جست‌وجوی استاد و پژوهشگر

Andrei Atanovمشاهده پروفایل

پژوهشگر

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

Andrei Atanov is a Researcher at the Laboratoire d'intelligence et d'apprentissage visuels (VILAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL) . His research focuses on advanced topics in artificial intelligence, computer vision, and machine learning, with particular emphasis on multimodal learning, vision-language models, and robust generalization. He is affiliated with the Department of Computer Science and contributes to projects exploring innovative solutions for vision tasks using computationally designed sensors and diffusion models. His work often bridges theoretical advancements with practical applications in robotics and generative systems. Key research areas include developing large vision-language models, optimizing vision algorithms for low-sensor environments, and enhancing model robustness through diversification strategies. His publications span topics from 3D data augmentation to uncertainty estimation in deep learning, reflecting a broad yet technically deep expertise in AI fundamentals and applications.

• Geospatial Analysis
• Remote Sensing
• Deep Learning
• +۳ مورد دیگر

Aldino Rizaldy is a Researcher at the Helmholtz Institute Freiberg for Resource Technology, part of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). His work focuses on geospatial technologies, deep learning applications in Earth observation, and environmental science. He is affiliated with HZDR's resource technology research programs and contributes to advancing sustainable development goals through innovative data integration methods. His research interests include hyperspectral imaging, LiDAR data processing, and multimodal fusion techniques for environmental monitoring, resource exploration, and urban analysis. Notable projects involve developing 3D geospatial models, change detection datasets for mining impact analysis, and benchmarking frameworks for geoscience applications. Rizaldy’s publications from 2023–2025 highlight advancements in semantic segmentation, 3D hyperspectral imaging, and AI-driven environmental applications. These studies emphasize precision agriculture, pollution monitoring, and optimizing data fusion strategies for geoscientific challenges. No scientific awards or grants are explicitly mentioned in the provided texts. He collaborates within HZDR’s interdisciplinary teams, focusing on resource technology and sustainability. His contact information includes a.rizaldy@hzdr.de and +49 351 260 4752.

• Computer Vision
• Robotics
• Mixed Reality
• +۳ مورد دیگر

Zuria Bauer is a Lecturer in the Department of Computer Science at ETH Zürich. Her research focuses on advancing computer vision, robotics, and mixed reality technologies with applications in human-robot interaction, scene understanding, and assistive technologies. Key areas include monocular depth estimation, 3D scene reconstruction, and neural rendering techniques. Her work integrates theoretical advancements with practical implementations, such as enhancing robotic perception systems for real-world environments and developing intuitive mixed reality interfaces. Bauer has contributed to datasets like UASOL and frameworks like MaRINeR, which address challenges in novel view synthesis and object manipulation. Research trends in her articles emphasize cross-disciplinary approaches, combining deep learning with robotics to solve problems in healthcare accessibility (e.g., COMBAHO system) and environmental perception for autonomous systems. She explores both hardware-software co-design (e.g., low-cost wearable sensors) and algorithmic innovations (e.g., NeRF-based augmentation). While no formal awards are listed, her publications reflect sustained contributions to advancing perception technologies across dynamic scenes, robotic interaction, and assistive applications.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Dr. Sergey Prokudin is a Lecturer at the Department of Computer Science, ETH Zürich, affiliated with the Professur für Computer Vision. His research focuses on computer vision, machine learning, medical imaging, and robotics. Key areas include 3D reconstruction, neural fields, and uncertainty quantification in biomedical applications. His work bridges theoretical advancements in neural networks with practical applications in healthcare and robotics. Research interests span dynamic scene understanding, spatiotemporal modeling, and medical imaging processing. Notable projects include digital twin creation for spinal surgery, robust MRI parameter estimation, and avatar generation from single images. He has published extensively on topics like Gaussian splatting, residual neural fields, and point cloud alignment. No scientific awards have been explicitly listed. His advising and grants focus on interdisciplinary projects combining computer vision with medical engineering. He is part of ETH's Computer Vision group and contributes to labs exploring 3D modeling and medical visualization technologies.

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

Bo Wang represents multiple academic researchers across various institutions worldwide, with prominent affiliations at Tsinghua University, Michigan State University, University of York, and Hamad Bin Khalifa University. The collective profile shows active research contributions across computer science, engineering, and applied mathematics disciplines. These researchers hold faculty positions primarily as Professors or Associate Professors in departments including Computer Science, Electrical Engineering, and Applied Mathematics. Research interests span artificial intelligence, machine learning, biomedical engineering, wireless communications, and computational mathematics. The collective publication record demonstrates significant contributions to federated learning, medical image analysis, control systems, and biomolecular modeling. Recent work shows particular strength in developing novel AI architectures for scientific computing and healthcare applications. Publication trends reveal increasing interdisciplinary collaboration, with substantial output in top-tier journals and conferences from 2020-2025. Key research areas include deep learning for medical imaging, optimization algorithms for engineering systems, and computational methods for biological data analysis. The collective work shows strong methodological innovation combined with practical applications. No specific scientific awards were identified in the disambiguation page, though the high publication volume in reputable venues suggests recognition within respective fields. The collective research demonstrates strong connections between theoretical advances and practical implementations across multiple domains. While specific student information isn't provided in the disambiguation page, the extensive publication record suggests active supervision of graduate students. Research appears well-funded through various institutional and potentially national funding mechanisms, supporting the diverse range of projects spanning theoretical and applied domains.

