جست‌وجوی استاد و پژوهشگر

• Image Compression
• Machine Learning
• Visual Media Processing
• +۷ مورد دیگر

Jona Ballé is an Associate Professor in the Electrical and Computer Engineering department at New York University's Tandon School of Engineering. His research focuses on developing efficient representations of visual media through machine learning and end-to-end optimization techniques. Dr. Ballé's research interests center on visual media compression, spanning still images, video, augmented reality, virtual reality, plenoptic imaging, and holographic imaging. His work bridges information theory, computer vision, and machine learning to develop perceptually optimized compression algorithms. He has made significant contributions to understanding the relationship between human visual perception and image statistics, which has led to improved compression results and ultimately contributed to the JPEG AI standard finalized in 2025. His recent publications demonstrate a strong trend toward Wasserstein distortion metrics, neural compression architectures, and rate-distortion optimization. These works span computer vision, information theory, and signal processing domains, with applications in both traditional and emerging visual media formats. His research shows consistent innovation in developing perceptually relevant metrics that balance fidelity and realism in compressed media. Contributed to JPEG AI standard (2025) Co-organizer of Challenge on Learned Image Compression (CLIC) since 2018 Program committee member of Data Compression Conference (DCC) since 2022 Reviewer for top-tier publications including NeurIPS, ICLR, ICML, and IEEE Transactions journals Dr. Ballé has advised numerous graduate students who have co-authored significant publications with him, particularly in the areas of neural compression and perceptual metrics. His research has been supported by institutions including the Simons Foundation. He maintains active collaborations across academia and industry, with his work at Google (2017-2024) directly informing his current academic research. His laboratory focuses on developing open-source implementations of advanced compression techniques, with notable GitHub repositories including Wasserstein Distortion implementation in PyTorch and CoDeX (Learned data compression in JAX), demonstrating his commitment to reproducible research and community engagement.

Wolfram Burgardمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Mobile Robotics
• +۳ مورد دیگر

Prof. Wolfram Burgard is a Professor for Robotics and Artificial Intelligence and Founding Chair of the Engineering Department at the University of Technology Nuremberg. Previously, he was a professor of computer science at the University of Freiburg (1999–2021), where he established the renowned Autonomous Intelligent Systems research lab. His research focuses on probabilistic techniques for robot navigation, state estimation, and control, with notable contributions to SLAM, path-planning, and mobile robot applications such as museum tour-guides (Rhino/Minerva) and autonomous vehicles. His research interests span artificial intelligence, mobile robotics, and autonomous systems. Key projects include deploying interactive museum robots, developing autonomous parking systems (2008), and the pedestrian-like Obelix robot (2012). He has authored over 350 papers, co-authored seminal books like Probabilistic Robotics , and holds the prestigious Gottfried Wilhelm Leibniz Prize (2009) and ERC Advanced Grant (2010). He is an ECCAI and AAAI Fellow, and member of the German Academy of Sciences Leopoldina and Heidelberg Academy of Sciences. Prof. Burgard coordinated the BrainLinks-BrainTools Cluster of Excellence (2012–2019), funded by the German Research Foundation. His work emphasizes real-world applications, including industrial plant supervision and autonomous driving systems. His Erdős number is ≤4, and his publications are indexed on Google Scholar.

• Computer Graphics
• 3D Visualization
• Machine Learning in Graphics
• +۵ مورد دیگر

Sebastian Wohner is a researcher at the Chair of Computer Graphics and Visualization (Prof. Westermann) at the Technical University of Munich. His work focuses on advanced visualization techniques, machine learning applications in graphics, and GPU-accelerated algorithms for 3D design and simulation. He actively contributes to projects such as the NVIDIA CUDA Research Center and ERC-funded initiatives like SaferVis and realFlow, emphasizing real-time liquids and safer visualization systems. His research interests span 3D Gaussian splatting, topology optimization, neural fields for statistical dependencies, and spatio-temporal flow visualization. He has pioneered methods for compressing meteorological ensembles and accelerating novel view synthesis in consumer devices. Wohner also explores GPU-based linear algebra optimizations and efficient rendering techniques for ribbons and twisted lines. In teaching, he leads courses on game physics, visual data analytics, deep learning in computer graphics, and topology optimization. Notable contributions include the development of the Particle Engine and Bunny Demo applications, as well as advancements in differentiable rendering and robotic perception systems. His work bridges theoretical foundations with practical implementations in both academia and industry.

