Find Academic Supervisors

• Robotics
• Algorithmic Automation
• Simulation
• +3 more

Prof. AF (Frank) van der Stappen is a Professor of Robotics and Automation at Utrecht University's Faculty of Science, Department of Information and Computing Sciences. He also holds a position at University College Roosevelt. His research focuses on robotics (grasping, manipulation, motion), algorithmic automation, simulation, computational geometry, and computer animation. He has been an IEEE Fellow since 2017. His work emphasizes geometric computing and its applications in automation, with contributions to form-closure grasping, path planning, and virtual character animation. Education: PhD in Motion Planning from Utrecht University (1994), MSc in Distributed Data Structures from Eindhoven University of Technology (1988). Research Interests: Robotics (manipulation/grasping), algorithmic automation, geometric algorithms, computer animation, and crowd simulation. His work bridges theoretical foundations with practical applications in manufacturing, virtual environments, and agricultural robotics. Awards: IEEE Fellow (2017). Advisees/Grants: No explicitly listed students, but extensive collaborative work with researchers like Elon Rimon, Ken Goldberg, and Mark Overmars. His research includes grants related to motion planning, caging, and automation. Labs/Teams: Involved in geometric computing and robotics teams at Utrecht University, contributing to Game Research themes and interdisciplinary projects.

Chinmay HegdeView profile

Associate Professor

• Machine Learning
• Algorithms
• Big Data
• +6 more

Chinmay Hegde is an Associate Professor at the Tandon School of Engineering, NYU, with a joint appointment in Computer Science and Electrical Engineering. His research focuses on machine learning, signal processing, and their applications in cybersecurity, imaging, and materials science. He leads the DICE Lab, exploring data-driven solutions for complex engineering problems. Previously, he held positions at Iowa State University and MIT. Education: Ph.D. in Electrical and Computer Engineering, Rice University (2012) M.S. in Electrical and Computer Engineering, Rice University (2010) B.Tech. in Electrical Engineering, IIT Madras (2006) Research Interests: Machine Learning, Algorithms, Big Data, Signal Processing, Generative Models, Scientific Computing. Awards: NSF CAREER Award (2018-2023) NeurIPS Top Reviewer Award (2019) MRS Open Data Challenge Award (2019) Ralph Budd Award for Best Thesis (2013) Labs & Teams: Director of the DICE Lab, collaborating with industry partners on projects like real-time deepfake detection (GOTCHA), generative models for microstructure design, and AI-driven materials science.

• Cognition-Aware Systems
• Intelligent Reading Interfaces
• Digital Health
• +3 more

Dr. Tilman Dingler is an Associate Professor in the Knowledge and Intelligence Design (KInD) section at the Faculty of Industrial Design Engineering (IDE), Delft University of Technology. He holds a PhD in Computer Science from the University of Stuttgart and has held roles at the University of Melbourne and Osaka Prefecture University. His research focuses on cognition-aware systems, intelligent reading interfaces, and digital health technologies. He is an expert in designing systems that adapt to cognitive states to enhance information processing and mitigate biases. Education: PhD in Computer Science, University of Stuttgart (Germany) Master of Science in Web Science, University of San Francisco (USA) Honours Degree in Technology Management, University of Munich (Germany) Research Interests: Cognition-Aware Systems: Sensing cognitive states like attention and alertness Bias-Aware Systems: Detecting and mitigating cognitive biases in decision-making Digital Health: Proactive monitoring systems for chronic disease management Intelligent Reading Interfaces: Enhancing readability and reading habits Article Trends: Recent work explores interventions to reduce social media addiction, AI-generated content for non-native readers, and tools for analyzing 360° video. His studies often bridge HCI, AI ethics, and health tech. Awards: Wolfgang Heilmann Award (twice) Fulbright Scholar Grants & Collaborations: Secured funding from NHMRC and Japan Science and Technology Agency. Leads projects like ODECO (Open Data Ecosystem) and SmartHeart (home-based heart failure monitoring). Labs & Teams: Active in IDE’s Human-Centred AI and Sustainable Design Engineering groups. Co-founded the SIGCHI Melbourne Local Chapter.

