جست‌وجوی استاد و پژوهشگر

Rishabh Dabralمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• Computer Graphics
• Multi-Modal Interactions
• +۶ مورد دیگر

Rishabh Dabral is a Research Group Leader at the Max Planck Institute for Informatics since August 2024, leading the "3D Visual Intelligence" group. He is also affiliated with the Research Training Group on Neuro-Explicit Models of Language, Vision, and Action at Saarland University. Expertise: 3D computer vision, computer graphics, human-object interaction modeling, and motion synthesis. Leadership: Conducts cutting-edge research on 3D human performance capture and physical plausibility in motion. His research focuses on: 3D human pose estimation under gravity constraints Multi-modal gesture synthesis using neural architectures Quantum auto-encoding for 3D representations Wearable robotics informed by human behavior Temporal dynamics in human-object interaction Recent publications at top venues like SIGGRAPH , CVPR , and ICCV demonstrate his work on: Music-driven motion synthesis Egocentric motion capture systems Reactive two-person interaction models Diffusion-based gesture generation Object-aware motion prediction Wearable robotic limb design

• Intelligent Transportation Systems
• Connected and Autonomous Vehicles
• Vehicular Networks
• +۶ مورد دیگر

Daxin Tian is a prominent professor at Beihang University's School of Transportation Science and Engineering, specializing in intelligent transportation systems and vehicular networks. With over 170 publications spanning from 2006 to 2025, his research has significantly contributed to the advancement of connected and autonomous vehicle technologies. His work appears consistently in top-tier IEEE journals including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, and IEEE Internet of Things Journal, establishing him as a leading authority in the field. Professor Tian's research interests encompass several critical areas in modern transportation technology: Connected and Autonomous Vehicle Systems Vehicular Networking and Communication Protocols Vehicle Platooning and Cooperative Driving Algorithms Edge Computing Applications for Transportation Computer Vision for Autonomous Driving Perception Traffic Flow Optimization and Prediction Models Resource Allocation in Vehicular Networks His recent publications demonstrate an increasing sophistication in addressing complex multi-vehicle scenarios while maintaining practical considerations like communication reliability, energy efficiency, and safety constraints. The research trajectory shows a clear evolution from foundational networking and control problems toward more integrated AI-driven solutions that combine computer vision, natural language processing, and advanced control theory for next-generation transportation systems. Professor Tian maintains extensive international collaborations, particularly with researchers at Canadian institutions including Victor C. M. Leung's group, while leading a substantial research team at Beihang University. His work frequently bridges theoretical advances with practical transportation challenges, resulting in numerous high-impact publications that address real-world implementation barriers in intelligent transportation systems.

Abdelhak M. Zoubirمشاهده پروفایل

استاد

• Statistical Signal Processing
• Cooperative Sensor Communications
• Radar Imaging
• +۵ مورد دیگر

Abdelhak M. Zoubir is a Professor of Signal Processing and Head of the Signal Processing Group at Technische Universität Darmstadt, Germany. He has held leadership roles including Head of the Department of Electrical Engineering and Information Technology (2012–2014 and 2020–2022), and President of the European Association for Signal Processing (EURASIP, 2017–2018). His research focuses on statistical signal processing with applications in radar imaging, biomedical engineering, and automotive systems. Zoubir has authored over 500 publications and is a Fellow of IEEE and EURASIP. He currently leads projects on radar communication integration, robust signal processing algorithms, and radiation-hardened sensor development. Education: Dipl.-Ing. (BSc/MSc) from Fachhochschule Niederrhein and Ruhr-Universität Bochum, followed by a Dr.-Ing. (PhD) in Electrical Engineering from Ruhr-Universität Bochum (1992). Research Interests: Bootstrap techniques, robust detection/estimation, cooperative sensor networks, radar for landmine detection, and automotive safety systems. He has pioneered methods in robust statistical signal processing, including low-rank matrix completion and sparsity-aware algorithms. Recognition: Recipient of the IEEE Meritorious Service Award (2018), IEEE Signal Processing Magazine Best Paper Award (2017), and the M. Barry Carlton Award (2014). He has been a keynote speaker at major conferences such as ICASSP and EUSIPCO, and served as Editor-in-Chief of the IEEE Signal Processing Magazine (2012–2014). Current Projects: Focus on automotive radar signal processing, radiation-hardened sensors (MALTA), and distributed learning robustness. His work bridges theoretical advancements with practical applications in defense, healthcare, and automotive industries.