Daniel Weiskopfمشاهده پروفایل

استاد

• Scientific Visualization
• Eye Tracking
• Human-Computer Interaction
• +۵ مورد دیگر

Prof. Daniel Weiskopf is a Professor at the University of Stuttgart's Faculty of Computer Science, Electrical Engineering and Information Technology, affiliated with the Institute of Parallel and Distributed Systems. His research focuses on visualization techniques, eye tracking, and human-computer interaction, with applications in virtual/augmented reality (VR/AR), data analysis, and uncertainty modeling. He has contributed to advancements in scientific visualization, including ML-driven flow visualization, energy-efficient rendering, and gaze-aware interfaces. His work emphasizes empirical methodologies, such as eye-tracking studies for evaluating visualization literacy and collaborative learning in AR environments. Research interests include: Scientific Visualization and Uncertainty Representation Eye Tracking Methodologies and Human Factors VR/AR Systems and Immersive Analytics Machine Learning Applications in Visualization Recent publications highlight trends in optimizing visualization performance, improving data interpretation through gaze-aware systems, and integrating advanced visualization into gaming and engineering contexts. His work bridges computational methods with user-centric design principles. He leads efforts in the Institute of Parallel and Distributed Systems , collaborating on tools like Gazealytics for exploratory gaze analysis and mint for VR visualization integration. No awards or grants are explicitly listed in the provided information.

Jiřina Vejnarováمشاهده پروفایل

دانشیار

• Multidimensional structured models
• Conditional independence
• Artificial intelligence
• +۳ مورد دیگر

Jiřina Vejnarová is an Associate Professor and the Director of the Institute of Information Theory and Automation at the Czech Academy of Sciences. She also teaches at the Faculty of Informatics of the University of Economics and the Faculty of Nuclear Sciences and Physical Engineering of the Czech Technical University. Her educational background includes graduation from the Faculty of Mathematics and Physics at Charles University in Prague in 1985, with a focus on probability and mathematical statistics. She received her RNDr. title in 1986 and her CSc. (PhD equivalent) in theoretical cybernetics in 1991. Professor Vejnarová's research focuses on structured multidimensional models within interval probabilities, particularly in possibility theory and evidence theory. Her work spans artificial intelligence, uncertainty modeling, belief functions, and probabilistic reasoning. She has developed compositional models for credal sets and has extensively researched the marginal problem in evidence theory. Her recent publications demonstrate a consistent focus on evidence theory, possibility theory, and related areas of artificial intelligence. She has made significant contributions to the understanding of credal networks, composition operators, and the interpretation of various probability extensions. Professor Vejnarová has been actively involved in the academic community, having organized the 5th International Symposium on Imprecise Probabilities and served on the editorial board of the journal Kybernetika. She was a member of the board of the Czech Society for Cybernetics and Informatics from 2003-2010. As Director of the Institute since 2017 (previously serving as Deputy Director for Research from 2009), she leads research initiatives in information theory and automation. Her teaching responsibilities include courses such as Mathematical Informatics, Information Theory and Inference, Logic and Semantics, and Probabilistic Models of Artificial Intelligence.