Usman AlimView profile

Associate Professor

• Computer Graphics
• Visualization Techniques
• Volume Rendering
• +6 more

Usman Alim is an Associate Professor in the Department of Computer Science at the University of Calgary, and Director of the Data Science Program. He holds a dual Bachelor's in Mathematics and Physics from the University of Rochester (2001), an M.Sc. in Computer Science from Rochester Institute of Technology (2007), and a Ph.D. in Computer Science from Simon Fraser University (2012). His research focuses on global illumination, physically-based modeling, and signal processing applications in computer graphics and visualization. He is particularly noted for innovations in volume visualization, VR interfaces, and haptic systems. Key research areas include: Interactive visualization systems for multivariate data Uncertainty visualization in geological and medical datasets Efficient rendering techniques for large-scale datasets Low-code frameworks for data workflow automation Notable achievements include winning the 2019 IEEE SciVis Contest's Best Report Cover and multiple awards in Compute Canada's Visualize This contest. His work bridges theoretical foundations with practical applications in fields like environmental science, healthcare, and urban analytics. Current initiatives include advancing haptic interaction design, developing cloud-deployable medical imaging pipelines, and exploring AI-driven solutions for oil sands analysis. He teaches graduate courses like DATA 601 (Data Visualization) and special topics in computer science, contributing to both academic research and applied data science education.

• Scientific Visualization
• Medical Visualization
• Computer Graphics
• +4 more

Eduard Gröller is a Professor at the Department of Informatics, University of Bergen. His research focuses on scientific visualization, medical visualization, and computer graphics, with emphasis on volume rendering, interactive systems, and visualization of complex data such as seismic volumes and protein dynamics. He has contributed to tools like VolumeShop and ViSlang, and his work spans medical imaging, climate data analysis, and bioinformatics. He has supervised PhD students including Daniel Patel and Johannes Kehrer. His recent articles address challenges in medical visualization, protein-protein interactions, and interactive navigation systems.

Helwig HauserView profile

Professor

• Visualization
• Data Science
• Interactive Data Analysis
• +5 more

Helwig Hauser is a Professor at the Department of Informatics, University of Bergen, specializing in visualization and data science. His research focuses on interactive visual data exploration, flow visualization, and applications in engineering, biomedical, and geoscience domains. He leads the Visualization research group and teaches courses in computer graphics and data science. Key research interests include scientific visualization, molecular dynamics analysis, high-dimensional data exploration, and visual analytics. His work spans computational fluid dynamics, medical imaging, and engineering simulation, with a focus on developing intuitive tools for complex data interpretation. Hauser has authored/co-authored over 100 peer-reviewed publications, including influential works on flow visualization, ensemble analysis, and interactive brushing techniques. He has edited conference proceedings such as PacificVis and serves on program committees for top visualization conferences. His contributions include open-source tools like the SemSeg project and collaborations with industry on automotive and biomedical applications. He advises graduate students and collaborates internationally, emphasizing interdisciplinary applications of visualization in medicine, geology, and engineering. Current projects explore AI-enhanced visualization, real-time data exploration, and multiscale molecular analysis.

Veronika ŠoltészováView profile

Adjunct Associate Professor

• Scientific Visualization
• Medical Visualization
• Volume Rendering
• +5 more

Veronika Šoltészová is an Adjunct Associate Professor at the Department of Informatics , University of Bergen. Her research focuses on scientific and medical visualization, volume rendering, and perception-driven visualization techniques. She has contributed to advancements in real-time data processing, shading models, and medical imaging workflows. Education: PhD in Computer Science (2012), University of Bergen Research Interests: Volume rendering and streaming data visualization Medical imaging (ultrasound, sonar) Perception-based shading models and illumination Real-time visualization algorithms Publications Trends: Her work spans medical imaging pipelines, volume data processing, and perceptual studies. Recent contributions include output-sensitive filtering for streaming volumes and visibility-driven processing techniques. Labs/Teams: Active in the MedViz (Medical Visualization) and SciVis (Scientific Visualization) research groups.

