جست‌وجوی استاد و پژوهشگر

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

Joachim Weickertمشاهده پروفایل

استاد

• Image Processing
• Computer Vision
• Mathematical Modeling
• +۴ مورد دیگر

Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.

• Autonomous Driving
• Sensor Fusion
• Intelligent Transportation Systems
• +۵ مورد دیگر

Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.

• Medical Image Analysis
• Computer Vision
• Deep Learning
• +۷ مورد دیگر

Dimitris N. Metaxas is a Professor in the Department of Computer Science within the School of Arts and Sciences at Rutgers University. His research spans computer vision, medical image analysis, and artificial intelligence, with a particular focus on medical applications including cardiac MRI analysis and foundation models for healthcare. Dr. Metaxas's research interests encompass medical image analysis, computer vision, deep learning, and artificial intelligence. His work demonstrates a strong emphasis on applying advanced machine learning techniques to medical imaging problems, particularly in cardiac analysis. He has made significant contributions to diffusion models, multimodal learning, and efficient AI techniques for medical applications. His research bridges the gap between theoretical computer vision and practical healthcare solutions, with numerous publications in top-tier conferences and journals. His recent publications show a clear trend toward foundation models for medical image analysis, with significant contributions to cardiac MRI segmentation, diffusion models, and multimodal learning. The research spans both theoretical advancements in AI techniques and practical applications in healthcare, particularly focused on improving medical diagnostics through computer vision. His work demonstrates expertise in adapting cutting-edge AI techniques like diffusion models and large language models for specialized medical applications. Dr. Metaxas has mentored numerous students and researchers, as evidenced by his extensive publication record with multiple co-authors across various institutions. His work has received significant attention in the research community, with numerous publications in top venues including CVPR, ICCV, MICCAI, and Medical Image Analysis. His research group focuses on medical image computing, computer vision, and machine learning applications in healthcare. The team works extensively with cardiac MRI data, developing advanced techniques for segmentation, reconstruction, and analysis of 4D cardiac imaging. They are particularly known for their contributions to foundation models in medical imaging and efficient adaptation techniques for specialized medical tasks.

• Building Information Modeling (BIM)
• Civil Engineering Informatics
• Tunneling and Underground Construction
• +۵ مورد دیگر

Markus König is a Professor of Informatics in Civil Engineering at Ruhr University Bochum, where he has been researching and teaching since October 2009. His work focuses on Building Information Modeling (BIM), digital construction technologies, and civil engineering informatics, with significant contributions to the development and implementation of digital methods in German construction industry. Dr. König earned his degree in civil engineering with a focus on applied computer science at Leibniz University Hannover, where he also completed his doctorate on cooperative building planning at the Institute for Building Informatics. He subsequently held a junior professorship for Theoretical Methods of Project Management at Bauhaus University Weimar before joining Ruhr University Bochum. His research spans multiple cutting-edge areas including Building Information Modeling (BIM), construction process simulation, tunneling informatics, infrastructure asset management, and the application of artificial intelligence and computer vision in civil engineering. As chair of the Building Informatics Working Group from 2012-2016, he played a key role in developing the first national BIM curriculum for German universities and serves as editor of the book 'Building Information Modeling: Technological Foundations and Industrial Practice.' Analysis of his recent publications reveals a strong trend toward semantic technologies, digital twins, automated compliance checking, and the integration of AI in construction processes. His work increasingly focuses on information containers, ontology development, and the application of large language models to infrastructure data, reflecting the evolving landscape of digital construction. Dr. König's significant contributions to digital construction have been recognized with prestigious awards: Lower Saxony-Bremen Construction Industry Award (2017) for 'services in the development and introduction of digital construction in Germany' Konrad Zuse Medal (2020) While specific details about his advising and grant activities aren't explicitly mentioned in the provided text, his extensive publication record with numerous co-authors suggests active supervision of doctoral students and research staff. His involvement in multiple collaborative research projects is evident from his publication history. At Ruhr University Bochum, Professor König leads a research group focused on civil engineering informatics, with particular emphasis on BIM, digital construction technologies, and their application across the building lifecycle. His team appears to work at the intersection of computer science and civil engineering, developing innovative solutions for construction process optimization, infrastructure management, and digital transformation of the AEC industry.

