e.g., neuromorphic systems) Synthetic DNA Storage Technology Interdisciplinary Applications in Medicine and Neuroscience Recent Work Trends: Heinis’s publications emphasize innovative storage solutions like Motif-based DNA encoding, efficient spatial indexing algorithms (e.g., FLAT, SCOUT, TOUCH), and machine learning-driven approaches for healthcare and scientific data analysis. His work bridges computational methods with real-world applications in neuroscience, medicine, and environmental science. Scientific Awards: SystemsX Interdisciplinary Ph.D. Fellowship (2007) Finalist, Venture Leaders Entrepreneurship Competition (2007) Finalist, Purdue Burton D. Morgan Entrepreneurship Competition (2003) Fulbright Scholarship (2002–2004) Advising & Grants: Supervises Ph.D. students in scientific data management, spatial data, and DNA storage. Funding opportunities include Marie-Curie post-doctoral fellowships, CSC Imperial Scholarships, and others. His lab actively collaborates with institutions like the Blue Brain Project and explores scalable tools for data-driven research. Labs & Teams: Leader of the SCALE Lab at Imperial College. Collaborations with the Blue Brain Project (BBP) on neuroscientific data management.

Satish BukkapatnamView profile

Professor

• Smart Manufacturing
• Manufacturing Process Monitoring
• Cybersecurity in Manufacturing
• +3 more

Dr. Satish Bukkapatnam is a Professor of Industrial & Systems Engineering at Texas A&M University, holding dual titles as Regents Professor and Rockwell International Professor. He leads the TEES Institute for Manufacturing Systems and is affiliated with Multidisciplinary Engineering and Biomedical Engineering. His research focuses on smart manufacturing, leveraging sensor data for process monitoring and prognostics in ultraprecision machining, nanomanufacturing, and wearable health sensors. With over 160 peer-reviewed publications and $6.5M in grants, his work bridges machine learning, materials science, and cybersecurity in manufacturing. Education: Ph.D. in Industrial & Manufacturing Engineering from Pennsylvania State University (1997). Research interests span smart manufacturing systems, including real-time process monitoring, anomaly detection, and materials discovery. He emphasizes cybersecurity in Industry 4.0, additive manufacturing, and explainable AI for quality assurance. Key applications include fusion target polishing, surface finishing, and health-related wearable devices. His articles highlight innovations in defect detection, digital twin integration, and autonomous experimentation. Awards include the Fulbright-Tocqueville Chair (2019-20), IISE Fellowships, and top paper recognitions. Dr. Bukkapatnam has advised numerous students, leading to best-paper/poster awards. His grants involve NSF, DOD, and industry partnerships. He pioneered cybersecurity frameworks for manufacturing-as-a-service and developed methods for spatial property control in 3D printing. Labs/Teams: Director of the TEES Institute for Manufacturing Systems, leading interdisciplinary teams in cybermanufacturing and materials innovation.

Giovanni SteaView profile

Associate Professor

• Quality of Service
• Wireless Networks
• Network Calculus
• +3 more

Giovanni Stea is an Associate Professor at the Department of Information Engineering within the College of Engineering at the University of Pisa. His research focuses on Quality of Service, resource allocation in wireline and wireless networks, performance evaluation through simulation and analytical techniques, and traffic engineering. Dr. Stea's research spans network calculus, wireless systems, and telecommunications infrastructure. His recent work demonstrates strong emphasis on 5G/6G networks, AI integration in telecommunications, and optimization of network performance. Key trends include: Advancements in network calculus methodologies Innovations in mobile edge computing applications Development of trustworthy AI systems for next-generation networks Cross-disciplinary approaches combining telecommunications and machine learning His publications consistently address computational efficiency in network analysis and practical implementations of theoretical models in real-world telecommunications environments.

Marcello BalducciniView profile

Associate Professor

• Knowledge Representation & Reasoning
• Ontologies
• Agent Architectures
• +6 more

Marcello Balduccini is the Department Chair and Associate Professor of Decision and System Sciences at Saint Joseph's University's Erivan K. Haub School of Business. His research focuses on knowledge representation & reasoning, ontologies, agent architectures, and cybersecurity applications in cyber-physical systems (IoT) and cognitive robotics. He previously held roles as an Assistant Research Professor at Drexel University and Principal Research Scientist at Kodak Research Labs. Research Interests: Knowledge Representation & Reasoning Cyber-Security and Cyber-Analytics Ontology-Based Systems Natural Language Understanding Constraint Satisfaction Problems Trustworthiness in AI/Robotics Recent work emphasizes explainable AI (XAI) systems for Answer Set Programming (ASP), cybersecurity frameworks, and formal methods for cyber-physical systems. His over 100 publications span conferences like LPNMR and ICLP, addressing topics from actual causation to autonomous UAV mission planning. Dr. Balduccini has organized international conferences and received grants supporting AI research, including travel grants for knowledge representation conferences. His work bridges theoretical advancements with practical applications in smart grids, supply chain management, and SDG-aligned AI systems.