Daniel PatelView profile

Researcher

• Geological Visualization
• Computer Graphics
• Scientific Visualization
• +3 more

Daniel Patel is an Adjunct Researcher at the University of Bergen's Department of Informatics. His work focuses on advancing visualization techniques for geological and geophysical data, with a particular emphasis on interactive modeling, real-time rendering, and domain-specific tools for scientific communication. He has contributed to projects such as 'Geological Storytelling' and 'Rapid Sketch-Based Modeling,' which aim to bridge the gap between geologists' conceptual ideas and computational modeling. Patel’s research integrates computer graphics innovations with domain-specific challenges in geology and marine science. His research interests include volume rendering, sketch-based modeling, seismic data interpretation, and educational approaches to PhD training. He collaborates with interdisciplinary teams, including the VisGroup at UiB, to develop tools for visualizing complex geological structures and sonar data. Patel holds a PhD in expressive visualization from TU Wien (2009) and has published extensively on topics ranging from chromatic shadows in medical visualization to statistical transfer functions for noisy data. Key contributions include the development of occlusion shading models, instant convolution shadows, and knowledge-assisted visualization frameworks for seismic exploration. His work emphasizes rapid prototyping and user-centric design, enabling domain experts to rapidly communicate and explore geological hypotheses.

Yuedong WangView profile

Professor

• Biostatistics
• Computational Statistics
• Machine Learning
• +7 more

Yuedong Wang is a Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara (UCSB), part of the College of Letters and Science. He holds a PhD from the University of Wisconsin-Madison and has prior experience at the University of Michigan. His research focuses on statistical methodologies, including nonparametric and semiparametric models, smoothing splines, machine learning, and biostatistical applications in renal diseases and human circadian rhythms. He is a Fellow of the ASA, IMS, ISI, and RSS, and a member of IBS and ICSA. His work spans over 150 publications, with recent emphasis on modeling dialysis patient outcomes, SARS-CoV-2 transmission in healthcare settings, and statistical computing. Key collaborations include the MONDO initiative for dialysis outcomes research. He has developed R packages like ASSIST for nonlinear mixed-effects models and contributed to smoothing spline theory. His book *Smoothing Splines: Methods and Applications* is a seminal text in the field. Education: BS in Mathematics (USTC), MS in Operations Research (Chinese Academy of Sciences), PhD in Statistics (UW-Madison). Research interests: Machine learning applications, functional data analysis, longitudinal data modeling, and biostatistical methods in nephrology. His articles address topics like tensor product RKHS analysis, nonparametric mixture models for clinical data, and AI-driven predictive analytics for dialysis patients. Awards include recognition for contributions to statistical methodology and its biomedical applications.

Sinan KockaraView profile

Lecturer

• Medical Image Processing
• Virtual Reality (VR) Surgical Simulation
• Arthroscopic Surgery Training
• +3 more

Sinan Kockara is a Lecturer in the Department of Computer Science at Rice University. He holds adjunct roles at the University of Central Arkansas (Computer Science and Engineering) and the University of Arkansas at Little Rock. With a Ph.D. in Applied Computing and a BS in Computer Engineering, his expertise spans medical image processing, virtual reality (VR) surgical simulation, and high-performance computing. His research focuses on dermatology imaging and arthroscopic surgery training systems, with $2.4+ million in funding from NIH, NSF, and industry partners. He has authored 40+ publications and holds over 640 citations. Education: BS, Computer Engineering, Dokuz Eylul University (Izmir, Turkey) PhD, Applied Computing, University of Arkansas at Little Rock Research Interests: Dr. Kockara develops VR-based surgical simulators for orthopedic training, including fluid dynamics simulations and bone drilling procedures. His work integrates machine learning for skill assessment and medical image analysis for dermatology applications. His lab (Biomedical Image Processing & HPC Lab) emphasizes real-time visualization and HPC optimization for medical systems. Grants & Validation: He secured major grants to validate surgical training platforms like ViRCAST and VATDEP. His studies compare novice/expert surgeon performance metrics and employ systematic validation frameworks for medical simulations. Labs & Future Work: Leads the Biomedical Image Processing & High-Performance Computing Lab, advancing real-time medical visualization and GPU-accelerated algorithms. Ongoing projects include cloud-based surgical simulators and AI-driven medical education tools.