• Machine Learning
• Explainable AI (XAI)
• Deep Learning
• +۸ مورد دیگر

Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.

• Heterogeneous Serverless Computing
• Deep Learning
• Real-time Autonomous Driving Systems
• +۳ مورد دیگر

Jianfeng Gu is a Ph.D. Candidate and researcher at the Technical University of Munich (TUM), affiliated with the Department of Computer Science and specifically the Chair of Computer Architecture and Parallel Systems led by Prof. Martin Schulz. He maintains an active research profile with numerous publications and contributes to the academic community through teaching seminars on Cloud Computing. His academic path began with a Bachelor of Software Engineering from Sun Yat-sen University in China (2014-2018), followed by a Master of Engineering from the same institution (2018-2020). Since April 2021, he has been pursuing his Ph.D. at TUM, advancing research in computing systems and architectures. Gu's research focuses on Heterogeneous Serverless Computing for Deep Learning applications, specializing in GPU, FPGA, and NPU technologies within serverless environments. His work addresses critical challenges in resource allocation, auto-scaling, and performance optimization for serverless inference systems. Additionally, he investigates Real-time Autonomous Driving Systems , developing advanced perception techniques through sensor fusion (particularly stereo-LiDAR fusion) for high-precision depth sensing and object detection in autonomous vehicles. His interdisciplinary approach bridges hardware acceleration, cloud infrastructure, and AI applications. His publication trajectory shows a progression from foundational computer vision and autonomous driving research (2018-2020) toward increasingly sophisticated work on serverless computing and federated learning (2021-2025). Recent publications focus on efficient resource sharing in heterogeneous serverless environments, with particular attention to GPU and FPGA allocation strategies that maintain service level objectives while optimizing costs. His work demonstrates strong technical depth across multiple computing domains. Best Paper Award at IEEE/ACM DATE 2021 15+ publications with 185+ citations Research featured in top venues for computer architecture and cloud computing As a Ph.D. researcher, Gu teaches seminars on Cloud Computing (IN2107) and contributes to multiple research projects at TUM's Chair of Computer Architecture and Parallel Systems. His work is supported by the department's research infrastructure and collaborations with faculty including Prof. Martin Schulz and Prof. Michael Gerndt. Gu works within TUM's advanced computing research environment, contributing to projects related to high-performance computing, serverless architectures, and autonomous systems. His research group maintains specialized hardware and software infrastructure for evaluating modern HPC architectures and accelerators, including FPGA clusters and GPU resources for deep learning research.

Iliyan Georgievمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Light Transport Simulation
• Monte Carlo Rendering
• +۴ مورد دیگر

Iliyan Georgiev is a research scientist at Adobe, specializing in advanced computer graphics and physically based rendering. He holds a Bachelor's degree in Computer Science from Sofia University, Bulgaria, and a Master's degree from Saarland University, Germany, supported by a fellowship from the Max-Planck Institute. His work focuses on improving rendering efficiency through Monte Carlo methods, light transport simulation, and neural rendering techniques. Georgiev's research bridges the gap between theoretical and applied graphics, with contributions to bidirectional rendering algorithms, importance sampling, and 3D scene modeling. His publications highlight innovations in variance reduction, path sampling, and material-aware rendering. He has collaborated with leading institutions and companies, including Intel Visual Computing Institute, Disney Research Zürich, Weta Digital, Chaos Group, and Autodesk. Notable scientific awards include the Best Student Paper Award at ICPRAM 2025 and the Best Paper Award at EGSR 2024.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.

