جستجوی استاد راهنمای دانشگاهی

Christian Theobaltمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۵ مورد دیگر

Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays

Michael Carbinمشاهده پروفایل

استادیار

• Programming Systems
• Approximate Computing
• Probabilistic Programming
• +۴ مورد دیگر

Michael Carbin is the Jamieson Career Development Assistant Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) and leads the MIT Programming Systems Group. His research focuses on programming systems that address system uncertainty to enhance performance, energy efficiency, and resilience, particularly in environments involving neural networks , approximate computing , and unreliable hardware . His work spans probabilistic programming , quantum computing , and machine learning systems . Articles highlight contributions in pruning neural networks , quantum data structures , and compiler optimization , reflecting trends in deep learning , formal verification , and language-driven systems . Scientific Awards : MIT Frank E. Perkins Award (2020) Sloan Research Fellowship (2020) Facebook Research Award (2019) NSF CAREER Award (2018) Best Paper Awards at OOPSLA (2013, 2014) He has advised numerous graduate students and postdocs including Eric Atkinson, Cambridge Yang, and Charles Yuan, and served on program committees for conferences like POPL, OOPSLA, and ICLR. His group collaborates with institutions such as MIT CSAIL and explores applications in quantum algorithms and probabilistic inference .

• Robotics
• Photogrammetry
• Simultaneous Localization and Mapping (SLAM)
• +۱۲ مورد دیگر

Cyrill Stachniss is a Full Professor at the University of Bonn , where he heads the Lab for Photogrammetry and Robotics and is affiliated with the Lamarr Institute for Machine Learning and Artificial Intelligence . He was previously a Visiting Professor in Engineering at the University of Oxford until 2025. His academic journey includes positions at the University of Freiburg, University of Zaragoza, and the Swiss Federal Institute of Technology. University: University of Bonn School: Faculty of Engineering Department: Department of Photogrammetry Academic Rank: Professor His research spans robotics, photogrammetry, SLAM, autonomous navigation, perception systems, agricultural robotics, and unmanned aerial vehicles . He emphasizes probabilistic techniques for mobile robots and has made significant contributions to visual and LiDAR-based localization, scene understanding, and 3D reconstruction. The recent publications highlight a strong trend toward neural implicit representations, agricultural phenotyping, radar-based perception, and active learning . His team develops robust systems for real-world deployment in dynamic and unstructured environments, particularly in precision farming and autonomous vehicles. Scientific Awards: IEEE RAS Early Career Award (2013) Microsoft Research Faculty Fellow (2010) 7th EURON Georges Giralt Award (2008) Multiple Best Paper Awards at ICRA, IROS, RSS, and RAL Faculty Teaching Award, University of Freiburg He has advised numerous students and leads the DFG Cluster of Excellence PhenoRob and Research Unit FOR 1505 Mapping on Demand . His lab has co-founded three startups, reflecting strong industry and societal impact. He also runs the educational video series 5 Minutes with Cyrill , explaining key robotics concepts.

• Computer Vision
• Machine Learning
• Robotics
• +۴ مورد دیگر

Andreas Geiger is a Professor and Head of the Department of Computer Science at the University of Tübingen, Germany. He leads the Autonomous Vision Group (AVG) within CyberValley and is a core faculty member of the Tübingen AI Center. His roles also include PI in the ML in Science Excellence Cluster and the CRC Robust Vision, as well as ELLIS Fellow and coordinator of the ELLIS PhD program. He specializes in machine learning models for computer vision, robotics, and autonomous systems, with applications in self-driving cars, VR/AR, and scientific document analysis. Educational background: While not explicitly detailed, his positions imply a Ph.D. in Computer Science or related field. His work spans interdisciplinary collaborations with institutions like ETH Zürich, Microsoft, and the University of Bonn. Research focuses on 3D scene understanding, Gaussian splatting, generative models, and reliable autonomous systems. Notable contributions include the KITTI dataset and foundational work in neural radiance fields. Awards include the Sage 10-Year Impact Award (2024), ERC Starting Grant (2019), and IEEE PAMI Young Researcher Award (2018). Key projects include the Scholar Inbox paper recommender platform, ReSim (reliable world simulation), and advancements in 3D scene generation (e.g., UrbanCAD, PrITTI). His lab maintains a strong focus on open-source tools and datasets, such as the CARLA Route Generator. Grants and funding include support from Vector Stiftung (MINT innovation program) and EU initiatives like the ML in Science Cluster. His team collaborates internationally, with recent work presented at CVPR, SIGGRAPH, and NeurIPS.