• 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.

• Biomedical Engineering
• Machine Learning
• Wearable Technology
• +۳ مورد دیگر

Muhammad Awais Bin Altaf is a researcher specializing in biomedical engineering, machine learning, and wearable technology. His work focuses on low-power embedded systems for neurological and cardiovascular monitoring, including EEG processors for seizure detection and PPG-based blood pressure classification. He has collaborated extensively with co-authors like Wala Saadeh and Jerald Yoo on IEEE journals and conferences. His research interests include Biomedical signal processing Wearable health devices Machine learning for medical diagnostics Energy-efficient hardware design Neurological disorder detection Embedded systems for clinical applications Recent publications highlight trends in shallow neural networks, autoencoders, and hardware acceleration for real-time health monitoring. Key subfields span seizure prediction, stress detection, and impedance-adaptive sensors. Collaborations include institutions in Germany, Finland, and Pakistan. His work often integrates open-source toolflows and industry-standard chip design techniques, emphasizing practical implementations for wearable environments. Contributions to HDR imaging algorithms and biomedical SoCs demonstrate interdisciplinary expertise in signal processing and healthcare technology.

Anestis Terzisمشاهده پروفایل

استاد

• Digital System Design
• FPGA Technology
• Camera Monitor Systems
• +۶ مورد دیگر

Professor Anestis Terzis serves as a Professor of Digital Systems Design and Head of the Institute for Communication Technology (IKT) at Technische Hochschule Ulm within the Faculty of Electrical Engineering and Information Technology. He coordinates the International Electrical Engineering Program and is responsible for the Vehicle Systems specialization. His office is located in Room W2405 at Albert-Einstein-Allee 53-55, 89081 Ulm, Germany. Professor Terzis specializes in digital system design with focus on FPGA, VHDL, and high-level design methodologies. His research spans Camera Monitor Systems (CMS) for automotive mirror replacement, advanced camera-based driver assistance systems, vehicle electronics, digitalization in laboratory didactics, and autonomous driving technologies. He has pioneered work in digital mirror systems compliant with ISO 16505 standards and has contributed significantly to image processing for automotive applications. His publication record demonstrates consistent contributions to automotive imaging technology, with recent work focusing on image compression impacts on detection quality, CMS image quality parameters, and programmable processing for autonomous vehicles. Professor Terzis has edited the comprehensive Handbook of Camera Monitor Systems published by Springer, establishing himself as a leading authority in this specialized field. As Head of the Institute for Communication Technology, he leads research initiatives connecting digital systems design with automotive applications. He also serves as Coordinator for the Study with In-depth Practice program and is a member of the Institute for Vehicle System Technology (IFS), demonstrating his commitment to both theoretical advancement and practical implementation in automotive electronics.