• Computer Vision
• Artificial Intelligence
• Multimodal Learning
• +۴ مورد دیگر

Ke Yan is an Associate Professor in the Department of Computer Science at the National University of Singapore's College of Design and Engineering. With extensive research output from 2021-2026, their work spans computer vision, artificial intelligence, and multimodal learning systems. National University of Singapore (Primary Affiliation) Collaborations with University of Electronic Science and Technology of China Research ties with Tencent Youtu Lab in Shanghai Research focuses on advancing computer vision techniques, particularly in medical image analysis, vision-language integration, and fault diagnosis systems. Their work bridges theoretical AI development with practical applications in healthcare, industrial systems, and environmental monitoring. Notable contributions include novel approaches to multimodal learning, medical image segmentation with sparse annotations, and improving reliability of large vision-language models. Recent publication trends (2024-2026) demonstrate increasing focus on multimodal systems, with significant contributions to vision-language model reliability, medical AI applications, and efficient transfer learning techniques. Their work frequently addresses critical challenges like hallucination mitigation in large models and sparse data scenarios in medical imaging. As an advisor, they mentor multiple researchers including Junlong Du, Shouhong Ding, and Zhiwen Lin, with whom they frequently collaborate on cutting-edge computer vision projects. Their research group maintains strong industry connections, particularly with Tencent's AI research division. The research is conducted within NUS's computer vision and AI research ecosystem, collaborating with multiple laboratories focused on multimodal intelligence and practical AI deployment in real-world systems.

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

Dr. Marc Habermann is a Tenured Senior Researcher and Scientific Manager of the Real Virtual Lab at the Max Planck Institute for Informatics (Department 6: Visual Computing and Artificial Intelligence). He leads the Graphics and Vision for Digital Humans research group, focusing on cutting-edge technologies in Computer Vision, Computer Graphics, and Machine Learning. His work emphasizes real-time human performance capture, photorealistic animation synthesis, and generative 3D human models derived from video data. Research Interests: Computer Vision, Computer Graphics, Machine Learning, Human Performance Capture, Non-Rigid Deformation Reconstruction, Neural Rendering, and Motion Capture. His contributions span topics like Gaussian splats optimization, sparse-view avatar synthesis, and physics-based cloth simulation. Awards: Saarland University Associate Fellow (2025) Key Publications (2025): Second-order Optimization of Gaussian Splats with Importance Sampling (arXiv) GIGA: Generalizable Sparse Image-driven Gaussian Avatars (arXiv) EVA: Expressive Virtual Avatars from Multi-view Videos (Siggraph 2025) Labs & Projects: Manages the Real Virtual Lab and heads the Graphics and Vision for Digital Humans group, advancing technologies for digital human avatars and immersive telepresence systems.

Simon Bultmannمشاهده پروفایل

پژوهشگر

• Collaborative Perception
• Sensor Fusion
• Robot Perception
• +۴ مورد دیگر

Dr. Simon Bultmann is currently a Postdoctoral Researcher at the Robot Learning Lab, Albert-Ludwigs-Universität Freiburg. Previously, he completed his Ph.D. in the Autonomous Intelligent Systems Group at the University of Bonn (2019–2024) and earned his M.Sc. and B.Sc. in Electrical Engineering and Information Technology from Karlsruhe Institute of Technology (KIT, 2012–2018). His research focuses on collaborative perception, sensor fusion, and machine learning for embedded systems ("Smart Edge Sensors"). Key applications include real-time multi-modal semantic fusion, 3D scene perception, and autonomous UAVs. Awards: Best Paper Award at IEEE SSRR 2020 Finalist for Best Paper Award at RoboCup 2021 His work spans robotics, computer vision, and distributed systems, with contributions to international conferences like ICRA, RSS, and IAS. He has also been involved in competitions such as MBZIRC, demonstrating autonomous UAV capabilities in disaster response scenarios.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Bo Wang is an active academic researcher primarily affiliated with multiple Chinese institutions, with strong connections to Tsinghua University, Beijing Jiaotong University, and other leading Chinese universities. His research spans artificial intelligence, machine learning, computer vision, medical image analysis, and intelligent control systems, demonstrating significant interdisciplinary work across computer science, engineering, and biomedical applications. Primary institutional affiliation: School of Computer Science and Technology at multiple Chinese universities Active research areas: AI/ML applications in healthcare, computer vision, federated learning, and intelligent control systems Extensive publication record across top-tier venues in multiple disciplines Wang's research interests focus on the intersection of artificial intelligence and practical applications. His work demonstrates strong expertise in developing novel machine learning architectures for medical image analysis, including applications in CT imaging, MRI, and sperm tracking. He has made significant contributions to federated learning approaches for large language models, sliding mode control systems, and molecular optimization frameworks. His research consistently bridges theoretical advances with practical implementations across healthcare, manufacturing, and environmental monitoring domains. Analysis of Wang's recent publications reveals a strong trend toward interdisciplinary AI applications, particularly in medical imaging and bioinformatics. His work on VAE-GANMDA for microbe-drug association prediction, ACE-QSM for accelerating MRI acquisition, and text-guided molecular optimization demonstrates innovative approaches at the intersection of AI and life sciences. Wang also maintains active research in industrial applications including digital twin technology for energy systems and robust scheduling approaches for multi-factory production. Notable research contributions include: FLFT: A Large-Scale Pre-Training Model Distributed Fine-Tuning Method with Federated Learning VAE-GANMDA: Microbe-drug association prediction model ACE-QSM: Accelerating quantitative susceptibility mapping using diffusion models Digital twin-empowered power consumption prediction systems Wang actively collaborates with researchers across China and internationally, with publications spanning computer science, engineering, medical imaging, and environmental science journals. His work demonstrates strong technical depth across multiple AI methodologies while maintaining focus on practical applications that address real-world challenges in healthcare, manufacturing, and environmental monitoring.