Wesley De Neveمشاهده پروفایل

دانشیار

• Biotech
• Deep learning
• Machine learning
• +۴ مورد دیگر

Wesley De Neve is an Associate Professor working at both the Internet Technology and Data Science Lab (IDLab) at Ghent University in Belgium and the Center for Biotech Data Science at the Ghent University Global Campus (GUGC) in Korea. His academic career spans computer science and biotech data science, with significant contributions to machine learning and multimedia analysis. Dr. De Neve received his M.Sc. in Computer Science (2002) and Ph.D. in Computer Science Engineering (2007) from Ghent University, Belgium. His research focuses on developing novel compression and machine learning approaches for analyzing multimedia content (text and video) and biotech data (omics data and medical images). His work bridges computer science with biological applications, particularly in genomic data analysis and medical imaging. He has made significant contributions to splice site prediction, translation initiation site prediction, and hashtag recommendation systems. An analysis of his recent publications (2023-2025) shows a strong focus on applying deep learning to medical imaging and genomic data analysis. His work increasingly incorporates vision transformers, diffusion models, and multimodal approaches to solve problems in healthcare AI, particularly in surgical video analysis, cancer screening, and protein structure prediction. There's a clear trend toward developing more interpretable models for medical applications. Dr. De Neve is actively involved with the Internet Technology and Data Science Lab (IDLab), where he leads research at the intersection of computer science and biotechnology. His team works on applying advanced machine learning techniques to solve real-world problems in healthcare and multimedia analysis.

Ahmad Adnan Qidanمشاهده پروفایل

پژوهشگر

• Optical Wireless Communication
• Machine Learning in Communications
• Wireless Networks
• +۳ مورد دیگر

Dr. Ahmad Adnan Qidan is a Research Associate in the Department of Engineering at King’s College London, UK. He previously served as a Research Fellow at the Institute of Communication and Power Networks within the School of Electronic and Electrical Engineering at the University of Leeds (2020-2023). His career focuses on advancing optical wireless communication systems through AI-driven optimization. PhD in Multimedia and Communications from Universidad Carlos III de Madrid (2020) Current affiliation: King’s College London Past affiliation: University of Leeds Dr. Qidan's research bridges optical wireless communication with machine learning , targeting efficiency in high-speed networks. Key areas include interference management , power allocation , and dynamic resource optimization . His publications highlight trends in AI/ML for OWC , NOMA systems , and IRS technologies , with recent work addressing satellite-terrestrial network integration and energy-efficient architectures. Outstanding Thesis Award (2020-2021) - Universidad Carlos III de Madrid IEEE PhD Thesis Award (2020) - IEEE Spanish Signal Processing and Communications Dr. Qidan contributes as a reviewer to leading IEEE journals and has published extensively on AI-driven solutions for optical wireless systems, despite no explicit mention of student advising or grant funding.

• Meteorology
• Atmospheric Chemistry
• Climate Science
• +۳ مورد دیگر

Wiebke Frey is an Assistant Professor in the Department of Meteorology, focusing on atmospheric science with expertise in aerosol-cloud interactions, climate dynamics, and experimental methodologies. Her research integrates meteorological phenomena such as convection, stratosphere-troposphere exchange, and volcanic aerosol behavior. Research Interests: Investigating aerosol properties and their role in cloud formation and climate processes. Studying atmospheric turbulence and its impact on precipitation mechanisms. Analyzing volcanic plumes and their environmental implications. Key Research Contributions: Her work spans experimental hygrometry in wind tunnels, ice-nucleating particle efficiency, and the dynamics of deep convection in tropical regions. Recent studies address the interplay between aerosols and cloud microphysics, contributing to climate modeling advancements. Advising & Projects: Co-promotor for "From drop to rain: turbulence matters" (2024–present), exploring droplet growth and turbulence effects in precipitation. Co-promotor for "Towards a better understanding of upper level downdrafts in deep convection above the Amazonian rain forest" (2023–present), examining convective life cycles and global climate impacts. Labs/Teams: Collaborates with interdisciplinary groups on atmospheric chemistry and meteorological instrumentation, leveraging experimental setups like the LACIS-T facility.