Jakob Zechمشاهده پروفایل

استاد

• Uncertainty Quantification
• High-Dimensional Approximation
• Sparse-Grid Techniques
• +۴ مورد دیگر

Jakob Zech is a Professor at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University since April 2020. Before this, he held postdoctoral positions at MIT (2019-2020) and ETH Zürich (2018-2019). He earned his PhD in Mathematics from ETH Zürich in 2018, focusing on Sparse-Grid Approximation of High-Dimensional Parametric PDEs, followed by a Master’s (2014) and Bachelor’s (2012) in Applied Mathematics from ETH Zürich and TU Wien, respectively. Research Interests : Zech’s work bridges Uncertainty Quantification (UQ), high-dimensional approximation, and computational mathematics. Key areas include sparse-grid techniques, neural networks, transport methods, Bayesian inverse problems, and the theoretical foundations of deep learning. His research emphasizes developing algorithms for stochastic modeling and analyzing their mathematical properties. Teaching : He has taught advanced courses such as High Dimensional Approximation, Theory of Deep Learning, and Numerical Methods for Bayesian Inverse Problems at Heidelberg University. He also served as a teaching assistant for numerous courses at ETH Zürich, covering numerical analysis, partial differential equations, and linear algebra. Publications : His recent work explores quantum computing applications in polynomial chaos expansions, statistical learning theory for neural operators, and multilevel optimization strategies. His articles reflect a strong focus on combining classical numerical methods with modern machine learning techniques. Labs/Teams : While no specific lab is mentioned, his research group is active in computational UQ and deep learning, collaborating internationally with institutions like MIT and ETH Zürich.

Kinga Lipskochمشاهده پروفایل

مدرس

• Computer Science
• Distributed Systems
• Data Management
• +۴ مورد دیگر

Dr. Kinga Lipskoch is a Distinguished Lecturer of Computer Science at the School of Computer Science & Engineering, Constructor University Bremen. She holds a Ph.D. in Computer Science from Carl von Ossietzky University of Oldenburg (2008–2012), a Master's in Computer Science from Transilvania University of Brașov (2005–2006), and a Diploma in Mathematics and Computer Science from the same institution (2001–2005). Her professional experience includes postdoctoral research at Jacobs University Bremen and visiting lectureships at Hamburg University of Applied Sciences and Transilvania University of Brașov. Her research focuses on distributed systems, data management, probabilistic algorithms, and big data analytics in earth sciences. Key areas include probabilistic quorum systems for improving data consistency and operation availability, array analytics for geospatial data, and algorithm design for optimization problems. She has also contributed to software engineering projects, such as text parsing tools for bilingual dictionaries and desktop applications for institutional resource management. Her work bridges theoretical computer science with practical applications, addressing challenges in distributed computing, environmental data analysis, and educational software development.

Yuxuan Xueمشاهده پروفایل

پژوهشگر

• Computer Vision
• 3D Reconstruction
• Neural Networks
• +۳ مورد دیگر

Yuxuan Xue is a PhD student in the Real Virtual Humans group at the University of Tübingen, supervised by Prof. Dr. Gerard Pons-Moll. He is affiliated with the International Max Planck Research School for Intelligent Systems (IMPRS-IS). Prior to his PhD, he earned B.Sc. and M.Sc. degrees in Mechanical Engineering and Robotics from the Technical University of Munich (TUM), with his Master's thesis conducted at the Max Planck Institute for Intelligent Systems. His research focuses on 3D human modeling, neural fields, and diffusion models for avatar creation and clothed body reconstruction from monocular data. Education: PhD in Computer Science, University of Tübingen (2022–present) M.Sc. Mechanical Engineering & Robotics, Technical University of Munich (2020–2022) B.Sc. Mechanical Engineering, Technical University of Munich (2017–2020) Research Interests: Yuxuan’s work bridges computer vision and graphics, emphasizing: 3D human reconstruction from sparse/monocular data Neural fields for animatable body modeling Diffusion models for coherent 3D generation Event-based cameras for non-rigid tracking Parameter-efficient model fine-tuning Publications: His contributions include groundbreaking work on NSF (Neural Surface Fields) and Human 3Diffusion, advancing the state-of-the-art in 3D avatar creation and cloth modeling. Awards: Best Student Paper Award at BMVC 2022 OpenAI Research Access Program Grant ($5000, 2024) Multiple top-tier conference acceptances (NeurIPS, ICCV, ICLR) Grants and Funding: Supported by the Carl Zeiss Foundation, DFG Emmy Noether Programme, and BMBF Tübingen AI Center. Labs and Collaborations: Active in the Real Virtual Humans group and collaborates with institutions like Meta Reality Labs and MPI for Informatics.