Ulrike Pestel-Schillerمشاهده پروفایل

پژوهشگر

• Hyperspectral Imaging
• Image Processing
• Video Coding
• +۲ مورد دیگر

Dr. Ulrike Pestel-Schiller is a researcher at the Institute for Information Processing, Leibniz University Hannover, Germany, where she has been employed since 1996. Her work focuses on hyperspectral and Synthetic Aperture Radar (SAR) image processing, coding, and evaluation, with significant contributions to remote sensing applications. She actively supervises bachelor's and master's theses in these fields. Her academic background includes: Electrical Engineering and Communications Engineering studies at University of Hannover Dipl.-Ing. (Master's equivalent) awarded in 1989 Dr.-Ing. (Doctorate) completed in 1997 with dissertation on filter bank optimization for subband coding Her research centers on hyperspectral image data processing, coding efficiency, and usability evaluation for human interpreters. She investigates how compression techniques (HEVC, JPEG) impact SAR image usability, often finding counterintuitive results where compression improves interpretability. Recent work integrates deep learning, particularly CNNs, for spectral-spatial analysis in hyperspectral data and fruit classification. Her early career focused on HDTV video coding standards development. Analysis of her publication trends reveals a clear evolution from foundational HDTV subband coding research (1990s) to contemporary hyperspectral/SAR applications. A dominant theme is human-centered evaluation of compressed imagery, with 70% of her 2018-2023 publications examining interpreter performance. She increasingly employs deep learning for band selection and semantic segmentation, while maintaining core expertise in image compression algorithms. No scientific awards were documented in the source material. Dr. Pestel-Schiller supervises undergraduate and graduate theses in hyperspectral/SAR processing but no specific grant funding or formal advising records were provided. Her research appears institutionally supported through the Institute for Information Processing. The Institute for Information Processing serves as her primary research base, collaborating on projects involving drone remote sensing, VideoSAR stabilization, and hyperspectral band optimization. Current work emphasizes practical applications where image compression directly impacts interpreter effectiveness in remote sensing scenarios.

Marc Rodemerمشاهده پروفایل

مدرس

• Organic Chemistry Education
• Chemistry Didactics
• Cognitive Science
• +۳ مورد دیگر

Dr. Marc Rodemer is an Academic Councillor at the University of Duisburg-Essen, focusing on organic chemistry education and learning processes. He holds a PhD from the Leibniz Institute for Science and Mathematics Education (IPN) in Kiel, with prior degrees in Chemistry and Biology from the Christian-Albrechts-University, Kiel. His research emphasizes cognitive learning processes, instructional design with digital media, and teacher professionalization. He co-coordinates the DFG network ROChET, advancing organic chemistry education research. Key interests include learning from mistakes, analysis of cognitive processes via eye-tracking, and developing adaptive instructional tools. Rodemer teaches courses such as Special Organic Chemistry and R for Beginners, and supervises bachelor’s/master’s theses. He actively contributes to conferences like NARST and GDCP, publishing on topics like student difficulties in organic chemistry and the efficacy of tutorial videos. Education: Dr. rer. nat. (2021), Leibniz Institute for Science and Mathematics Education (IPN), Kiel M.Ed. (2017), Chemistry & Biology, Christian-Albrechts-University, Kiel B.Sc. (2015), Chemistry & Biology, Christian-Albrechts-University, Kiel Research Interests: Learning from errors and conceptual representations Digital media in education Cognitive processes (eye-tracking, pupil dilation) Teacher diagnostic skills and professional development Publications and Talks: Over 20 peer-reviewed articles and conference contributions since 2018, focusing on organic chemistry education, instructional video design, and student learning mechanisms. Recent work explores representation challenges in digital formats and adaptive support tools. Grants and Teams: Co-leads the ROChET DFG network, collaborating on projects like the development of organic chemistry competency tests and error-based learning strategies. Engaged with labs/teams in chemistry education research and teacher training.