Zeynep Akataمشاهده پروفایل

استاد

• Multimodal Learning
• Explainable Artificial Intelligence
• Zero-Shot Learning
• +۲ مورد دیگر

Zeynep Akata is the Liesel Beckmann Distinguished Professor of Computer Science at the Technical University of Munich (TUM) and Director of the Institute for Explainable Machine Learning at Helmholtz Munich. Previously she was a W3 Professor at the University of Tübingen (2019-2023) and held faculty and post-doctoral positions at the University of Amsterdam, UC Berkeley and the Max Planck Institute for Informatics. Her research focuses on multimodal learning and explainable artificial intelligence . Education: PhD, University of Grenoble / INRIA Rhône-Alpes, 2014 MSc, RWTH Aachen University, 2010 BSc, Trakya University, Turkey, 2008 Research Interests: Professor Akata’s group develops algorithms that learn from vision, language and other modalities simultaneously, with a strong emphasis on zero-shot, few-shot and continual learning . A central theme is making decisions interpretable, leading to work on explainable AI, concept bottleneck models, multimodal reasoning and human-aligned representation learning . Recent projects investigate large-scale multimodal language models, dataset distillation, model merging and continual knowledge editing. Publication Trends: Her 2024-2025 publications reveal a shift toward foundational large-scale models (diffusion, LLMs, vision-language transformers) while retaining the core themes of interpretability and generalization under limited supervision . Topics span dataset distillation, model merging, continual learning, fairness auditing of generative models and novel evaluation protocols for zero-shot learning systems. Scientific Awards: Lise-Meitner Award for Excellent Women in Computer Science (2014) Young Scientist Honour, Werner-von-Siemens-Ring Foundation (2019) ERC Starting Grant, European Commission (2019) DAGM German Pattern Recognition Award (2021) ECVA Young Researcher Award (2022) Alfried Krupp Award (2023) Advising & Funding: Prof. Akata currently supervises or co-supervises 25+ PhD students across TUM and the University of Tübingen via ELLIS and IMPRS-IS doctoral programs. She holds major grants including an ERC Starting Grant and DARPA Explainable AI funding, and is a frequent program chair and area chair for premier conferences (CVPR 2024, ECCV 2026, NeurIPS, ICML, etc.). Labs & Teams: She leads the Institute for Explainable Machine Learning at Helmholtz Munich and heads the Multimodal Learning and Explainable AI group at TUM. The institute collaborates closely with the ELLIS Institute Tübingen and Cyber Valley ecosystem, and maintains close ties with the Max Planck Institute for Intelligent Systems and Informatics.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +۵ مورد دیگر

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Martin Burgerمشاهده پروفایل

استاد

• Inverse Problems
• Applied Mathematics
• Mathematical Modelling
• +۱۵ مورد دیگر

Prof. Dr. Martin Burger is a leading scientist at DESY and a Full Professor in the Department of Mathematics at Universität Hamburg, where he leads the Computational Imaging Group. His research bridges applied mathematics, imaging sciences, and machine learning, with a focus on inverse problems, mathematical modeling, and partial differential equations. He has held professorial positions at Universität Münster and FAU Erlangen-Nürnberg prior to his current dual appointment. Full Professor, Universität Hamburg (2023–present) Leading Scientist, DESY, Hamburg (2023–present) Full Professor, FAU Erlangen-Nürnberg (2018–2023) Full Professor, Universität Münster (2006–2018) His research interests include inverse problems, variational regularization, optimal transport, kinetic models, and mathematical modeling in biology and social sciences. He has made significant contributions to imaging reconstruction, sparse neural networks, and the analysis of transformer architectures. His work often integrates theoretical analysis with computational methods, influencing both pure and applied mathematics. The most recent articles reflect a strong trend toward interdisciplinary applications, combining deep learning with PDE-based modeling, analyzing social and biological systems via kinetic and mean-field models, and advancing mathematical imaging through graph-based and optimal transport methods. His publications span high-impact venues in applied mathematics and computational science. Calderon Prize, Inverse Problems International Association (IPIA) ERC Consolidator Grant (2014) Invited speaker at ECM (2021), ICM (2022), and ICIAM (2023) Editor-in-Chief, European Journal of Applied Mathematics (since 2017) Prof. Burger has supervised numerous PhD students and postdoctoral researchers, many of whom appear as co-authors in his publications. His research is supported by major grants, including funding from the German Federal Ministry of Education and Research (BMBF). He is actively involved in collaborative projects across mathematics, physics, and engineering disciplines. He leads the Computational Imaging Group at DESY, fostering a collaborative environment for developing novel mathematical tools in imaging science. The group works on both theoretical foundations and practical implementations, contributing to advancements in tomography, machine learning, and data analysis.

