Find Academic Supervisors

• Computer Vision
• Machine Learning
• Medical Imaging
• +2 more

Lazaros T. Tsochatzidis is a researcher with significant contributions to computer vision , machine learning , and their applications in medical imaging and autonomous driving . He has published extensively in top-tier venues like Pattern Recognition , IEEE Access , and Robotics and Autonomous Systems , often collaborating with researchers such as Ioannis Pratikakis and Konstantinos Zagoris . Research Interests Specializes in LiDAR-based 3D object detection for autonomous vehicles Developed domain adaptive object detection techniques using mean teacher models Innovated part-aware graph fusion for skeleton-based action recognition Expert in medical image analysis for breast cancer diagnosis Contributed to historical document transcription and sclera segmentation research Scientific Awards No explicit awards mentioned in available data Notable Collaborations Long-term collaboration with Konstantinos Zagoris (6 co-authored papers) Extensive work with Ioannis Pratikakis (11 co-authored publications) Multidisciplinary collaborations with experts in medical imaging , autonomous systems , and biometric security

• Computer Architecture
• High Performance Computing
• GPU Programming
• +5 more

Josie Esteban Rodriguez Condia is a Fixed-term Assistant Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is a member of the CARS@PoliTO Interdepartmental Center - Center for Automotive Research and Sustainable Mobility and serves as an invited member of both the College of Electronic, Telecommunications, and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Her research focuses on computer architecture reliability, particularly in GPU and AI accelerator systems. Key areas include functional testing, general purpose graphics processing units (GPGPUs), hardware accelerators, hardware architecture, and parallel processing. Her work addresses critical challenges in reliability assessment of AI-based automotive systems, self-test libraries for tensor cores, and hardening techniques for neural networks on GPUs. Her recent publications demonstrate a strong trend toward reliability engineering for AI hardware, with particular emphasis on automotive applications and GPU-based neural network implementations. The research spans fault injection methodologies, error modeling, and architectural solutions to enhance system resilience against soft errors and permanent faults. Dr. Rodriguez Condia actively supervises PhD students including Gustavo Vilar De Farias, Giuseppe Esposito, and Robert Alexander Limas Sierra, all working on reliability evaluation and enhancement of neural networks. She is a member of the PNRR Research Group for the National Center for HPC, Big Data and Quantum Computing (2022-2025). She teaches multiple courses including GPU Programming and High Performance Computing for both Computer Engineering and Quantum Engineering programs. Her editorial work includes serving as Guest Editor for APPLIED SCIENCES in 2024.

• Cybersecurity
• Machine Learning
• Web Development
• +3 more

Nikola Savanović is a researcher and academic at Singidunum University, specializing in cybersecurity, machine learning, and web technologies. He holds a PhD in Advanced Security Systems (2024) from Singidunum University, following a Master's in Contemporary Information Technologies (2016) and a Bachelor's in Informatics and Computing (2013), both from the same institution. He also completed high school in Computer-Aided Design at Politehnika School for New Technologies (2003-2007). His research focuses on intrusion detection systems, machine learning optimization for cybersecurity applications, and adaptive web technologies. He has contributed to educational technology through virtual learning environments and game-based learning. His mathematical work includes fixed point theory in b-metric spaces. He has authored/co-authored multiple books on web design, multimedia systems, and digital marketing. Publications highlight interdisciplinary work: hybrid CNN-XGBoost models for medical diagnostics, metaheuristic algorithms for software defect detection, and cybersecurity innovations in healthcare IoT systems. His articles span computer science, mathematics, and data science, emphasizing practical applications in education and healthcare. Despite no listed awards, his extensive publication record and teaching roles reflect significant academic contributions. He actively collaborates on university projects and advises students in informatics and computing disciplines, though no formal student list is provided.

