جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Professor Andreas Geiger leads the Autonomous Vision Group (AVG) at the University of Tübingen , heading the Department of Computer Science and serving as core faculty at the Tübingen AI Center . He is Principal Investigator in the ML in Science cluster of excellence and CRC Robust Vision , while coordinating the ELLIS PhD program . Develops machine learning models for computer vision, NLP, and robotics Focus on 2D/3D representations, geometry/material reconstruction, and robust AI Applications in autonomous vehicles, VR/AR, and document analysis His research has produced hundreds of publications with significant impact, including multiple best paper awards at top venues. The Scholar Inbox platform he co-created revolutionizes academic paper discovery, winning business model awards at Tübingen AI Center spinoff events. Key research areas include: Neural rendering and 3D scene understanding Self-driving perception and planning systems Simulation frameworks for autonomous validation Efficient reinforcement learning architectures Recent awards include: CVPR 2024 Best Paper Sage 10-Year Impact Award 2024 IEEE PAMI Young Researcher Award 2018 Active in CyberValley and ELLIS Institute Tübingen , he maintains strong industry collaborations through initiatives like the ML ⇌ Science Colaboratory . His group's work appears in journals like TPAMI and conferences including SIGGRAPH 2025.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Hongbo Liu is a researcher at Indiana University - Purdue University Indianapolis , Department of Computer Information and Graphics. With a focus on Artificial Intelligence, Machine Learning, and Network Analysis , Liu has contributed extensively to computational intelligence through 118+ publications since 2004. Multi-disciplinary research spanning Graph Theory, Swarm Intelligence, and Deep Learning Recent work includes Robust Gated Models for Temporal Networks and Self-Adaptive Neuroevolution Systems (2024-2025) Key research themes include: Dynamic network analysis and link prediction Crowd behavior modeling and trajectory forecasting Swarm-based optimization for complex systems Fuzzy logic and granular computing applications Neural network architectures for image and text processing Liu's publications demonstrate strong collaborations with researchers like Ajith Abraham, Yu Yang, and Bo Zhang across 15+ academic journals and conferences . The work spans from theoretical graph algorithms (2015-2017) to applied systems in autonomous robotics and blockchain (2024).

Jonathon Shlensمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Neuroscience
• +۳ مورد دیگر

Jonathon Shlens is a principal scientist and research director at Google DeepMind, focusing on vision, language, and learning. He has led teams in deploying production systems, invented TensorFlow, and collaborated with Waymo. His work spans machine learning, computer vision, basic science, and autonomous driving. Research interests include: Machine learning with applications in multimodal and transformer models Computer vision, particularly robustness and 3D object detection Neuroscience, analyzing neural computations in the primate retina Autonomous driving systems and motion forecasting Recent articles highlight trends in: Vision-language models and semantic guidance Transformer architectures for scene flow and calibration Adversarial robustness across human and machine perception Scalable datasets and model architectures Scientific awards include: Best Paper Award at CVPR 2013 Former advisees include notable researchers now at institutions like Stanford, MIT, and OpenAI.

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.

Pascal Grittmannمشاهده پروفایل

پژوهشگر ارشد

• Global Illumination
• Monte Carlo Rendering
• Importance Sampling
• +۲ مورد دیگر

Dr. Pascal Grittmann is a Research Fellow in Computer Graphics at Saarland University, specializing in efficient and robust rendering algorithms. His research advances Monte Carlo methods, importance sampling, and bidirectional rendering techniques for global illumination. Current DFG-funded project develops adaptive bidirectional rendering solutions requiring minimal user configuration. Research aims to create universal rendering algorithms balancing efficiency and robustness across diverse scenes. Publications demonstrate innovations in variance-aware sampling, path guiding optimizations, and caustic rendering. Served as Eurographics Symposium on Rendering conference co-chair (2021). Teaching includes practical implementation of rendering techniques using Vertex Connection and Merging methods. Maintains collaborations with international computer graphics researchers.

• Explainable AI
• Machine Learning
• Social Media Analysis
• +۶ مورد دیگر

Professor Alun D. Preece is a distinguished academic at Cardiff University's Crime and Security Research Institute, UK, with significant contributions to artificial intelligence, particularly in explainable AI (XAI), social media analysis, and neuro-symbolic approaches. His research spans over three decades with continuous publication output through 2024, demonstrating sustained scholarly impact in the AI community. His research interests focus on creating transparent and interpretable AI systems that can effectively collaborate with humans in complex environments. Key areas include explainable AI methodologies, social media analysis for misinformation detection, neuro-symbolic integration for robust reasoning, and collaborative perception-cognition-communication-action frameworks. His work bridges theoretical AI foundations with practical applications in security, public safety, and coalition operations. Recent publications (2022-2024) reveal a strong focus on cutting-edge AI challenges, with particular emphasis on neuro-symbolic approaches, vector symbolic architectures, and human-AI teaming. His work consistently addresses the critical challenge of creating AI systems that are not only effective but also transparent, trustworthy, and capable of meaningful collaboration with human users. Professor Preece has established extensive collaborations across the AI research community, with notable co-authors including Dave Braines, Federico Cerutti, Ian J. Taylor, and Mani Srivastava. His research has been published in top venues including FUSION, IEEE Transactions, and AAAI workshops. His work on verifiable credentials for AI model transparency, collaborative perception frameworks, and misinformation analysis demonstrates practical applications of his theoretical contributions to real-world security and information challenges. The trajectory of his recent publications indicates continued leadership in addressing the critical challenges of trustworthy and explainable AI systems.

