Find Academic Supervisors

• Human-Computer Interaction
• User Experience Design
• User-Centered Design
• +8 more

Veronika Vanden Abeele serves as a Senior Lecturer and Department Head of the Human-Machine Interaction (HCI) group within the Department of Computer Science at the Faculty of Industrial Engineering Sciences, KU Leuven. She additionally holds leadership roles as subdivision head of Subdivision 14 at Campus Group T Leuven and maintains active memberships in the Leuven Centre for Accessible Health Technology and DigiSoc – KU Leuven Institute for Digital Society. Her research expertise spans Human-Computer Interaction with core focus areas including user experience design, user-centered design, participatory design, and participatory action research. She extends these methodologies to motivational design, gamification, persuasive system design, and the development of game-based assessments, with specialized applications in design for ageing populations and health technology. Her work consistently emphasizes user empowerment through co-creation and accessible digital solutions. Analysis of her 2025 publications reveals dominant themes in explainable AI for healthcare contexts, gamified health interventions, and mobile health applications for chronic conditions. Key trends include user-centered explanation interfaces for AI systems, video games for cognitive impairment assessment, and empowering mHealth tools for musculoskeletal pain management and stroke rehabilitation. She supervises doctoral candidates including: M. Szymanski (Human-Centered Explainable AI) S. Keyaerts (Mobile health for musculoskeletal pain) Her research is funded by multiple competitive grants: ReDUX: Building children's resilience against deceptive digital interactions (2025-2027) - Promoter Explainable AI for older adults with service robots (2025-2029) - Promoter AI-driven stress management explainability (2025-2029) - Promoter Game-based biomarkers for cognitive disorders (2021-2025) - Promoter As leader of the Human-Machine Interaction research group at Campus Group T Leuven, she directs interdisciplinary collaborations with healthcare institutions and technology partners through the Leuven Centre for Accessible Health Technology and DigiSoc. Her team specializes in co-designing and evaluating interactive technologies that address real-world challenges in health, aging, and digital inclusion, with strong emphasis on participatory methodologies and user empowerment.

• Data Visualization
• Human-Centered AI
• Human-Computer Interaction
• +7 more

Huamin Qu is a Chair Professor and founding Dean of the Academy of Interdisciplinary Studies at the Hong Kong University of Science and Technology (HKUST) . He leads the VisLab and coordinates the Human-Computer Interaction (HCI) group within the Department of Computer Science and Engineering. His academic journey began with a BS in Mathematics from Xi'an Jiaotong University , followed by an MS and PhD in Computer Science from Stony Brook University . Founding Head of HKUST's Division of Emerging Interdisciplinary Areas (EMIA) Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ) Director of IPO and senior administration team member As a pioneer in data visualization and human-computer interaction , his research bridges urban computing , explainable AI , social media analysis , and E-learning . Recent work focuses on human-AI teaming , multimodal communication , and augmented reality applications . His publications reveal a trajectory from foundational graph visualization and volume rendering to cutting-edge AI-integrated visual systems . Scientific awards include induction into the IEEE Visualization Academy , IEEE VGTC Technical Achievement Award , and multiple best paper/honorable mention awards at top conferences like IEEE VIS, ACM CHI, and IEEE VAST. His lab has graduated 48 PhDs and 21 MPhil students , with 21 PhDs now faculty members at institutions including UC Davis, University of Minnesota, and Zhejiang University. Technologies developed by his group have been adopted by Microsoft , IBM , and Google . His work on projects like Pulse of HKUST and ATMSeer has received global media coverage from MIT News , IEEE Spectrum , and NHK TV . He has served as Associate Editor of IEEE TVCG and held leadership roles in major conferences including IEEE VIS , PacificVis , and VINCI .

