Find Academic Supervisors

• Cognitive Psychology
• Communication Studies
• Visual Perception
• +4 more

Dr. Hans Westerbeek serves as an Assistant Professor in the Language and Communication group within the Department of Languages, Literature and Communication at Utrecht University's Faculty of Humanities. His academic work bridges cognitive psychology, communication studies, and visual design with a particular focus on data visualization and visual communication. Westerbeek completed his PhD at Tilburg University in 2016 with a dissertation titled 'Visual realism: Exploring effects on memory, language production, comprehension, and preference.' His research career demonstrates a consistent trajectory from cognitive aspects of visual perception toward practical applications in information design and data communication. His research interests encompass cognitive psychology, communication studies, visual perception, visual design, and information visualization. Westerbeek's work particularly examines how visual elements affect comprehension, memory, and decision-making processes. His current research includes a Netspar Topicality project on visual communication of expected pension benefits and supervision of a PhD project addressing challenges in communicating about changes in the Dutch pension system. Analysis of his publication history reveals a strong focus on the intersection of visual design principles and cognitive processing. His work spans from fundamental research on visual perception and language production to applied studies on data visualization in domains including sports statistics, urban tourism, and financial communication. The recent shift toward pension communication demonstrates his ability to apply visualization principles to complex real-world communication challenges. As an educator, Westerbeek teaches CIS bachelor-level courses including CIW-lab and Rhetorics of Data Visualization, as well as the master-level Data Analysis and Visualization course. He also supervises theses and internships, mentoring students in the practical application of visualization principles. His technical expertise includes quantitative and qualitative research methods, statistical analysis, and programming skills in Bash, JavaScript, HTML, and Python. These technical capabilities support his research in creating and evaluating visual communication solutions across various domains.

E. EisemannView profile

Professor

• Computer Science
• Computer Graphics
• Virtual Reality
• +4 more

E. Eisemann is a Professor at the Computer Graphics and Visualisation department within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology (TU Delft). Their research focuses on computer graphics, virtual reality, and 3D modeling, with recent publications addressing ray-box intersections, neural scene representations, and interactive modeling systems. Current research trends include Advancements in real-time rendering algorithms Applications of neural networks for 3D scene reconstruction Innovative approaches to human-computer interaction in VR environments Optimization techniques for large-scale environment rendering Research output spans 164 publications, with recent work appearing in venues like Computer Graphics Forum and ACM SIGGRAPH conferences. Supervised work includes 18 formal advisees.

Zerrin YumakView profile

Assistant Professor

• Virtual Humans
• Social Robotics
• Affective Computing
• +3 more

Zerrin Yumak is an Assistant Professor at the Department of Information and Computing Sciences, Faculty of Science, Utrecht University, specializing in virtual humans and social robotics. She leads Task 3.2: Embodiment and Physical Interaction in the RAGE project. Her research focuses on Social and emotional behavior analysis for virtual characters Machine learning applications in motion capture data Constrained motion graph search algorithms Lip-sync and gesture animation generation Real-time multimodal interaction systems Notable projects include the Virtual Human Controller asset for Unity 3D, integrated with BiP Media and Randstad applications. Recent work spans 2025: SemGes for semantics-aware gesture generation 2024: ProbTalk3D emotion-controllable facial animation 2023: Diffusion model-based facial animation (Facediffuser) 2020: Music-driven expressive gesture generation She contributes to workshops like GENEA (2021-2022) and MASSXR (2025), advancing non-verbal behavior modeling for embodied agents.

Antonios LiapisView profile

Associate Professor

• Artificial Intelligence
• Procedural Content Generation
• Computational Creativity
• +5 more

Antonios Liapis is an Associate Professor at the Institute of Digital Games, University of Malta. He completed his PhD in September 2014 under the supervision of Georgios N. Yannakakis at the IT University of Copenhagen. His academic journey includes an M.Sc. in Information Technology from the same institution and a 5-year Diploma in Electrical and Computer Engineering from the National Technical University of Athens. Dr. Liapis has held various academic positions at the University of Malta: Post-doctoral Researcher (2014-2015), Lecturer (2015-2020), Senior Lecturer (2020-2022), and currently Associate Professor (2022-present). He has served as General Chair for multiple international conferences including FDG (2020), GALA (2019), and EvoMusArt (2018-2019). He is an Associate Editor of the IEEE Transactions on Games and a member of the Games Technical Committee of the IEEE Computational Intelligence Society. His research focuses on Artificial Intelligence as an autonomous creator and as a facilitator of human creativity. Key areas include computationally intelligent tools for game design and computational creators that blend semantics, visuals, sound, plot, and level structure to create various game genres including horror, adventure, shooter, and dungeon crawler games. His work has resulted in over 150 peer-reviewed publications and several research awards. Dr. Liapis has secured multiple research grants from the European Commission, including projects on AI-powered robotic material recovery, virtual reality aided design, and learning science through coding and play. His notable project series "Data Adventures" demonstrates the use of open data from Wikipedia, DBpedia, Wikimedia Commons, and OpenStreetMap to automatically generate adventure games with complete plots, characters, items, and locations. His scientific contributions have been recognized with several awards including Best Paper Awards at major conferences, Best Reviewer Award, and Runner-Up Best Student Paper Award. Dr. Liapis has also co-organized 16 workshops in diverse conferences throughout his career. Research interests include: Artificial Intelligence for creative applications Procedural Content Generation in games Computational Creativity systems Machine Learning for game design Affective Computing in virtual environments Human-AI collaboration in creative processes His recent work shows a strong trend toward integrating Large Language Models with game design, exploring quality diversity algorithms for creative applications, and advancing affect modeling for improved player experience. The research spans computer science, artificial intelligence, game studies, and human-computer interaction, with practical applications in education, entertainment, and design.