Le Chenمشاهده پروفایل

پژوهشگر

• Robotics
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Le Chen is a Doctoral Researcher at the Empirical Inference Department of the Max Planck Institute for Intelligent Systems and ETH Zurich , advised by Prof. Bernhard Schölkopf and Prof. Dieter Büchler. Previously, he obtained his M.S. in Electrical Engineering and Information Technology from ETH Zurich and gained research experience at Microsoft Mixed Reality & AI Lab, Tencent AI Lab, and Tencent Robotics X Lab. Research Interests: Le Chen focuses on the intersection of robotics and machine learning, with a specific emphasis on reinforcement learning for dexterous manipulation, visual-inertial calibration, and uncertainty-aware robotic perception. His work spans dynamic motion control, policy gradient subspaces, and novel algorithms for 3D/4D reconstruction. Key Contributions: He co-developed the RP1M dataset for bimanual piano manipulation and contributed to tendon-driven robot design ( Safe & Accurate ) and LEAP-VO for robust visual odometry. His research also includes Gaussian splatting dynamics for 4D content creation ( GaussianFlow ). Scientific Awards: Best Systems Paper Finalist at RSS 2024 for the RP1M dataset

Gabriele Petersمشاهده پروفایل

استاد

• Human-Computer Interaction
• Computer Vision
• Computational Neuroscience
• +۵ مورد دیگر

Prof. Dr. Gabriele Peters is a Professor of Computer Science and Chair for Human-Computer Interaction at FernUniversität in Hagen since 2010. She previously held a Research Professor position and led the Visual Computing Group at Dortmund University of Applied Sciences (2007-2010) and served as Visiting Professor at Caltech's Computational Vision Group (2004, 2005). Her research spans computer vision, computational neuroscience, cognitive systems, and new media art.

• French Language Practice
• Romance Studies
• Computer Vision
• +۳ مورد دیگر

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

Gemma Roigمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Neuroscience
• +۷ مورد دیگر

Gemma Roig is a Professor of Brain-inspired Artificial Intelligence at the Computer Science Department of Goethe University Frankfurt since 2020. She holds an honorary research affiliate position at MIT's Center for Brains Minds and Machines and is a member of hessian.ai. Her research focuses on computational models of human vision and their application to AI systems. Previously, she served as an Assistant Professor at Singapore University of Technology and Design (SUTD) and conducted postdoctoral research at MIT, exploring connections between AI and neuroscience. Education: BEng and MSc in Telecommunication Engineering from Ramon Llull University (Barcelona), PhD in Computer Vision from ETH Zurich, and extensive postdoctoral training at MIT and EPFL. Career highlights include establishing a research group in Singapore and securing a tenure position in Frankfurt after relocating for family reasons. Research interests span computational vision, neural networks, and the intersection of AI with human cognition. She leads the CVAI lab, developing tools like Net2Brain to bridge artificial and biological neural systems. Her work emphasizes ethical AI, explainability, and multimodal learning. Labs/Teams: Director of the CVAI lab; collaborator with MIT's CBMM and Ernst Strüngmann Institute. Active in interdisciplinary projects like the Algonauts Challenge. Supervises 10+ PhD students and postdocs in topics ranging from neuro-symbolic AI to molecular dynamics simulations. Grants & Funding: Supported by DFG, ERC, and DAAD scholarships. Net2Brain receives funding from German Research Foundation (DFG) and European Research Council (ERC).