• Learning Analytics
• Artificial Intelligence
• Explainable AI (XAI)
• +5 more

Mohamed Abdelgalil is an Associate Professor of Computer Science at the University of Eastern Finland (UEF), leading the Learning Analytics Unit —recognized as Europe’s most productive laboratory in his field over the last five years and globally ranked second in 2024. His academic journey includes a PhD in Learning Analytics from Stockholm University (Best Thesis Award), postdoctoral roles at Université Paris Cité, KTH Royal Institute of Technology, and UEF, and a Docent title in Learning Analytics from the University of Oulu . Research Focus: Precision Explainable AI (XAI), network science, and methodological innovations for personalized, equitable AI-driven learning environments. He developed the tna software for dynamic process analytics and learning networks. Publications: Over 190 works, including journals like British Journal of Educational Technology and IEEE Transactions on Learning Technologies , with recent studies on emotional regulation in collaborative learning, AI interaction modeling, and accessibility in virtual labs. Awards: Europe’s Emerging Scholar Award (2023), Best Paper awards at LAK, ICCE, SITE, and TEEM. Leadership: Serves on editorial boards of leading journals, chairs conference tracks, and delivers keynotes. Collaborates with global institutions, governmental bodies, and industry partners.

• Adaptive Learning Systems
• Applied Artificial Intelligence
• Learning Analytics
• +12 more

Professor Alexandra I. Cristea is a faculty member in the Department of Computer Science at Durham University, serving as Deputy Executive Dean (Postgraduate Research) in the Faculty of Science. She founded the Artificial Intelligence in Human Systems research group and leads the SCENE Lab , focusing on adaptive, personalized, and gamified educational technologies. Alan Turing Academic Liaison for Durham N8 CIR Digital Humanities Team Lead IEEE European Public Policy on ICT Member Research Interests include adaptive learning systems, gamification frameworks, user modeling, semantic/social web applications, and AI-driven educational analytics. Her work spans learning analytics, VR/XR environments, and ethical AI, with over 350 publications and 7100+ Google Scholar citations. Recent Publications highlight AI in education, including MOOC dropout prediction, XAI frameworks, VR safety training, and sentiment analysis tools. Notable collaborations include the Weizmann Institute, British Council, and industry partnerships with Distinctive Publishing. Scientific Awards BCS Fellow HEA Fellow IEEE Senior Member EATEL Founding Member Associate Editor roles at ACM, IEEE, and Frontiers Supervision involves mentoring postgraduate students in AI, educational technology, and data ethics. Her labs and teams, such as the SCENE Lab, drive innovations in neuro-enhanced education and cultural diversity initiatives like the TechUP project.

Mohammad MiralinaghiView profile

Assistant Professor

• Smart Cities
• Connected and Automated Vehicles
• Electric Vehicles
• +5 more

Mohammad Miralinaghi is an Assistant Professor in the Civil, Architectural, and Environmental Engineering Department at Illinois Institute of Technology (Illinois Tech), part of the Armour College of Engineering. His research focuses on smart cities, connected/automated vehicles, electric vehicles, and sustainable transportation systems. He holds a Ph.D. from Purdue University, an M.S. in Transportation Systems Engineering from the University of Alabama, and two M.S. and a B.S. from Sharif University of Technology. His expertise includes transportation infrastructure design, tradable credit schemes, and congestion pricing. Recent work explores AI-driven solutions for autonomous vehicle decision-making (XAI), robust design of electric vehicle charging networks, and reinforcement learning for infrastructure scheduling. He has published widely in journals such as Transportation Research Part D , European Journal of Operational Research , and Computer-Aided Civil and Infrastructure Engineering . Key projects include optimizing project scheduling to minimize user/business impacts and designing frameworks for electric vehicle adoption. His research addresses emerging technologies like CAVs (connected and automated vehicles), emphasizing equity, resiliency, and sustainability in urban systems.