• 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 Vision
• Medical Image Analysis
• Pattern Recognition
• +۵ مورد دیگر

Jaime S. Cardoso is a prominent researcher at the University of Porto and Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) in Portugal. His extensive publication record spanning two decades demonstrates his leadership in computer vision, medical image analysis, and pattern recognition. His research primarily focuses on applying artificial intelligence to healthcare challenges, particularly in medical imaging and diagnostics. Cardoso's research interests center on explainable AI for medical applications, biometrics, and computer vision. His work bridges the gap between theoretical machine learning and practical medical solutions, with significant contributions to breast cancer diagnosis, medical image segmentation, and biometric security systems. He has developed innovative approaches to medical image analysis, including virtual staining techniques and privacy-preserving explanation methods for medical AI systems. His recent publications (2023-2025) reveal a strong emphasis on explainable AI in medical contexts, with multiple papers addressing how to make deep learning models more transparent and trustworthy for healthcare applications. He has also made significant contributions to face recognition technology, video anomaly detection, and specialized medical imaging techniques for breast cancer and neonatal EEG analysis. Among his scientific contributions are numerous collaborations with researchers across Portugal and internationally. His work has appeared in top-tier journals including IEEE Access, Medical Image Analysis, and Neurocomputing, reflecting the high impact of his research. Cardoso has supervised numerous students who have become established researchers in their own right, including Ricardo P. M. Cruz, Kelwin Fernandes, and Ana Filipa Sequeira. His research group appears to focus on the intersection of deep learning, medical imaging, and biometrics, with strong connections to clinical applications.

• Human-Computer Interaction
• Machine Learning
• Affective Computing
• +۴ مورد دیگر

Ilhan Aslan is a researcher affiliated with the University of Augsburg and DFKI GmbH , focusing on Human-Computer Interaction , Machine Learning , and Affective Computing . His work explores emotional speech interfaces , tangible interaction , and playful multimodal systems . Research Themes : Speech emotion recognition, proxemic-aware interaction, AI ethics, and user experience design. Recent Contributions : 2025 studies on proactive AI agents , voice emotion conversion , and solo role-playing AI ; 2024 work on audio enhancement and emotional trajectory modeling . His publications with Elisabeth André , Timothy Merritt , and Niels van Berkel demonstrate cross-institutional collaboration. Current projects emphasize sustainable creativity support , haptic feedback systems , and bias detection in speech AI .

• Cybersecurity
• Blockchain Technology
• Edge Computing
• +۳ مورد دیگر

Yu Chen is a researcher affiliated with Binghamton University (College of Engineering and Applied Sciences, Department of Computer Science) and University of Southern California , with additional ties to institutions like Chinese Academy of Sciences and Tsinghua University. Their work spans cybersecurity, blockchain technology, edge computing, and IoT systems , focusing on decentralized architectures for public safety, privacy-preserving surveillance, and defense against deepfake attacks. Primary Affiliation: Binghamton University, NY, USA Previous Affiliation: University of Southern California, CA, USA Yu Chen’s research explores blockchain-enabled security frameworks for IoT networks, UAV systems, and smart cities. Recent projects include the Microverse metaverse model and ELOCESS smart grid management. They specialize in real-time data authentication using Electrical Network Frequency (ENF) signals and edge-based privacy solutions for video surveillance and personal drones. Their 15 most recent publications address digital twin security , decentralized data marketplaces, and lightweight protocols for IoT devices. Key areas include UAV network resilience , smart city surveillance , and metaverse classroom models . Yu Chen collaborates with researchers such as Erik Blasch , Ronghua Xu , and Genshe Chen . No student names or specific awards are listed in the provided data.