• Neural Networks
• Medical Image Analysis
• Computer Vision
• +۵ مورد دیگر

Zhigang Li is a Professor in the Department of Computer Science at South China University of Technology's School of Computer Science and Engineering. His research spans multiple technical domains with significant contributions to neural networks, medical imaging, computer vision, and sensor technologies. Recent collaborations include work with Northwestern Polytechnical University, Hong Kong University of Science and Technology, and various medical research institutions. Dr. Li's research interests focus on neural network architectures, particularly small-world and feedforward networks for system modeling and medical applications. His work bridges computer science with practical applications in healthcare (EEG analysis, schizophrenia detection, liver transplant allocation), environmental monitoring (wastewater treatment), and engineering systems (CMOS image sensors, UAV networks). His research demonstrates strong interdisciplinary connections between theoretical computer science and real-world problem solving. Analysis of his recent publication trends shows increasing focus on medical applications of AI, with significant work in brain functional network analysis, depression recognition, and schizophrenia detection. His technical contributions include novel neural network architectures, efficient sensor systems, and advanced signal processing techniques. The publications reveal a consistent pattern of high-quality output in top-tier journals across multiple disciplines. Dr. Li has received recognition through publications in prestigious venues including IEEE Transactions, Medical Image Analysis, and Expert Systems with Applications, though specific awards aren't documented in the provided bibliography. His work demonstrates significant impact across multiple fields, particularly in applying computational methods to healthcare challenges. His research program includes collaborations with medical researchers for brain imaging applications, electrical engineers for sensor development, and computer scientists for network architecture design. Current projects appear focused on multi-view brain network analysis, energy-efficient sensor systems, and medical AI applications with potential clinical impact.

Ge Gaoمشاهده پروفایل

پژوهشگر

• 6D Object Pose Estimation
• Cognitive Robotic Vision
• Deep Learning
• +۵ مورد دیگر

Dr. Ge Gao is a researcher at the Department of Informatics, University of Hamburg, affiliated with the CV Research Group. His research focuses on 6D object pose estimation, cognitive robotic vision, and deep learning applications in robotics. He holds a PhD in Informatics from the University of Hamburg, completed in 2021. Education: PhD in Informatics (2021), University of Hamburg. Previously conducted research on occlusion-resistant object pose estimation and saliency-guided segmentation techniques. Research emphasizes robotics perception, point cloud analysis, and multimodal sensor fusion. His work spans applications from robotic grasp planning to gait analysis using dynamic vision sensors. Key contributions include CloudAAE framework for 6D pose regression and saliency-driven supervoxel segmentation. Publications (2015–2021) address challenges in 3D object localization, multimodal data processing, and robotic vision systems. Active in conferences like ICRA, ECCV, and IROS. Labs/Projects: Involved in InteGreatDrones (drone-based robotics), Crossmodal Learning (CML), ahoi digital (crossmodal sensor data collection), and MACS (machine learning applications).

• Computer Vision
• Machine Learning
• Teleoperation
• +۳ مورد دیگر

Dong Yang is a Researcher and PhD candidate at the Chair of Media Technology, Technical University of Munich (TUM) , since October 2021. His work bridges Computer Vision, Machine Learning, and Robotics to advance haptic teleoperation systems. B.Eng in Electrical Engineering and Automation (Fuzhou University, China) B.Sc in Electrical Engineering and Information (Technical University of Kaiserslautern, Germany) M.Sc in Electrical Engineering and Information (TUM, Germany) Research focuses on haptic communication , teleoperation , and scene understanding for robots. Key contributions include: Developing SRI-Graph for scene-robot interaction Creating ISSC for semantic shared control Advancing depth estimation in adverse weather Designing HPF-SLAM for visual navigation His publications span top conferences like ICRA , IROS , and RO-MAN , addressing topics in haptic teleoperation , sensor fusion , and collaborative robotics . Supervises thesis projects including Diffusion Model-based Imitation Learning and Scene Graph-based Real-time Scene Understanding Collaborates on projects like Centre for Tactile Internet with Human-in-the-Loop (CeTI) and Teleoperation over 5G

Luisa Verdolivaمشاهده پروفایل

دانشیار

• Image and Video Forgery Detection
• Deepfake Video Detection
• GAN Fingerprints Attribution
• +۵ مورد دیگر

Luisa Verdoliva is an Associate Professor at the Department of Industrial Engineering, University Federico II of Naples, Italy. She holds a Hans Fischer Senior Fellowship at the Technical University of Munich (TUM) Institute for Advanced Study (2020–present), hosted by Prof. Matthias Nießner. Her research focuses on multimedia forensics, deepfake detection, and generative AI analysis. She has been a visiting professor at Friedrich-Alexander-University (FAU) and a visiting scientist at Google AI's Perception team. Her research interests include image/video forgery detection, GAN fingerprints, biometrics liveness detection, and remote sensing applications. Verdoliva’s work has been funded by DARPA, Google, and the Italian Ministry of Research. She serves as Chair of the IEEE Signal Processing Society’s Information Forensics and Security Technical Committee and is an IEEE Fellow (2021). Key achievements include pioneering FaceForensics++ (a benchmark dataset for manipulated facial images), winning the IEEE Signal Processing Cup (2018), and receiving the Google Faculty Research Award (2018). Her awards span best paper recognitions at CVPR, ICIP, and ICIAP conferences. As part of TUM’s Visual Computing Focus Group, her fellowship research advances digital twin technology for built environments and AI-driven media forensics. Collaborations span academia and industry, with impactful contributions to combating synthetic media misuse and improving multimedia trustworthiness.