• Privacy-friendly home-based monitoring
• Unobtrusive sensing solutions
• Activity recognition and classification
• +8 more

Dr. Idongesit Ekerete (FHEA, IEEE/IPEM member) is a Lecturer in Computing Science at the School of Computing , Ulster University. He serves as Lab Manager for the Pervasive Computing Research Centre and co-investigator in a £3.3M Advanced Research and Engineering Centre project focused on FinCrime. His academic background includes a B.Eng. (First Class) in Electrical/Electronic Engineering from University of Uyo (2011), M.Sc. in Biomedical Engineering from University of Strathclyde (2017), and a PhD in Unobtrusive Sensing from Ulster University (2021). Current role: Lecturer in Computing Science (Ulster University, since 2021) Past role: Lecturer in Electrical/Electronic Engineering (University of Uyo, 2011–2021) Professional: Vice-Chair of IEEE SMC UK-Ireland Chapter His research focuses on privacy-preserving home monitoring systems using unobtrusive sensing , sensor fusion , and AI activity modeling . Recent publications explore thermal sensor applications for mood detection, federated learning for financial crime detection, and gait analysis for elderly wellbeing. Key themes include human-computer interaction , digital twins , and control systems engineering applied to healthcare contexts. Scientific contributions include: Learning and Teaching Award 2024 for student support UNIUYO Best Graduating Student Award Prof. Hilary’s Academic Excellence Award He has authored 20+ research outputs and led collaborations across biomedical engineering, pervasive computing, and financial crime detection domains.

• Machine Learning
• Wireless Sensor Networks
• Data Mining
• +5 more

Wang-chien Lee is an active Associate Professor in Computer Science and Engineering, specializing in machine learning, data mining, and graph optimization. His work spans domains including social networks, wireless sensor systems, and location-based services. Key research focus areas: Recommendation systems, Graph neural networks, and Social network analysis Pioneering applications in traffic safety, VR configuration, and blockchain marketing His publications demonstrate expertise in transfer learning, deep learning frameworks, and heterogeneous network modeling. Recent work explores traffic crash prediction, social-aware VR systems, and NFT marketing optimization. Current projects include: Learning Latent Representations of Heterogeneous Information Networks Link Quality Estimation for Wireless Sensor Networks Community Clickthrough Model Development

• Explainable Artificial Intelligence (XAI)
• Counterfactual Reasoning
• Machine Learning
• +4 more

Prof. Mark Keane has served as Chair of Computer Science at University College Dublin since 1998. A cognitive psychology expert with a PhD from Trinity College Dublin, his career spans multiple institutions including the University of London, Open University, Cardiff University, and TCD. He has held leadership roles such as Director of ICT at Science Foundation Ireland (2004-2006) and Director General at SFI (2006-2007), overseeing €700M+ research investments. His work focuses on explainable AI, counterfactual reasoning, and sustainable agriculture applications. BA (UCD), PhD (TCD) in Cognitive Psychology Over 200 publications, H-index 45, 10,800+ citations Key advisor for Ireland's €3.7B Science, Technology & Innovation Strategy His research explores counterfactual explanations , semi-factual reasoning , and XAI in domains like dairy farming and climate resilience. Recent work combines machine learning with cognitive models to improve grass growth prediction and mastitis detection . He also investigates surprise theory through computational models. As Vice-President of Innovation & Partnerships at UCD (2007-2009), he drove academic-industry collaborations. Current affiliations include the Insight Centre for Data Analytics, where he leads AI research teams advancing case-based reasoning and deep learning integrations for explainable systems.