• Anti-Money Laundering (AML)
• EU Law
• Financial Regulation
• +5 more

Georgios D. Pavlidis is an Associate Professor of International and EU Law at the School of Law of Neapolis University in Cyprus. He holds a UNESCO Chair (Project: “Human development, security and the fight against transnational crime”) and a Jean Monnet Chair (Project: “Tracing Criminal Assets in the European Union”). He obtained his PhD in Law from the University of Geneva, with postgraduate studies in the U.S. (SMU Dallas) and the UK (University of Warwick). His research focuses on AML/CFT, digital finance, AI governance, and asset recovery. He has advised the European Commission, served as an AML expert for global initiatives (e.g., Stanford’s Digital Currency Global Initiative), and participated in EU-funded projects like the COST Action on Globalization and Illicit Trade. His recent work explores AI’s role in law enforcement, EU AMLA reforms, and challenges in cross-border asset confiscation. He coordinates the Jean Monnet Center of Excellence AI-2-TRACE-CRIME and collaborates with institutions like the Egmont Centre of FIU Excellence. His publications analyze FATF standards, EU digital finance strategies, and legal frameworks for stablecoins.

Stephen McArthurView profile

Professor

• Artificial Intelligence (AI)
• Power and Energy Systems
• Condition Monitoring
• +4 more

Stephen McArthur is the Executive Dean of Engineering and Distinguished Professor of Intelligent Energy Systems at the University of Strathclyde. He holds a BEng(Hons) and PhD in Electronic and Electrical Engineering from the same university. His research focuses on AI applications in power systems, including smart grids, nuclear plant monitoring, and distributed intelligence architectures. He led the UKRI EnergyREV program, a major initiative advancing smart local energy systems, and directed the EPSRC Centre for Doctoral Training in Future Power Networks. As co-founder and CTO of Bellrock Technology, he developed the Deckchair platform for AI-driven solutions across multiple industries. Education: PhD (1996) and BEng (1992) in Electronic and Electrical Engineering from the University of Strathclyde. Research Interests: AI in energy systems, condition monitoring, explainable AI, and nuclear plant lifetime extension. He has pioneered diagnostic systems using multi-agent architectures and knowledge-based reasoning. Scientific Awards: 2021 IEEE Richard Harold Kaufmann Award, IEEE Technical Committee Prize Paper Award. Projects include SF6 Escape Prediction for HV Switchgear and Advanced Substation Monitoring. Advising & Grants: Supervised 13 doctoral students and led multi-million-pound grants, including EnergyREV. Academic Director of the Advanced Nuclear Research Centre. Labs/Teams: Bellrock Technology (Deckchair platform), Strathclyde-Imperial College CDT in Future Power Networks, and collaborations with National Grid.

• Multimedia Quality Perception
• Personalized Quality-of-Experience Measurement
• Health Technology
• +4 more

Robert P. Spang is a researcher at the Technical University of Berlin, affiliated with the Quality Research Group and UXXR Research Group at QULab. He leads the JF experiment and teaches courses in Media Informatics and supervises projects. His research focuses on multimedia quality perception, personalized QoE, health technology, and dementia care using GPS analytics. Robert holds a degree in Computer Science from TU Berlin and earned his Dr. rer. nat. (PhD) through interdisciplinary studies in psychology, cognitive neuroscience, and biophysics. His work bridges computer science with healthcare applications, including outpatient dementia care and mobility analysis via GPS data. His recent publications span topics such as user state factors in multimedia QoE, the impact of hunger on perception, and noise-canceling technology effects on mental workload. He has contributed to health tech projects like the DemTab study evaluating tablet-based dementia care interventions in primary care settings. Robert advises students in Media Informatics and Technology programs and leads the JF experiment at QULab. Specific grants or funding details are not provided in the text. He is associated with the Quality and Usability Lab, contributing to both research and educational initiatives, including a free video course on geospatial data science using high-performance computing clusters.

