جست‌وجوی استاد و پژوهشگر

Mitra Baratchiمشاهده پروفایل

دانشیار

• Spatio-Temporal Data Analysis
• Machine Learning
• Mobility Data Modeling
• +۵ مورد دیگر

Mitra Baratchi is an Associate Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University. She leads the Spatio-temporal data Analysis and Reasoning (STAR) research group, co-leads the Automated Design of Algorithms (ADA) group, and founded the Special Interest Group on Spatio-Temporal Data Mining (SIG-SDTM) . PhD from University of Twente (Mobility Data) Master’s/Bachelor’s in Computer Engineering, Iran Research Interests focus on automated pattern extraction from spatio-temporal data across urban, environmental, and industrial domains. Key applications include: Automated Machine Learning (AutoML) for Earth Observations Time-Series Forecasting for public health (e.g., pandemic modeling) Urban Mobility Optimization with ESA, Honda, and municipalities Reliable Vehicular Communication Systems Smart Garments for Health Risk Detection Geocast Protocols for Internet-wide Communication Grant Highlights include €120K NWO-Aspasia, €2.9M Marie Skłodowska-Curie, €350K NWO-KLEIN, and €135K Center for BOLD Cities funding. She has supervised 12 PhD students and 4 current Master’s students since 2011, with notable best paper award at WWIC'16. Teaching includes Machine Learning (2020-present) and Urban Computing (2018-present) at Leiden, plus past courses in Data Visualization, Software Engineering, and Research Methods.

David Loمشاهده پروفایل

استاد

• AI for Software Engineering
• Mining Software Repositories
• Data Mining
• +۴ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science and the founding Director of the Center for Research in Intelligent Software Engineering (RISE) at Singapore Management University. He has held significant leadership roles including General Chair of MSR'22 and ASE'16, and Program Committee Co-Chair for ASE'20, FSE'24, and ICSE'25. Lo has championed the field of AI for Software Engineering (AI4SE) since the mid-2000s, demonstrating how data mining, machine learning, information retrieval, natural language processing, and search-based algorithms can transform software engineering data into actionable insights and automation. His research spans Mining Software Repositories (MSR), large language models for code, software testing, smart contract analysis, and developer tooling. His recent publications reveal a strong focus on the intersection of large language models and software engineering, with particular attention to code generation, evaluation, documentation, and the practical implications of AI tools for developers. His work increasingly addresses economic efficiency, privacy concerns, and human factors in AI-assisted development. Two Test-of-Time awards Eleven ACM SIGSOFT/IEEE TCSE Distinguished Paper awards ACM Fellow IEEE Fellow ASE Fellow National Research Foundation Investigator (Senior Fellow) Lo has supervised numerous students and collaborated extensively across the software engineering community. His work on Mining Software Repositories has led to practical tools and insights that have shaped the field. He regularly contributes to major conferences and has served in leadership roles across ASE, ICSE, and FSE communities. As founding Director of the Center for Research in Intelligent Software Engineering (RISE) at SMU, Lo leads a research group focused on advancing AI techniques for software engineering problems, with emphasis on practical applications that address real developer pain points.

• 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.

• Electrical Engineering
• Automotive Engineering
• Machine Learning
• +۳ مورد دیگر

Shengdun Zhao is an active researcher in the fields of Electrical Engineering, Automotive Engineering, and Machine Learning, contributing extensively to optimization techniques and control systems for electric vehicles and motors. His work spans journals like IEEE Transactions on Vehicular Technology and Journal of Intelligent & Fuzzy Systems , focusing on practical applications of deep reinforcement learning, meta-learning, and multi-objective optimization. Key research areas: Electric motor control, energy management systems, and clustering algorithms. Collaborates with researchers such as Yiming Zhang, Wei Du, Chee-Kong Chui, and Chin-Boon Chng. His publications from 2007–2025 address technical challenges in mechatronics, sustainable transportation, and data-driven engineering solutions. Research Trends Recent articles highlight Zhao's emphasis on deep reinforcement learning for motor control, meta-learning in energy systems, and evolutionary algorithms for multi-objective optimization. He integrates machine learning with automotive engineering to improve electric vehicle efficiency and motor performance.

• 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.

• Computer Vision
• Machine Learning
• Video Analysis
• +۵ مورد دیگر

Prof. Laura Leal-Taixé is an Associate Professor at the Technical University of Munich (TUM) leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Chair, promoted from a 2017 Tenure Track Assistant Professorship. Her work focuses on advancing computer vision and machine learning, particularly in video analysis, multi-object tracking, and autonomous systems. She received a Sofja Kovalevskaja Award (2017) for her project socialMaps, which integrates dynamic social data into traffic modeling. Education: B.Sc./M.Sc. in Telecommunications Engineering, Technical University of Catalonia (UPC), Barcelona Ph.D. in Information Processing, Leibniz University Hannover (2014) Postdoc at ETH Zurich (2014–2016), and Senior Researcher at TUM’s Computer Vision Group (2016–2019) Research Interests: Multi-object tracking and segmentation in videos Motion analysis and semantic segmentation for autonomous driving Deep learning for video understanding Social dynamics modeling in urban environments Awards & Grants: €1.65M Sofja Kovalevskaja Award (Humboldt Foundation, 2017) DAAD Australia-German Joint Research Scheme (2017) Multiple travel grants from CVPR and Women in Computer Vision Labs & Collaborations: Dynamic Vision and Learning Group at TUM Collaborations with ETH Zurich, Northeastern University, and NVIDIA