Yuanbo Xiangliمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Deep Generative Modeling
• Photorealistic Scene Reconstruction
• +۳ مورد دیگر

Yuanbo Xiangli is a postdoctoral researcher at Cornell University , advised by Prof. Noah Snavely. Previously, he obtained his Ph.D. from the Multimedia Lab in the Department of Information Engineering at the Chinese University of Hong Kong (CUHK) , supervised by Prof. Dahua Lin. His research focuses on 3D computer vision and deep generative modeling for urban scene reconstruction. 3D scene reconstruction from sparse images Neural rendering and Gaussian splatting Deep generative modeling for urban environments Multi-source geospatial data processing City-scale modeling and synthetic datasets His recent work includes advanced NeRF extensions (BungeeNeRF, GridNeRF), Gaussian splatting enhancements (GSDF, Scaffold-GS), and urban scene datasets (MatrixCity, OmniCity). A pioneer in combining classical vision techniques with modern deep learning approaches. ICLR 2020 Spotlight Award Collaborates with leading researchers in photorealistic rendering, including Noah Snavely and Dahua Lin. Develops systems enabling efficient 3D reconstruction from diverse data sources like satellite imagery and street-level panoramas.

Felix Krahmerمشاهده پروفایل

استادیار

• Compressed Sensing
• Signal Processing
• Optimization
• +۴ مورد دیگر

Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).

Wolfgang Nejdlمشاهده پروفایل

استاد

• Web Science
• Information Retrieval
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Wolfgang Nejdl is a Professor at the Institute for Data Science within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover. He serves as Executive Director of the L3S Research Centre and Leibniz Forschungszentrum Inclusive Citizenship. Web Science Information Retrieval Artificial Intelligence Deep Learning His recent research focuses on AI applications in medicine , multimodal data fusion , and ethical AI systems . Projects include CAIMed (AI in Causal Medicine) and DAISEC (AI & Cybersecurity). His publications span conferences like AAMAS, WWW, and SIGIR. Notable awards include membership in the National Academy of Science and Engineering (acatech) . Former students hold positions at institutions like Stanford, TU Dresden, and ETH Zürich. Current projects involve climate resilience AI , federated learning for healthcare , and quantum-inspired data science .

Diogo Carbonera Luvizonمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• Deep Learning
• +۴ مورد دیگر

Diogo Carbonera Luvizon is a Researcher at the Max-Planck-Institut für Informatik (MPI-INF) in Saarbrücken, Germany, and a member of the Visual Computing and Artificial Intelligence (VIA) Research Center. He holds a PhD in Computer Vision and Machine Learning from CY Cergy Paris University (2019), and Bachelor's and Master's degrees in Engineering and Applied Computing from UTFPR, Brazil. His research focuses on solving complex problems in Computer Vision, Computer Graphics, and Deep Learning, particularly in human modeling and real-time systems. Education: PhD (2019) - CY Cergy Paris University; M.Sc. (2015) - UTFPR; B.Sc. (2011) - UTFPR. Research interests include 3D human pose estimation, action recognition, multitask learning, and novel view synthesis. He has contributed to patents on multiplane image generation (Samsung) and vehicle speed measurement systems. His work has been recognized with awards like the Best Paper Honorable Mention at GCPR-VMV 2022 and Best Presentation Award at ETIS Lab (2018). He has developed open-source tools, including the deephar repository for human action recognition and pose estimation. His current affiliations include MPI-INF and the VIA Research Center, a partnership between MPI-INF and Google.