• Machine Learning
• Natural Language Processing
• Network Analysis
• +3 more

Dr. Derek Greene is an Assistant Professor at the School of Computer Science, University College Dublin, and a Funded Investigator at the Insight Centre for Data Analytics and the VistaMilk Research Centre. His research spans machine learning, natural language processing, and network analysis, with a focus on interdisciplinary applications in cultural analytics, smart agriculture, and political science. Dr. Greene has published over 60 research papers at international conferences and journals. His work includes developing methods for natural language processing , network analysis , and deep learning applied to diverse domains such as literary text mining, dairy industry monitoring, and political communication analysis. He leads projects integrating machine learning into cultural analytics, enhancing agricultural practices, and modeling policy agendas. The articles in his Google Scholar profile highlight a trend toward explainable AI , synthetic data generation , and network-based modeling . Key sub-fields include counterfactual explanations , transformer-based frameworks , temporal analysis of historical texts , and interdisciplinary knowledge transfer . His work bridges machine learning with applications in cultural studies , agriculture , and political science . Dr. Greene collaborates with institutions like the Insight Centre for Data Analytics and the VistaMilk Research Centre , integrating academic research with industry and policy needs. His funded investigator roles reflect ongoing support for applied research in data analytics and agricultural technology.

Simone LentiView profile

Assistant Professor

• Cybersecurity
• Visual Analytics
• Human-Computer Interaction

Simone Lenti is an Assistant Professor (Ricercatore RTDa) at the Department of Computer, Control, and Management Engineering of Sapienza University of Rome. He is an active member of the A.WA.RE research group , which specializes in visual analytics. Lenti obtained his Ph.D. in 2021 with a dissertation on visual analytics techniques for cybersecurity. His research bridges cybersecurity , visual analytics , and human-computer interaction , focusing on: Developing computational methods for vulnerability analysis (e.g., NLP for CVE relevance, smart contract taxonomies) Designing visual tools for threat detection (e.g., attack graphs, firmware fuzzing) Enhancing interpretability in data-driven systems (e.g., partial dependence analysis, process mining) Lenti's publications (2019–2025) demonstrate a consistent focus on applying visual analytics to cybersecurity challenges , with recent expansions into bioinformatics and education. Key trends include automated vulnerability management, human-centered explainability, and scalable threat modeling. Awards: IEEE VizSec 2018 Best Paper for contributions to cybersecurity visualization. He contributes to academic infrastructure through tools like easyDeclare (declarative process modeling) and BUCEPHALUS (business-centric cybersecurity analysis), emphasizing practical applications of his research.

• Data Science
• Big Data
• Natural Language Query Processing
• +4 more

Prof. Dr. Kurt Stockinger is a Professor of Computer Science at ZHAW School of Engineering and holds a doctorate at the University of Zurich . He serves as Head of the MAS Data Science program and co-leads the ZHAW Datalab . His research focuses on Intelligent Information Systems , bridging information systems, natural language processing, and machine learning. Affiliated with the University of Zurich, he contributes to Quantum Machine Learning and Open Data Exploration initiatives. Stockinger's educational background includes a PhD in Computer Science (University of Vienna & CERN), a Master in Business Informatics (University of Vienna), and a CAS in Didactics & Methodology (ZHAW). He has taught courses in Quantum Computing , Big Data for Natural Sciences , and Data Science programs at ZHAW and University of Zurich. His research spans Data Science , Big Data , Natural Language Query Processing , Knowledge Graphs , and Quantum Machine Learning . Recent publications focus on quantum autoencoders , hybrid quantum neural networks , and prompt engineering for knowledge graph question answering. He has developed frameworks like ScienceBenchmark for real-world NL-to-SQL evaluation and NQuest for natural language query exploration. Scientific awards include the Best Paper Award at 7th Swiss Conference on Data Science (2020) He leads major projects such as DataGEMS (Data Discovery Platform, Horizon Europe) Digital Health Zurich (Clinical Innovation Lab) INODE4StatBot.swiss (NL-to-SQL Translation) GraphQueryML (Graph Database Optimization) ScienceBenchmark (NL-to-SQL Evaluation) Stockinger's work intersects with computer vision , biomedical data , and industrial applications , demonstrated through collaborations with institutions like Lawrence Berkeley National Laboratory, CERN, and University of Washington. He has contributed to establishing QuantumBasel and ZHAW Datalab as research hubs.