Christopher BattyView profile

Associate Professor

• Computer Graphics
• Scientific Computing
• Physics-based Simulation
• +6 more

Christopher Batty is an Associate Professor and Director of Infrastructure at the University of Waterloo's Department of Computer Science. His research focuses on computer graphics and scientific computing, with an emphasis on physics-based numerical simulation of fluids and solids for applications in animation, visual effects, and interactive environments. He holds a Ph.D. from the University of British Columbia (2010) and a B.C.Sc. from the University of Manitoba (2004). His work spans fluid dynamics, solid mechanics, and geometry processing, addressing challenges like surface reconstruction, multi-scale simulations, and efficient solvers for complex fluid-solid interactions. Recent contributions include novel methods for divergence-free fluid editing, discrete elastic rod optimization, and Monte Carlo-based approaches for PDEs on surfaces. Batty’s research integrates computational geometry, numerical analysis, and optimization to create scalable and accurate tools for procedural fluid and solid simulation. His articles emphasize robustness in handling thin obstacles, narrow gaps, and intricate boundary conditions, often leveraging advanced techniques like closest point methods and monolithic solvers. No scientific awards are listed, though his extensive publication record reflects significant contributions to the field. He leads projects on adaptive liquid simulations, surface-only deformable models, and high-resolution embedded fluid surfaces.

George LabahnView profile

Professor

• Computer Algebra
• Symbolic-Numeric Computation
• Computational Finance
• +2 more

George Labahn is Professor and Director of the Symbolic Computation Group at the University of Waterloo's David R. Cheriton School of Computer Science. His research spans computer algebra, computational finance, and pen-based mathematical interfaces. Key contributions include fundamental algorithms for matrix normal forms (Hermite/Smith forms), symbolic-numeric polynomial computation, and development of the MathBrush system for handwritten mathematics recognition. Research focuses on efficient algorithms for polynomial matrix arithmetic, rational approximation, and solving differential equations with elliptic coefficients. Computational finance work develops numerical methods for option pricing under jump diffusion models. Current projects include fast Hermite form computation and rank-sensitive matrix algorithms. Serves as associate editor for Journal of Symbolic Computation (JSC) and previously for ACM Transactions on Mathematical Software (TOMS). Authored core Maple programming guides and implemented key packages for symbolic integration, differential equations, and graphics in Maple.

Markus SchützView profile

Researcher

• Computer Graphics
• Point Cloud Rendering
• Real-Time Visualization
• +3 more

Markus Schütz is a Researcher at the Department of Computer Graphics, Faculty of Informatics, TU Wien. He holds a Dipl.-Ing. Dr.techn. and BSc. His work focuses on real-time rendering of massive point clouds, GPU acceleration, and interactive visualization. Key projects include 'Bringing Point Clouds to WebGPU' and 'Instant Visualization and Interaction for Large Point Clouds'. He has developed the Potree library for web-based point cloud visualization. Education: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) Doctor of Technical Sciences (Dr.techn.) from TU Wien Research Interests: His research emphasizes real-time rendering techniques for large-scale point clouds, GPU optimization, and efficient data processing. He explores areas such as level-of-detail generation, compute shader utilization, and web-based visualization tools like Potree. Recent work includes software rasterization of 2 billion points and simultaneous LOD generation for point clouds. Awards: Best Paper Award at EGPGV2024 High-Performance Graphics 2022 Best Paper Award Second Place in SIGGRAPH Poster Student Research Competition (2018) AGEO AWARD 2017 Projects & Grants: Bringing Point Clouds to WebGPU (2024–2025, netidee Foundation) Instant Visualization and Interaction for Large Point Clouds (2023–2026, WWTF) IVILPC (Interactive Visualization of Large Point Clouds) project Labs & Teams: Active in TU Wien's Computer Graphics Group, collaborating on GPU-accelerated rendering and real-time visualization systems.

Katharina KröslView profile

Lecturer

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

Katharina Krösl is a Lecturer at Vienna University of Technology, affiliated with the Institute of Computer Graphics and Algorithms within the Rendering and Modeling workgroup. Her research bridges computer graphics and assistive technologies through immersive virtual reality (VR) and augmented reality (AR) applications. PhD in Computer Science (2016-2020), supervised by Michael Wimmer Master's thesis on interactive photon tracing for lighting design Her work focuses on simulating vision impairments (e.g., cataracts), developing real-time rendering techniques, and creating VR-based training systems for disaster management and attention disorders. Publications span path tracing optimization, XR accessibility, and perceptual modeling. Scientific recognitions include the Young Experts Award 2021 and IEEE VR 2020 Best Research Demo Award. She contributes to IEEE and ACM publications while integrating luminaire design with VR/AR workflows.