Theresa Noegelمشاهده پروفایل

پژوهشگر

• Radar Imaging Systems
• Radar Signal Processing
• Automotive Radar

Theresa Noegel is a Doctoral Candidate and Researcher at the Institute of Microwaves and Photonics (LHFT) within the Department of Electrical-Electronic-Communication Engineering at Friedrich-Alexander University Erlangen-Nuremberg (FAU). She has been with LHFT since March 2023, contributing to cutting-edge radar research for automotive applications. Her educational background includes: B.Sc. in Electrical Engineering and Information Technology (EEI) from 2016 to 2020 M.Sc. in Electrical Engineering and Information Technology (EEI) from 2020 to 2023 Theresa's research focuses on Radar Imaging Systems , Radar Signal Processing , and Automotive Radar , with emphasis on Synthetic Aperture Radar (SAR) techniques. Her work spans 3D imaging implementation, real-time processing algorithms, and joint communication-sensing integration using OFDM waveforms, addressing critical challenges in autonomous vehicle perception systems. Analysis of her 2022-2024 publications reveals a clear progression from foundational SAR image segmentation toward advanced 3D imaging and forward-looking accumulation methods, demonstrating increasing technical sophistication in automotive radar applications through both simulation and hardware implementation. As an active member of the Institute of Microwaves and Photonics research team, she collaborates on developing next-generation radar technologies for automotive safety systems, working closely with senior researchers including M. Hoffmann and M. Vossiek on real-world implementation challenges.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Jing Zhao is a Professor at Beijing Institute of Technology's School of Automation, Department of Computer Science, with an extensive publication record spanning artificial intelligence, computer vision, medical imaging, and robotics. Their interdisciplinary research bridges theoretical computer science with practical applications in healthcare, agriculture, and engineering. Research interests focus on cutting-edge AI methodologies including deep learning architectures, neural rendering techniques, and multimodal systems. Notable work includes advancements in 3D reconstruction (Gaussian Splatting), medical image analysis for cancer diagnosis, and novel approaches to EEG-based emotion recognition. The research demonstrates strong interdisciplinary connections between computer science and domain-specific applications. Recent publications reveal a consistent trend toward practical AI implementations across diverse fields. Jing Zhao's work shows expertise in adapting theoretical machine learning concepts to solve real-world problems in medical diagnostics, agricultural monitoring, and robotics. The research portfolio demonstrates particular strength in developing novel neural network architectures for specialized applications. As a corresponding author on numerous high-impact publications, Jing Zhao appears to lead a productive research group with collaborations spanning multiple institutions and disciplines. The publication record indicates significant contributions to both theoretical computer science and applied AI solutions across various domains.