Adam PererView profile

Assistant Professor

• Data Visualization
• Human-Computer Interaction
• Machine Learning
• +3 more

Adam Perer is an Assistant Professor at Carnegie Mellon University within the Human-Computer Interaction Institute . His research focuses on data visualization , machine learning , and human-AI collaboration systems, particularly in healthcare contexts. Co-directs the Data Interaction Group Area Papers Chair at IEEE VIS PhD in Computer Science from University of Maryland Former Research Scientist at IBM Research His work integrates visual analytics with machine learning to create interactive systems for clinical data analysis , AI interpretability , and exploratory data science . Recent publications demonstrate applications in epidemiology , ICU medicine , and child welfare decision-making . Key research themes: Designing collaborative AI interfaces Visualizing embedding spaces for ML Interactive clinical decision support tools Explainable AI systems evaluation Temporal data exploration techniques Awards include Best Paper Honorable Mentions at CHI and EuroVis, plus Most Reproducible Paper recognition. He advises multiple PhD candidates and teaches courses in Data Visualization and Interactive Data Science .

• Trustworthy Machine Learning
• Program Synthesis
• Formal Verification
• +3 more

Osbert Bastani serves as an Associate Professor in the Department of Computer and Information Science at the University of Pennsylvania. He leads the trustml@Penn research group and holds affiliations with the ASSET, PRECISE, and PRiML research centers, as well as PLClub. His academic work centers on developing reliable and interpretable artificial intelligence systems through interdisciplinary approaches combining programming languages, formal methods, and machine learning. He earned his Ph.D. in Computer Science from Stanford University under the guidance of Alex Aiken, followed by a postdoctoral position at MIT working with Armando Solar-Lezama. This foundation in both theoretical computer science and practical systems has shaped his research trajectory. Bastani's primary research areas include Trustworthy Machine Learning (focusing on robustness against adversarial attacks, fairness in algorithmic decision-making, and explainable AI), program synthesis, and formal verification. His recent publications address critical challenges in large language models, such as defending against jailbreaking attacks and ensuring trustworthy retrieval-augmented generation. He also develops methods for conformal prediction under distribution shifts and neurosymbolic program synthesis for complex tasks like web question answering. His teaching portfolio features advanced courses including CIS 7000: Trustworthy Machine Learning and CIS 4190/5190: Applied Machine Learning, where he integrates cutting-edge research into the curriculum. Through his research group, he mentors graduate students on projects spanning neurosymbolic programming, uncertainty quantification, and fairness in sequential decision-making. As an active member of Penn's research ecosystem, Bastani contributes to the ASSET center's mission of building secure systems, PRECISE's work on cyber-physical systems, and PRiML's machine learning initiatives, while collaborating with PLClub on programming language innovations.

Muhammad Bilal ZafarView profile

Professor

• Artificial Intelligence
• Machine Learning
• Fairness in AI
• +3 more

Professor Muhammad Bilal Zafar holds the Chair of AI and Society within the Department of Computer Science at Ruhr University Bochum. He maintains key affiliations with the Research Center for Trustworthy Data Science and Security, Cluster of Excellence CASA (as Principal Investigator), and the Horst Görtz Institute for IT Security. His research program centers on human-centric artificial intelligence with core expertise in: Fairness algorithms for bias mitigation Explainable AI systems Robustness testing in human-AI interaction Trustworthy machine learning deployment Professor Zafar develops technical solutions addressing challenges that emerge when AI models interact with human users across various application domains. Prior to his academic appointment, he gained significant industry experience as Senior Applied Scientist at Amazon Web Services (AWS) in Berlin and Research Scientist at Bosch Center for Artificial Intelligence in Renningen. His educational foundation includes a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University under the supervision of Krishna P. Gummadi and Manuel Gomez Rodriguez. He leads the AI and Society research team at Ruhr University Bochum, which investigates the societal implications of artificial intelligence while developing concrete technical approaches for responsible AI development. Prospective researchers can find current opportunities through the team's dedicated information page.