• Computer Vision
• Assistive Technologies
• Healthcare Informatics
• +3 more

Irene Ballester Campos is a Researcher at TU Wien's Faculty of Informatics, affiliated with the Department of Computer Graphics and Algorithms. Her work focuses on applying computer vision and AI to develop assistive technologies for healthcare contexts, particularly in dementia care and clinical applications. She is involved in the visuAAL project (2020–2026) and previously contributed to the DIANA project (2020–2023). Her research emphasizes privacy-sensitive systems, 3D sensor technologies, and human-centered design for assistive applications. Key areas include dementia behavior monitoring through depth sensors, action recognition in privacy-sensitive scenarios, and ethical implications of assistive systems. She also explores clinical applications such as Parkinson's disease severity estimation through motion analysis. Publications highlight contributions to 3D human pose estimation, dynamic object tracking in robotics, and vision-based solutions for toileting assistance. Her work bridges computer science with healthcare, emphasizing practical deployment in real-life clinical settings.

Adam CelarekView profile

Researcher

• Computer Graphics
• Physically Based Rendering
• Deep Learning for 3D Data
• +4 more

Adam Celarek is a PreDoc Researcher and University Assistant at the Computer Graphics department (E193-02) within the Faculty of Informatics at Vienna University of Technology. He holds the position of Scientist in the Rendering and Modeling workgroup and serves as a PhD student. Celarek teaches courses including Computer Graphics, Fundamentals of Computer Graphics, Seminar in Visual Computing, and Rendering. His research interests span Physically Based Rendering, Deep Learning for 3D data, Point Clouds, Gaussian Mixture Models, and Terrain Rendering. Celarek's work focuses on the intersection of computer graphics and machine learning, particularly in processing unstructured 3D data. He is currently working on deep learning applications for Gaussian mixture models and developing AlpineMaps, a navigation aid for alpinists in collaboration with Manuela Waldner. His publication record shows a consistent focus on rendering techniques and 3D data processing, with recent work emphasizing the application of deep learning to Gaussian mixture models and terrain visualization. The research demonstrates progression from foundational work on light transport algorithms to more recent applications in geospatial visualization and deep learning for 3D data. OCG-FÖRDERPREIS 2018 (with Christoph Weinzierl-Heigl) Celarek has supervised multiple student projects including Johannes Eschner's work on real-time avalanche risk visualization and Stefan Fraiss's research on Gaussian mixture models from point clouds. He is involved in the EVOCATION training network as a former Marie Skłodowska-Curie fellow and currently leads the High-performance triangle-mesh streaming for unbounded datasets project funded by netidee Projektförderung. As a member of the Rendering and Modeling workgroup, Celarek contributes to the department's research in physically based rendering, 3D data processing, and visualization techniques, with particular expertise in light transport algorithms and Gaussian mixture models.

Rémi AllègreView profile

Associate Professor

• Geometric Modeling
• Appearance Modeling
• Texture Synthesis
• +6 more

Rémi Allègre is an Associate Professor in Computer Science at the Institute of Technology Haguenau (IUT Haguenau), affiliated with the University of Strasbourg (France). He conducts research at the ICube CNRS laboratory within the Geometric and Graphical Computing (IGG) group. Department: Multimedia and Internet Professions (Internet and multimedia technologies) Research Lab: ICube - UMR 7357, CNRS Contact: remi.allegre@unistra.fr | +33 (0)3 88 05 34 39 His research focuses on Geometric Modeling , Appearance Modeling , and Texture Synthesis , with applications in 3D data processing, procedural modeling, and biomedical imaging. Key trends include: Developing differentiable point process models for inverse procedural modeling Creating GAN-based data augmentation techniques for histopathological images Advancing semi-procedural texture synthesis methods Improving dynamic surface reconstruction algorithms Investigating hybrid representations (implicit surfaces, meshes, point sets) Multiscale label-map extraction for structured texture synthesis Scientific recognition includes: AFIG 2005 Best Article Award He supervises doctoral students such as Pascal Guehl, Florian Allender, Guillaume Baldi, Steve de Rose, and Maximilien Nowak. Collaborations span the ICube laboratory and biomedical imaging projects.