Yan Xiaمشاهده پروفایل

پژوهشگر

• 3D Vision
• Localization and Navigation
• Robotics
• +۲ مورد دیگر

Yan Xia is a Senior Researcher at the Computer Vision Group of the Technical University of Munich (TUM) and a Research Scientist at the Munich Center for Machine Learning. She collaborates with Prof. Daniel Cremers and previously completed her PhD at TUM under supervision of Prof. Uwe Stilla and Prof. Daniel Cremers, with a visiting period at the Visual Geometry Group of the University of Oxford under Dr. João Henriques.

• Large Language Models
• Autonomous Driving
• Computer Vision
• +۴ مورد دیگر

Xingcheng Zhou is a Research Assistant at the Technical University of Munich (TUM), affiliated with the Chair of Robotics, Artificial Intelligence and Real-time Systems since 2023. He holds an M.Sc. in Electrical and Computer Engineering from TUM (2021) and previously worked as an Industrial AI Researcher at Siemens. Research Interests: Focus on Large Language Models , Vision Language Models , 3D Environment Perception , and Domain Adaptation in autonomous driving contexts. Publications: Contributions to 3D object detection refinement, sim2real domain adaptation, vision-language models, and dataset development for intelligent transportation systems. Teaching Involvement: Co-supervisor for master's theses and seminars on autonomous agents, perception models, and traffic environment understanding. Advising: Mentoring students on projects including LiDAR-guided monocular detection, world models, and multimodal benchmarks for transportation scenes. Trends in Research: Zhou's work bridges low-light image enhancement with spatial-frequency features, surface-aware frameworks for 3D detection, and weakly-supervised domain adaptation. He contributes to benchmarking spatio-temporal video understanding and evaluating autonomous driving datasets. Supervision and Collaboration: Co-authored key surveys and frameworks with Prof. Alois C. Knoll and peers, focusing on real-time roadside LiDARs, graph-based object relationships, and vision-language integration for traffic analysis.

Aysim Tokerمشاهده پروفایل

پژوهشگر

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

Aysim Toker is a Ph.D. candidate at the Chair for Computer Vision and Artificial Intelligence , affiliated with the Technical University of Munich . She works under the supervision of Prof. Dr. Laura Leal-Taixe and Prof. Dr. Xiaoxiang Zhu on interdisciplinary projects. Research Focus: Deep learning, sequence analysis, and remote sensing. Key Contributions: Advancing video object segmentation, Earth observation models, and anonymization techniques. Collaborations: Partnered with international researchers across multiple institutions. Publications span top venues like eLife , ICCV , NeurIPS , and CVPR , with a focus on: Deep learning architectures for multi-modal tasks Image and video analysis in remote sensing and anonymization Tracking and segmentation algorithms Her work bridges foundational computer vision research with applications in Earth science and privacy-preserving technologies.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Sio Kei Im is an active researcher with a focus on computer science, machine learning, and human-computer interaction. His recent work spans multiple domains including image processing, quantum computing, and virtual reality. Publications address advanced data augmentation (LogicMix), multi-modal quantum watermarking (MMQW), and efficient neural decoding algorithms (TRHyper). Research interests include time series optimization, dialogue summarization, and haptics in VR environments. Collaborations with experts in linguistics, electrical engineering, and software development indicate interdisciplinary expertise. Key contributions involve adaptive algorithms for AI model protection, speaker recognition systems, and real-time 3D rendering techniques.

• Computer Graphics
• Augmented Reality
• 3D Modeling and Reconstruction
• +۵ مورد دیگر

Kwan H. Lee is a prominent researcher in computer graphics and augmented reality, affiliated with the University of Alberta in Canada. With a publication record spanning over three decades (1990-2020), he has established himself as a leading figure in visual computing research with 56 documented publications. His research interests focus on Computer Graphics , Augmented Reality , 3D Modeling and Reconstruction , Computer Vision , and Cultural Heritage Computing . Dr. Lee's work demonstrates particular expertise in material appearance modeling, projection mapping techniques, and spatial augmented reality applications. His research has evolved from foundational work in manufacturing systems to cutting-edge applications in cultural heritage preservation and industrial visualization. Analysis of his recent publications (2015-2020) reveals a strong emphasis on practical applications of augmented reality in construction, museum exhibitions, and cultural heritage preservation. His work consistently bridges theoretical computer graphics concepts with real-world implementations, particularly in projection mapping systems and 3D reconstruction techniques. The publications show increasing focus on industrial applications and cultural heritage preservation in his later career. Dr. Lee has collaborated extensively with researchers from Korean institutions, suggesting strong international connections. His co-author network includes Yong Yi Lee, Jong Hun Lee, Min Ki Park, and other frequent collaborators who appear across multiple publications spanning more than a decade.