Yu-Wing TaiView profile

Associate Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Yu-Wing Tai is an Associate Professor of Computer Science at Dartmouth College, one of the Ivy League institutions. His research focuses on Computer Vision, Deep Learning, and Multimodalities Generative AI. He holds a PhD from the National University of Singapore and has served as an Associate Editor for IEEE TPAMI and as an Area Chair for major conferences like CVPR, ICCV, and NeurIPS. He is a Senior Member of the IEEE. Previously, he held roles as a Senior Research Director at Kuaishou Technology (2020-2023), Adjunct Professor at HKUST (2017-2023), and Research Director at Tencent (2017-2020). His work spans low-level vision, image/video editing, 3D reconstruction, and applications of large generative models. His research highlights include Motion-Agent for human motion generation, ChatCam for camera control, and Gear-NeRF for 3D reconstruction. He advises a team of PhD students and has mentored alumni now in academia and industry worldwide. His teaching includes courses on Multi-modalities Generative AI and Computer Graphics.

Joshua ClaytonView profile

Clinical Professor

• Computer Science
• Digital Media
• Interactive Design
• +3 more

Joshua Clayton is a Clinical Professor of Computer Science at New York University's Department of Computer Science, part of the College of Arts and Science. He holds an MPS in Interactive Telecommunications from NYU (2011) and a BFA in Graphic Design from Western Michigan University (2001). His work bridges art and technology, focusing on computational creativity, web-based art, and interactive media. Education: MPS, Interactive Telecommunications, NYU (2011) BFA, Graphic Design, Western Michigan University (2001) Research Interests: Digital media, generative art, spatiotemporal data visualization, and the expressive potential of code in web design. His creative projects explore intersections between computational logic and artistic expression. His artistic works include Codex Lucida (spatiotemporal metadata interfaces), Polychromatics (procedural color composites), and Locative Waves (GPS-driven sound installations). Clayton has taught extensively since 2011, covering introductory programming, web design, and specialized topics like Drawing on the Web . While no formal awards are listed, his contributions to teaching and digital art practice are central to his profile. He advises via his course development and creative projects rather than traditional academic supervision.

Søren HøjsgaardView profile

Associate Professor

• Statistics
• Graphical Models
• Bayesian Networks
• +7 more

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

• Graph Grammars
• Artificial Intelligence
• Algorithms and Data Structures
• +3 more

Dr. Anna Paszyńska is a Lecturer at Jagiellonian University, specializing in computational science and graph-based algorithms. Her research focuses on graph grammars, artificial intelligence, and numerical methods for solving complex engineering problems. She has contributed to advanced simulations in environmental modeling, tsunami prediction, and adaptive finite element methods. Her work integrates graph theory with computational techniques to optimize solver performance and address real-world challenges like pollution tracking and disaster simulations. Affiliation: Jagiellonian University Key Research Areas: Graph grammars, AI-driven simulations, computational fluid dynamics, and numerical algorithms. Her recent publications highlight innovations in physics-informed neural networks (PINNs) for solving PDEs and optimizing tsunami and pollution models. She actively collaborates on interdisciplinary projects involving environmental science, engineering, and computer science.

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Xin Zhong is an Assistant Professor of Computer Science at the University of Nebraska at Omaha (UNO), affiliated with the College of Information Science & Technology (IS&T). He specializes in deep learning, machine learning, computer vision, and image processing, with a focus on robust watermarking techniques, adversarial training, and multimedia security. His work integrates computational intelligence into practical applications like medical imaging, disaster risk assessment, and smart city technologies. Dr. Zhong serves as an advisor for PhD, MS, and undergraduate researchers and contributes to academic governance as a member of the Artificial Intelligence concentration committee and the Computer Science Department’s graduate program committee. He also reviews for journals, conferences, and NSF grants, emphasizing interdisciplinary collaboration. His research interests span image watermarking robustness, AI ethics, and interpretable machine learning. Notable contributions include innovations in cross-attention mechanisms, noise-invariant domain learning, and deep morphological neural networks. His lab focuses on advancing cybersecurity, healthcare diagnostics, and urban infrastructure safety through AI-driven solutions. Xin Zhong’s advising and service roles reflect his commitment to advancing both technical research and educational initiatives. His projects often involve partnerships with industry and government agencies to address real-world challenges in automated systems and cyber-physical infrastructure.