• Optimization Algorithms
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Abdelhameed Ibrahim is an active academic researcher with a focus on optimization algorithms, machine learning, and their applications in healthcare, environmental science, and engineering. His work spans over 15 years, with contributions to nature-inspired metaheuristics, image processing, and predictive modeling. He frequently collaborates with institutions on projects involving cancer detection, energy forecasting, and medical diagnostics. Key areas: Optimization algorithms, medical imaging, renewable energy prediction, and machine learning frameworks. Notable collaborations include work on Greylag Goose Optimization and Al-Biruni Earth Radius algorithms. Research interests emphasize interdisciplinary applications, such as solar energy harnessing, rainfall forecasting, and electric vehicle recommendation systems. His methodologies often integrate advanced computational techniques with real-world problem-solving.

• Artificial Intelligence
• Computer Science
• Data Science
• +۳ مورد دیگر

Florian Marwitz is a doctoral researcher at the University of Hamburg affiliated with the Cluster of Excellence 'Understanding Written Artefacts' (UWA) and the Artificial Intelligence in Humanities (Prof. Dr. Möller) department. His work focuses on advancing computational methods for humanities research, including graph algorithms, probabilistic models, and AI applications in cultural heritage preservation. He contributes to projects like the Data Linking Infrastructure and ChatHA chatbot development, emphasizing sustainable information systems and efficient prediction techniques. His research bridges computer science and humanities, addressing challenges in data representation and decision-making systems. Marwitz holds an MA and is based at the Centre for the Study of Manuscript Cultures. His activities include organizing workshops on data linking and AI ethics, reflecting commitments to interdisciplinary collaboration. He is part of the Institut für Humanities-Centered Artificial Intelligence (CHAI), actively involved in training programs and public engagement initiatives.

Marcel Gehrkeمشاهده پروفایل

پژوهشگر ارشد

• Probabilistic Graphical Models
• Data Linking
• Machine Learning
• +۲ مورد دیگر

Dr. Marcel Gehrke is a Research Fellow in Artificial Intelligence for Humanities at Universität Hamburg, specializing in probabilistic models and data linking for manuscript research. His work develops computational methods for analyzing complex relationships in cultural heritage data through statistical relational AI approaches. He has received multiple best paper awards (FLAIRS 2024, IEEE ICSC 2024) for innovations in temporal probabilistic modeling. His research contributes to evidence-based understanding of written artefacts through projects like 'Data Linking Infrastructure' and 'Foundations of Evidence-based Understanding'.

Mathias Niepertمشاهده پروفایل

استاد

• Generative AI
• Geometric Deep Learning
• Physics-aware Deep Learning
• +۶ مورد دیگر

Mathias Niepert is a Professor (W3) at the University of Stuttgart, affiliated with the Department of Computer Science. He serves as Chief Scientific Advisor at NEC Laboratories Europe and is a faculty member of the International Max Planck Research School for Intelligent Systems (IMPRS-IS). His additional affiliations include the Cluster of Excellence for the Simulation Sciences (SimTech) and the European Laboratory for Learning and Intelligent Systems (ELLIS), where he leads the Machine Learning and Simulation Lab. Education: Ph.D. in Computer Science and Scientific Computing, Indiana University, USA Postdoctoral Researcher, Paul G. Allen School of Computer Science, University of Washington, Seattle Research Focus: His work centers on developing machine learning methods that incorporate physical and geometric structures. Key areas include efficient generative AI, physics-aware neural networks, and probabilistic models, with applications in computational chemistry, fluid dynamics, and biomedical research. His lab emphasizes interdisciplinary approaches bridging ML, natural sciences, and engineering. Awards and Honors: Google Faculty Research Award Best Paper Award (AKBC 2021) Best Reviewer Awards (UAI 2021, ICLR 2021) Multiple unspecified best paper awards Academic Leadership: He advises graduate students and postdoctoral researchers through the Machine Learning and Simulation Lab. His group regularly publishes at top-tier venues (NeurIPS, ICML, ICLR) and he serves as Area Chair for major conferences including NeurIPS, ICML, and ICLR.