Christian Waldمشاهده پروفایل

پژوهشگر ارشد

• Generative Modeling
• Flow Matching
• Stochastic Processes
• +۳ مورد دیگر

Christian Wald is a Post-doctoral researcher at Technical University Berlin working under Professor Gabriele Steidl, focusing on generative modeling, flow matching, and stochastic processes in machine learning. His research bridges theoretical probability with practical medical imaging applications, particularly in MRI reconstruction and analysis. He completed his PhD at Humboldt University of Berlin in 2017 with a thesis on p-adic quantum groups. His academic journey transitioned from pure mathematics to interdisciplinary machine learning research, reflecting his versatile expertise. Wald's primary research explores generative models through the lens of optimal transport and flow matching, with significant contributions to Wasserstein geometry and conditional distance metrics. His work frequently integrates stochastic processes to enhance medical image reconstruction, demonstrating strong cross-disciplinary impact in both theoretical machine learning and clinical applications. Recent publications highlight innovations in sliced MMD flows, Bayesian OT methods, and uncertainty-aware medical image analysis. Analysis of his 15 most recent publications (2019-2025) reveals a consistent trajectory toward unifying geometric probability with deep learning. Key themes include flow-based generative modeling for medical time-series data, optimal transport applications in image reconstruction, and novel kernel methods for distribution matching. His work spans both foundational theory (e.g., Fisher-Rao curves) and high-impact medical applications (e.g., coronary calcium scoring). No specific scientific awards are documented in the provided text, though his publications appear in prestigious venues including ICLR, IEEE TMI, and Physics in Medicine & Biology. Wald maintains extensive collaborations with the medical imaging group at Technical University Berlin, particularly with Andreas Kofler and Gabriele Steidl. His co-authored works demonstrate consistent contributions to MRI reconstruction pipelines and segmentation frameworks, though no formal advising roles or grant leadership are indicated. Current projects focus on uncertainty quantification in active learning for medical image segmentation. He operates within Gabriele Steidl's research group at Technical University Berlin, which specializes in mathematical imaging and machine learning. The team combines expertise in optimization, probability theory, and deep learning to solve medical imaging challenges, with Wald contributing core algorithmic innovations in generative modeling and stochastic reconstruction.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۱۲ مورد دیگر

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

Sandra Keiperمشاهده پروفایل

مدرس

• Compressed Sensing
• Sparse Signal Recovery
• Numerical Linear Algebra
• +۵ مورد دیگر

Sandra Keiper is a Lecturer at the Institute of Mathematics within Faculty II - Mathematics and Natural Sciences at Technical University of Berlin. She has held academic positions since at least 2011, including roles as Tutor, Assistant, and Lecturer, with teaching responsibilities in Analysis, Linear Algebra, and Partial Differential Equations for both mathematicians and engineers. Research interests include: Compressed Sensing and Sparse Signal Recovery Numerical Linear Algebra with applications to high-dimensional data Wavelet and curvelet transforms for geometric multiscale analysis Approximation theory for finite-valued and cartoon-like functions Deep learning and graph approximation techniques Professional activities : Active in teaching since 2011 (Analysis I-III, Functional Analysis, Integral Transforms) Supervising theses since 2015 on topics like Compressed Sensing and Deep Learning Invited lectures at Caltech, ETH Zurich, and Alan Turing Institute Research stays at Hausdorff Institute, ETH Zurich, and Duke University

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Min Peng is a Professor at Wuhan University's School of Computer Science. His research focuses on artificial intelligence, machine learning, natural language processing, and knowledge graphs. He has collaborated extensively with institutions like Hefei University of Technology and the University of Chinese Academy of Sciences. His work bridges theoretical advancements in AI with practical applications in finance, social media analysis, and network optimization. Recent contributions include neural-symbolic reasoning frameworks, contrastive learning for knowledge graphs, and financial benchmarking with large language models. Research interests emphasize scalable machine learning models for complex reasoning tasks, explainable AI, and domain-specific applications in finance and social networks. Over 100 publications span venues like WWW, ACL, and NeurIPS, highlighting interdisciplinary impact. Notable projects include SymAgent (neural-symbolic agent frameworks), PIXIU (financial LLM benchmark), and DTC (commonsense machine comprehension). Key technical trends include integrating large language models with structured data, temporal knowledge graph reasoning, and transfer learning across domains. His work often addresses real-world challenges in data efficiency, interpretability, and cross-domain scalability. Current efforts explore financial LLMs, agent-based reasoning systems, and multimodal applications. While no specific grants or awards are listed in the provided data, his prolific publication record indicates sustained research excellence. Collaboration networks include teams in computer science, electrical engineering, and finance disciplines.