• Computer Science
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Yang Cao is a Professor at the University of Science and Technology of China , Department of Automation, Hefei, China. He holds a PhD from Northeastern University (2004, Shenyang, China) and has active affiliations with institutions like Virginia Tech and Huazhong University of Science and Technology. Research Focus: Spatiotemporal modeling, event-based vision, 3D human-object interaction, and industrial defect detection. Publications: 15 recent articles highlight his work in diffusion models, transformers, and state-space networks for tasks like traffic emission imputation, eye tracking, and PCB defect detection. Collaborative Work: Co-authored with Zheng-Jun Zha, Wei Zhai, Yu Kang, and others in journals like IEEE Transactions on Neural Networks and CVPR Workshops. Scientific Contributions: His research bridges computer vision, machine learning, and industrial applications, emphasizing real-world challenges such as low-light enhancement and sensor fusion.

Mai Buiمشاهده پروفایل

مدرس

• Computer Vision
• Medical Image Analysis
• Surgical Robotics
• +۵ مورد دیگر

Dr. Mai Bui is a Lecturer at the Deutsches Herzzentrum München (German Heart Center Munich), affiliated with the Chair of Computer Science Applications in Medicine under Prof. Nassir Navab. Her research focuses on integrating advanced computer vision and deep learning techniques into medical applications, particularly in surgical robotics, image-guided interventions, and medical augmented reality. She has contributed to critical areas like catheter tracking, pose estimation, and real-time medical imaging analysis. Dr. Bui teaches multiple courses including Computer Aided Medical Procedures , Medical Augmented Reality , and Introduction to Surgical Robotics . Her work emphasizes low-dose imaging solutions and robust navigation systems for minimally invasive procedures. She collaborates with interdisciplinary teams at labs such as DHM, IFL Lab, and NARVIS Lab to advance translational research in medical AI. Her recent publications highlight innovations in adversarial networks for pose refinement, lightweight camera localization systems, and multimodal inference in ambiguous medical scenes. These contributions address core challenges in surgical navigation and medical device tracking.

Puze Liuمشاهده پروفایل

پژوهشگر

• Robotics
• Reinforcement Learning
• Control and Optimization
• +۳ مورد دیگر

Puze Liu is a Senior Research Scientist and Deputy Head at the System AI for Robot Learning (SAIROL) department of the German Research Center for Artificial Intelligence (DFKI) , with a secondary affiliation at the Intelligent Autonomous Systems group of the Technische Universität Darmstadt . He completed his Ph.D. at TU Darmstadt under Prof. Jan Peters , focusing on safe reinforcement learning for real-world robotics. Education: Ph.D., Intelligent Autonomous Systems, TU Darmstadt M.Sc., Computational Engineering Science, Technische Universitaet Berlin Research Interests include robotics , reinforcement learning , safe exploration , and human-robot collaboration , with a focus on deploying learned policies in dynamic environments. His work addresses safety in high-dimensional tasks through constraint manifolds and implicit neural representations. Publication Trends span safe reinforcement learning , robot air hockey , kinodynamic planning , and reactive motion generation . Notable contributions include ReDSDF for real-time collision avoidance and ATACOM for constraint manifold control. Scientific Awards Best Paper Award Finalist (CoRL 2021) Best Entertainment and Amusement Paper Award Finalist (IROS 2021) IROS Student Travel Award (2022) Supervision includes mentoring students on topics like safe policy search , air hockey planning , and robot dynamics estimation . He is part of the SAIROL and Intelligent Autonomous Systems teams.

Dominic Henzeمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Smart Environments
• Machine Learning Applications
• +۱ مورد دیگر

Dominic Henze is a Professor at the Technical University of Munich (TUM), affiliated with the Faculty of Informatics and the Chair of Software Engineering . He leads research initiatives in Cyber-Physical Systems , Smart Environments , and Machine Learning Applications , with a particular focus on Fog Computing architectures. His research spans theoretical frameworks and real-world implementations, including collaborations with institutions like Carnegie Mellon University and industry partners such as Siemens AG and Zeiss IMT . His work addresses challenges in resource allocation , predictive maintenance , and blockchain integration within industrial contexts. Dr. Henze's publications reveal a consistent focus on Fog Computing architectures, IoT resource management, and educational software engineering. Key trends include self-organizing network systems , decentralized supply chain traceability , and smart environment coordination . He has advised numerous students on topics ranging from QoS negotiation to autonomous drone coordination . As an educator, he has taught multiple iPraktikum courses and seminars on iOS development and Agile Project Management since 2014, with publications exploring team composition strategies and distributed programming pedagogy.