Peter DenningView profile

Professor

• Computational Science
• Computer Architecture
• Computer Education
• +20 more

Peter Denning serves as Professor of Computer Science at the Naval Postgraduate School, where he maintains active research and teaching responsibilities. He previously held leadership roles as president of the Association for Computing Machinery (ACM) and editor-in-chief of Communications of the ACM, and currently serves as editor of ACM Ubiquity. His prolific scholarly output includes ten authored books, notably "Great Principles of Computing" (MIT Press, 2015), and over four hundred scientific publications spanning decades of contribution to computing disciplines. Denning's research encompasses an exceptionally wide spectrum of computer science domains, ranging from foundational areas like Computer Architecture, Programming Languages, and Operating Systems to applied fields including Data Mining & Machine Learning, Security & Privacy, and Human-Computer Interaction. His interdisciplinary work extends into Science Policy, Social Computing, and Emerging Technologies, reflecting both technical depth and societal engagement across computational theory, systems, and applications.

Ahmed ElazabView profile

Research Fellow

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +7 more

Ahmed Elazab serves as an Associate Researcher at Shenzhen University's School of Biomedical Engineering since January 2021, following a Postdoctoral Research Fellowship at the same institution from January 2018 to April 2020. He holds a Ph.D. in Pattern Recognition and Intelligent Systems from the Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences (2017). Education Ph.D. in Pattern Recognition and Intelligent Systems, Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences, China (2017) Research Focus Dr. Elazab's work centers on machine learning and deep learning applications in biomedical contexts, with specialized expertise in medical image analysis , brain anatomy analysis , and computer-aided diagnosis . His research integrates computer vision, bioinformatics, and data science to develop AI-driven solutions for complex medical challenges including neurodegenerative diseases and infectious outbreaks. Publication Trends Analysis of his recent publications (2020-2023) reveals a concentrated focus on deep learning for medical diagnostics, particularly in Alzheimer's disease staging from MRI data and COVID-19 detection from X-ray images. His work consistently incorporates domain-specific knowledge into neural architectures, with growing emphasis on generative models for medical image segmentation and vaccine development. Cross-cutting themes include handling multi-modal biomedical data and addressing real-world clinical constraints. Scientific Recognition Active Academic Editor for PeerJ Computer Science with 1,600 contribution points Reviewer for prestigious international journals across computer science and biomedical domains Author/co-author of over 80 peer-reviewed publications Academic Engagement Dr. Elazab maintains significant scholarly involvement through editorial work at PeerJ Computer Science, where he has handled manuscripts on deep learning applications in medical imaging since 2020. His extensive publication record demonstrates consistent research productivity, though specific grant funding or student supervision details are not documented in available sources. He contributes to multiple subject areas including Artificial Intelligence, Bioinformatics, and Computational Biology. Research Environment As part of Shenzhen University's School of Biomedical Engineering, Dr. Elazab operates within a multidisciplinary research ecosystem focused on AI-driven medical solutions. His work intersects with ongoing initiatives in medical image computing and computational diagnostics, leveraging institutional resources for biomedical data analysis without specified laboratory affiliations.

Gang MeiView profile

Associate Professor

• Numerical Simulation and Computational Modeling
• GPU Computing
• Machine Learning
• +5 more

Dr. Gang Mei is an Associate Professor in Scientific Computing within the School of Engineering and Technology at China University of Geosciences (Beijing), where he has held academic positions since 2014. His career progression includes Postdoctoral Researcher (2014-2016), Lecturer (Oct-Dec 2016), and current Associate Professor (since Jan 2017). His research bridges computational science and engineering applications with significant editorial contributions to computer science literature. Education: Ph.D. in Computer Science, University of Freiburg, Germany (2014) Research Interests: Dr. Mei specializes in Numerical Simulation and Computational Modeling, GPU Computing, Machine Learning, and Data Mining, with strong applications in Network Science and Spatial Information Systems. His work integrates Distributed and Parallel Computing techniques for large-scale scientific simulations, particularly in geospatial modeling and network analysis. The research demonstrates consistent focus on computational efficiency through hardware acceleration and algorithmic optimization across diverse domains including satellite imagery processing, financial event detection, and medical image classification. Publication Trends: His editorial portfolio reveals strong interdisciplinary patterns connecting computer science fundamentals with domain-specific applications. Recent works emphasize GPU-accelerated methods for data-intensive problems (2020-2022), spatial-temporal modeling (2019-2020), and network science applications (2021). The publications consistently address computational scalability challenges while maintaining practical relevance across geospatial, financial, medical, and engineering contexts. Professional Recognition: As an IEEE Member, Dr. Mei serves on editorial boards for IEEE Access and PeerJ Computer Science, reflecting peer recognition in computational fields. His editorial contributions span 15+ publications demonstrating expertise in evaluating cutting-edge computer science research. Academic Service: Beyond editorial work, Dr. Mei's service includes advising on computational methodology across multiple disciplines. His role as Academic Editor demonstrates commitment to scholarly communication, particularly in bridging theoretical computer science with practical engineering applications. No grant funding details were specified in available materials.