Pascal SchreckView profile

Professor

• Geometric Computing
• Formal Methods in Geometry
• Automated Deduction
• +3 more

Pascal Schreck is a Professor of Computer Science at the University of Strasbourg, affiliated with the Department of Computer Science within the UFR of Mathematics and Computer Science. He is a researcher at the ICube laboratory and has held administrative roles, including Director of the Computer Science Department (2008–2011) and course coordinator for specialized programs. His research focuses on geometric computing, formal methods in geometry, automated deduction, and geometric constraint solving, with contributions to theorem proving, computational geometry, and CAD applications. His work integrates algebraic and geometric approaches to solve problems like constructibility in geometric constructions, incidence geometry, and constraint systems. He has developed methods using Coq proof assistants for formal verification and explored topics such as homotopy-based solutions for geometric constraints. His contributions span theoretical advancements and practical applications in areas like 3D modeling and medical trajectory planning. Key research trends include leveraging formal systems (e.g., Coq) for mechanized proofs, analyzing geometric constructs' feasibility, and improving algorithms for constraint resolution. His publications address foundational geometry theorems (e.g., Dandelin-Gallucci), combinatorial geometry, and automated construction verification. He has also contributed to international workshops and conference proceedings on automated deduction in geometry. As a member of the ICube laboratory and the IGG Team (Geometric and Graphical Computing), Schreck collaborates on interdisciplinary projects, bridging mathematics, computer science, and engineering. His work emphasizes rigorous formalization, algorithm optimization, and practical implementation in geometric problem-solving domains.

Ning NingView profile

Assistant Professor

• Stochastic Processes
• Markov Chains
• Time Series Analysis
• +4 more

Ning Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and the Institute for Quantum Science and Engineering. She co-founded the Stochastic Processes Seminar in the Department of Mathematics. Her research focuses on stochastic processes, Markov chains, machine learning, and quantum computing. Prior to her academic career, she worked as a software developer and database architect in Los Angeles for four years, specializing in data analysis and parallel computing. Education: B.S. in Math from Shandong University (China Math Base), M.S. in Math from the University of Southern California, and Ph.D. in Statistics and Applied Probability from UCSB. She held postdoctoral positions at the University of Washington (Applied Math) and the University of Michigan (Statistics). Research interests include stochastic algorithms, Monte Carlo methods, hidden Markov models, and quantum counterparts of classical methods. She is an Associate Editor for Bayesian Analysis (since 2025), Journal of Computational and Graphical Statistics (since 2024), and Statistics and Computing (since 2023). Recent talks and contributions include invited sessions at the Bayes Comp 2025 conference and the KAUST Stochastic Numerics Workshop. Her work spans theoretical advancements and computational innovations in statistical methods, with applications to large-scale data analysis and machine learning.