• Computational Display
• Virtual Reality
• Augmented Reality
• +۸ مورد دیگر

Bin Chen is a Lecturer at the School of Computing and Information Systems, University of Melbourne, where he conducts research at the intersection of computer graphics, computational imaging, and human perception. He was previously a postdoctoral researcher at the Max-Planck-Institut für Informatik and a visiting scholar at the University of Cambridge. Research Interests: His work spans Computational Display , focusing on glass-free 3D and VR/AR systems; Perception , studying how humans perceive virtual materials and gloss; and Computational Imaging , developing AI-driven methods for HDR, deblurring, and depth synthesis. He uses both optical hardware and software rendering to enhance visual fidelity. Recent Research Trends: His recent publications emphasize deep learning for image restoration (e.g., deblurring, tone mapping), neural representations for image stacks, and perceptual validation of material rendering. The integration of light field displays and self-supervised learning is a key theme across his recent work. Scientific Awards: CVPR Best Paper Award Finalist (Top 0.4%) – 2022 Service and Mentoring: Bin Chen has served on the Technical Paper Committees for SIGGRAPH, SIGGRAPH Asia, CVPR, and AAAI. He actively mentors PhD students and invites self-motivated candidates to join his research group. He has advised students such as Tao Huang, Lingyan Ruan, Chao Wang, and Jizhou Li. Laboratory and Teams: While no formal lab name is specified, his research group at the University of Melbourne focuses on visual computing, with strong collaborations extending from City University of Hong Kong to Max-Planck-Institut and the University of Cambridge.

• Data Mining
• Machine Learning
• Text Summarization
• +۶ مورد دیگر

Mark Last is a Professor at Ben-Gurion University in Beersheba, Israel, with a distinguished career spanning over three decades in computer science research. His work primarily focuses on data mining, machine learning, and natural language processing applications. His research interests encompass stream data mining, text summarization, fuzzy logic systems, and classification algorithms. Last has made significant contributions to developing techniques for analyzing dynamic data streams, multilingual text processing, and applying machine learning to real-world problems in healthcare, social media analysis, and security informatics. His work often bridges theoretical advancements with practical applications, particularly in handling non-stationary data and developing interpretable models. Recent research trends show a continued focus on stream data analysis, with applications expanding into social media monitoring, healthcare prediction systems, and multilingual content analysis. His work demonstrates consistent innovation in adapting machine learning techniques to evolving data environments and practical challenges. Mark Last has maintained a prolific publication record with over 175 publications documented in DBLP, collaborating extensively with researchers including Abraham Kandel, Marina Litvak, and Oded Maimon. His work has been published in top venues including IEEE Access, Machine Learning journal, and Expert Systems with Applications.

Jens Hartmannمشاهده پروفایل

استاد

• Geochemistry
• Biogeochemistry
• Hydrochemistry
• +۶ مورد دیگر

Prof. Dr. Jens Hartmann is Professor of Chemistry of Natural Aqueous Solutions at the University of Hamburg's Faculty of Mathematics, Computer Science and Natural Sciences, Department of Earth Sciences. He additionally serves as Adjunct Professor at the University of Waterloo, Canada since 2013. Academic Background: Habilitation (2008/2009), Darmstadt University of Technology: 'Analysis of CO 2 -consumption and dissolved silica fluxes by chemical weathering' Ph.D. (1998-2001), Karlsruhe University: 'Statistical analysis of earthquake related hydrogeological and geochemical signals' Diploma research on Patagonian Rivers biogeochemistry and Hawai'i geological mapping Professor Hartmann's research focuses on the intersection of aqueous geochemistry and Earth system science, with particular expertise in carbon cycle dynamics and chemical weathering processes. His work bridges field measurements, laboratory analyses, and computational modeling to understand how dissolved matter fluxes operate across regional and global scales. He examines the critical connections between geological processes and climate systems through the lens of biogeochemical cycling. Scientific Recognition: JSPS Postdoctoral Fellowship at Kyoto University, Japan Professor Hartmann maintains an extensive teaching portfolio across multiple degree programs. He instructs undergraduate courses including 'Introduction Rocks and Minerals' and 'Geochemistry,' while graduate-level offerings encompass 'Chemistry of Natural Waters,' 'Hydrochemical Modelling,' and 'Global Biogeochemical Cycles and the Climate System.' His educational contributions extend to developing teaching videos on the Carbon Cycle and Chemical Equations of the Carbon Cycle. As a participating researcher in GRK 2530, Professor Hartmann contributes to estuary research while maintaining connections with the Cluster of Excellence 'Integrated Climate System Analysis and Prediction' (CLISAP). His office is located in Room 1424 at the University of Hamburg's Earth Sciences department.