Yulia Alexandrمشاهده پروفایل

استادیار مدعو

• Algebraic Statistics
• Applied Algebraic Geometry
• Mathematical Machine Learning
• +۳ مورد دیگر

Dr. Yulia Alexandr is a Hedrick Assistant Adjunct Professor at UCLA and a Postdoctoral Fellow in Applied Mathematics at Harvard University . She completed her PhD in Mathematics at UC Berkeley in 2023 under the supervision of Bernd Sturmfels and Serkan Hoşten , with a thesis titled "From Voronoi Cells to Algebraic Statistics" . She holds a Bachelor's degree in Mathematics from Wesleyan University (2019), where she was advised by Karen Collins .

Philipp Sebastian Sommerمشاهده پروفایل

پژوهشگر

• Climate Science
• Paleoclimatology
• Data Analysis
• +۴ مورد دیگر

Dr. Philipp Sebastian Sommer is a Research Software Engineer at the Helmholtz Coastal Data Center (HCDC) within Helmholtz-Zentrum Hereon. With a PhD in Physics from the University of Hamburg (2013) and a Master in Integrated Climate System Sciences (2012), he specializes in climate modeling, open source software development, and paleoclimatic data analysis. Current Affiliation: Helmholtz-Zentrum Hereon (2019–present) Past Roles: University of Lausanne (2015–2019), Max Planck Institute (PhD) Education: PhD in Physics (University of Hamburg), Master in Integrated Climate System Sciences, Bachelor in Physics His research interests span big data visualization, numerical modeling, and open science infrastructure. He develops tools like psyplot for interactive climate data visualization, straditize for digitizing stratigraphic diagrams, and IUCm for climate-smart urban planning. His work emphasizes robust software engineering, reproducibility, and FAIR data principles. Article trends show a focus on paleoclimatology , climate modeling software (ICON, GWGEN), and open science frameworks (DJAC, DASF). He collaborates with international teams across the Helmholtz Association and participates in the Climate Limited-area Modelling Community. Key projects include the Helmholtz Coastal Data Center for sustainable coastal data management and the Eurasian Modern Pollen Database (EMPD) for paleoenvironmental studies. His work often bridges computational methods with geoscientific research to advance interdisciplinary collaboration.

Robert Bamlerمشاهده پروفایل

استاد

• Deep Generative Models
• Machine Learning
• Probabilistic Inference
• +۳ مورد دیگر

Robert Bamler is a Professor of Data Science and Machine Learning at the University of Tübingen, Germany , and a member of the Cluster of Excellence "Machine Learning: New Perspectives for Science" and the Tübingen AI Center . Current affiliation: University of Tübingen (since November 2020) Prior roles: Postdoctoral Scholar at UC Irvine (with Stephan Mandt), Machine Learning Researcher at Disney Research (Pittsburgh/Los Angeles) Education: PhD in Theoretical Statistical and Quantum Physics from University of Cologne (2016), advised by Achim Rosch Research Focus : Algorithm development for deep generative models (including large language models) Resource-efficient inference and model compression Probabilistic machine learning applications in natural sciences Theoretical foundations in statistics, information theory, and physics Scientific Contributions : German Telekom Foundation PhD scholarship awardee Co-founder of the BamlerLab reading group on probabilistic modeling and compression Key Collaborations : Stephan Mandt (UC Irvine) Bernhard Schölkopf (MPI-IS) Cluster of Excellence "Machine Learning" (Tübingen) Teaching : Lecturer for "Data Compression With and Without Deep Probabilistic Models" (2021-2025) Organizer of weekly reading groups on function-space inference and compression algorithms