• Computer networks and communications
• Cybersecurity
• Industry 4.0
• +7 more

Guido Marchetto is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino , where he conducts cutting-edge research in computer networks, cybersecurity, machine learning, and Industry 4.0. He is a member of the NETGROUP - Computer Networks Group , the SmartData@PoliTO interdepartmental center, and leads research at ACS LAB and LABINF . He serves as Deputy Coordinator of the Doctoral College in Computer and Systems Engineering and is the Scientific Advisor for the European Technology Platform for High Performance Computing (ETP4HPC). PhD Students: Federico Rinaudi, Doriana Monaco, Antonino Angi Research Groups: NETGROUP, SmartData@PoliTO Labs: ACS LAB, LABINF His research focuses on intelligent network management, explainable AI for networking, reinforcement learning, federated learning, and softwarized networks. He has led multiple EU and commercial research projects such as MIRANDA and DESIRE , with applications in cybersecurity, edge computing, and digital twins. Marchetto’s recent publications reveal a strong trend toward integrating AI/ML techniques—especially reinforcement learning and language models—into network automation, traffic engineering, and IoT systems. His work bridges theoretical innovation with practical implementation in cloud, edge, and telco environments. Scientific Awards: Best Paper Award Finalist, IEEE HPSR (2014) Best Paper Award, IEEE ICC (2007) He is a Senior Member of IEEE and serves on the editorial board of IEEE Transactions on Vehicular Technology . He has also been a Contract Professor at the Turin Polytechnic University in Tashkent from 2012 to 2022. His extensive grant leadership includes EU Horizon projects and numerous commercial research contracts with a focus on network intelligence and automation. He is actively involved in doctoral education and curriculum development, contributing to multiple degree programs and supervising ongoing PhD research in AI-driven networking and cybersecurity.

Soheyla AmirianView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +5 more

Dr. Soheyla Amirian is an Assistant Professor at the Seidenberg School of Computer Science and Information Systems at Pace University. She leads the Applied Machine Intelligence Initiatives & Education (AMIIE) Laboratory, collaborating with a multidisciplinary team on AI algorithm development for real-world applications in public health, imaging informatics, and AI-powered education. Previously, she served as a Faculty Fellow at the Institute for Artificial Intelligence and a Faculty Lecturer at the School of Computing, University of Georgia. Dr. Amirian earned her BSc, MSc, and PhD in Computer Science from the University of Georgia in 2021, specializing in AI and Deep Learning computer vision. Her research focuses on Explainable, Interpretable, and Accountable AI with applications in Health Informatics. PhD, University of Georgia, 2021 BSc, MSc in Computer Science Dr. Amirian's research spans technical domains of Explainable AI, Deep Learning, and Few-Shot Learning, with application domains in Health Informatics. She has extensive experience in AI fairness, particularly in medical imaging informatics where she addresses bias in AI-powered segmentation of orthopedic images. Her recent work focuses on Fair AI-powered orthopedic image segmentation, AI fairness in Hip Bony Anatomy segmentation, and explainable AI in healthcare applications. Dr. Amirian's research has been supported by grants from NIH/National Institute on Aging (NIA) and the Helene T. and Grant M. Wilson Center for Social Entrepreneurship. She has received numerous accolades including the 2024 PennAITech award, 2023 IEEE Atlanta Section Outstanding Educator Award, and the 2019 CSCI Outstanding Achievement award. She actively contributes to academic service as a Program Committee member at IEEE ICHI and Co-Chair of Research Tracks at CSCE and CSCI conferences. Dr. Amirian also serves as a keynote speaker at major academic events including Seidenberg Annual Research Day and CSCI conferences.

• Artificial Intelligence
• Explainable AI
• Case-Based Reasoning
• +4 more

Professor Nirmalie Wiratunga is a leading academic at Robert Gordon University, focusing on artificial intelligence and its applications across diverse domains. Her work bridges theoretical advancements with practical implementations in areas requiring high explainability and domain-specific adaptation. Research Pillars : Explainable AI (XAI), Case-Based Reasoning (CBR), Legal AI, Healthcare Informatics, and Machine Learning. Methodological Focus : Retrieval-Augmented Generation (RAG), domain adaptation, dialogue understanding, and systematic review automation. Recent publications highlight her contributions to neonatal pain assessment transparency, legal question answering systems (SCaLe-QA and CBR-RAG), and multi-shot explanation platforms (iSee). Her work on hallucination detection and federated learning emphasizes robustness and privacy preservation. Collaborations span biomedical, legal, and energy sectors, including mini-grid optimization for rural communities. Her research integrates context-aware systems into data-to-text generation and dual-task dialogue understanding, pushing boundaries in human-AI collaboration. Projects like Agentic CBR demonstrate practical implementations for financial decision-making with counterfactual explanations.