Markus PüschelView profile

Professor

• Program Generation
• Fourier Analysis
• Signal Processing
• +6 more

Markus Püschel is a Professor of Computer Science at ETH Zurich, leading the Advanced Computing Laboratory. He previously served as Head of the Department of Computer Science at ETH from 2013 to 2016. Before joining ETH in 2010, he was a Professor at Carnegie Mellon University (CMU) and retains adjunct status there. He holds a PhD in Computer Science (1998) and a Mathematics Diploma (1995) from the University of Karlsruhe. His research focuses on program generation for performance, Fourier analysis, signal processing, machine learning, and compiler optimization. He pioneered the SPIRAL system for generating optimized signal processing libraries and has contributed to algebraic signal processing theory. His work bridges mathematical foundations with practical high-performance computing, emphasizing automated code generation and domain-specific languages. Research trends in his articles include causal inference on directed acyclic graphs, quantized neural network inference, and efficient compiler techniques. His work on graph signal processing and causal analysis has led to novel methods in time-series data and DAG learning. Awards: IEEE Fellow (2020), Golden Owl Teaching Award (2015), NSF Discovery Grant (2008), and multiple best paper awards. Grants/Projects: Co-PI for Making Program Analysis Fast (SNF), SPIRAL GPU projects, and collaborations with industry partners like Intel and AMD. He advises over 40 PhD and master’s students, many of whom contribute to impactful projects in compilers, machine learning, and signal processing. His lab also co-founded the Swiss Data Science Center.

Kai HormannView profile

Professor

• Computational Sciences
• Geometric and Visual Computing
• Informatics
• +3 more

Kai Hormann is a full professor in the Faculty of Informatics at Università della Svizzera italiana (USI Lugano). He earned his Ph.D. in computer science from the University of Erlangen-Nürnberg in 2002 and has held positions at Clausthal University of Technology, Caltech, and the CNR in Pisa. His research focuses on mathematical foundations of geometry processing, including barycentric coordinates, subdivision algorithms, and rational interpolation. Hormann has authored over 100 publications and serves as an associate editor for journals like Computer Aided Geometric Design and Dolomites Research Notes on Approximation . In 2024, he received the prestigious John A. Gregory Award for his contributions to geometric modeling. Education : Ph.D. in Computer Science, University of Erlangen-Nürnberg (2002) Diploma in Mathematics, University of Erlangen (1997) Abitur, Leibniz-Gymnasium Bad Schwartau (1992) Research Interests : Hormann’s work bridges computer science, applied mathematics, and engineering. His current projects include interactive shape deformations, time-dependent object processing, 3D visualization, and GPU-accelerated algorithms. His research areas encompass geometry processing, computational sciences, and numerical analysis. Publications : His recent articles focus on barycentric coordinates, subdivision schemes, and surface reconstruction. Themes include transfinite coordinates, curvature continuity, and efficient interpolation methods. Awards : John A. Gregory Award (2024) Chair of SIAM Activity Group on Geometric Design (2017–2018) Advising & Grants : He has advised numerous PhD students, including work on barycentric rational curves and subdivision schemes. He has organized conferences such as the SIAM GD and GMP series, reflecting his leadership in computational geometry. Labs & Teams : Hormann is affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), collaborating on interdisciplinary projects in AI and geometry processing.

Wolfgang StuerzlingerView profile

Professor

• 3D User Interfaces
• Extended Reality
• Augmented Reality
• +10 more

Wolfgang Stuerzlinger is a Professor and Director of the VVISE Lab at Simon Fraser University's School of Interactive Arts & Technology. His research focuses on 3D user interfaces, virtual/augmented reality (VR/AR), and human-computer interaction. He holds a Doctorate from the Vienna University of Technology and has held academic roles at York University and the University of North Carolina. **Education**: PhD (Dr. techn.) in Computer Science, Technical University of Vienna, Austria Dipl.-Ing. (Master's equivalent), Computer Science, Technical University of Vienna **Research Interests**: Specializes in VR/AR interaction techniques, 3D user interfaces, and visual/immersive analytics. Current projects address 3D interaction challenges, occasionally failing systems, and hardware/software innovations for extended reality. Notable contributions include studies on vergence-accommodation conflict, gaze-based text entry, and locomotion techniques in VR. **Awards**: Member of the IEEE VGTC Virtual Reality Academy (2023) and ACM SIGCHI Academy, recognizing leadership in VR and HCI research. **Grants & Labs**: Leads the VVISE Lab, collaborating on projects like RedirectedStepper and Depth3DSketch. Active in grant-funded research on immersive analytics and accessibility in XR navigation. **Teaching**: Teaches courses such as IAT 445 (Immersive Environments) and IAT 848 (Mediated Reality Systems).