Zhikang Baoمشاهده پروفایل

استادیار

• Construction and Demolition Waste Management
• Circular Economy
• Digitalization in Construction
• +۳ مورد دیگر

Zhikang Bao is an Assistant Professor at Heriot-Watt University's School of Energy, Geoscience, Infrastructure and Society, affiliated with the Institute for Sustainable Building Design. He holds a BEng (First Class Honours) in Civil Engineering from the University of Liverpool and a PhD in Construction Project Management from the University of Hong Kong. Previously, he served as a Postdoctoral Fellow at City University of Hong Kong. His research focuses on circular economy strategies for construction and demolition (C&D) waste management, digitalization in sustainable construction, and low-carbon solutions. He has published over 60 peer-reviewed articles in top journals like Journal of Industrial Ecology and Science of the Total Environment , with over 2,600 citations and an H-index of 25. Notable awards include the 2024 World’s Top 2% High-Cited Researchers recognition and 9 ESI Highly Cited Papers. Editorial roles: Associate Editor for Alexandria Engineering Journal and Environment, Development and Sustainability . Grants: Funded by Horizon Europe, NSFC, and SAGES. Expertise: Sustainable Development Goals (SDGs), particularly environmental protection and sustainable cities. His work emphasizes policy-practice interfaces, digital tools for circular practices, and global sustainability challenges. He is actively supervising PhD candidates in construction sustainability and circular economy.

Panagiotis Kasnesisمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Semantic Web Technologies
• +۳ مورد دیگر

Panagiotis Kasnesis is a Senior Research Fellow at the Department of Electrical & Electronics Engineering, University of West Attica. He holds a Ph.D. in Computer Science from NTUA, alongside degrees in Chemical Engineering and Techno-Economic Systems. His research focuses on machine/deep learning, Semantic Web technologies, multiagent systems, and IoT, with over 20 publications in top journals and conferences. He is an NVIDIA DLI-certified instructor in Natural Language Processing and Computer Vision. Education: Ph.D. (Computer Science, NTUA), Diploma in Chemical Engineering (NTUA), M.Sc. in Techno-Economic Systems (NTUA) Research interests span health informatics, wearable devices, and sensor fusion, with projects in biomedical signal analysis and IoT applications. His work includes developing radar-based systems for health monitoring and AR/VR solutions for rehabilitation. He actively participates in EU-funded projects and promotes trustworthiness in social media through initiatives like EUNOMIA. Recent publications emphasize health tech innovations, such as stress detection via PPG signals, UWB radar-based human detection, and multimodal fusion for 3D pose estimation. His contributions also address cultural heritage preservation through VR and crowdsourcing. Grants and collaborations involve European R&D projects in IoT security, emergency response systems, and decentralized social media frameworks. He leads efforts in embodied AR for medical applications and energy-efficient neural networks.

Zhonghang Xiaمشاهده پروفایل

استاد

• Bioinformatics
• Data Mining
• Machine Learning
• +۲ مورد دیگر

Zhonghang Xia is a Professor in the Department of Computer Science at Western Kentucky University. His primary affiliations include the WKU COHH building (office 4141). His research focuses on Bioinformatics, Data Mining, Machine Learning, Multimedia Computing, and Combinatorial Optimization. He teaches advanced courses such as Machine Learning, Data Mining, Artificial Intelligence, and Thesis Research/Writing. His work emphasizes algorithm development for bioinformatics applications, kernel methods, and multimedia systems optimization. Research interests span peptide identification using machine learning, collaborative filtering prediction, and automation frameworks. His recent work addresses challenges in proteomics data analysis and distributed media systems. Advising focuses on distributed service systems and cloud computing infrastructure. He has contributed to over 20 peer-reviewed publications since 2005, with notable contributions in BMC Bioinformatics , IEEE Transactions , and Journal of Proteome Research . Teaching responsibilities include graduate and undergraduate courses in computer science fundamentals and specialized topics such as research methods and thesis guidance. His lab activities involve interdisciplinary projects combining machine learning with biological data analysis and network optimization.

• Computer Vision
• Medical Informatics
• 3D Scene Understanding
• +۵ مورد دیگر

Overview Pedro Hermosilla Casajus is an Assistant Professor at the Department of Computer Vision within the Faculty of Informatics at Technische Universität Wien (TU Wien). His research focuses on advanced computer vision techniques with applications in medical imaging, 3D scene understanding, and deep learning methodologies. He is actively involved in teaching multiple courses including Fundamentals of Computer Vision, Deep Learning for Visual Computing, and Scientific Research and Writing. Projects & Collaborations MyeFLOW (2020–2025) : Developing automated analysis systems for flow cytometry data. EVOCATION (2018–2022) : Real-time shape acquisition technologies. Modeling the World at Scale (2020–2026) : Large-scale 3D modeling techniques. Research Highlights His work bridges computer vision with medical diagnostics, particularly in measurable residual disease detection using flow cytometry. He explores geometric deep learning for 3D point cloud analysis and scene graph generation, advancing unsupervised semantic segmentation methods. Recent contributions include stylized Gaussian splatting for neural rendering and open-vocabulary 3D scene understanding frameworks. Teaching He teaches core courses like Fundamentals of Computer Vision and supervises student projects in medical informatics, visual computing, and human-centered computing.

• Computer Architecture
• Compilers
• GPUs
• +۴ مورد دیگر

Antonio González Colás is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona (FIB) and the Department of Computer Architecture. He leads the ARCO research group (Microarchitecture and Compilers) and focuses on advanced computer architecture, including GPUs, compilers, and embedded systems. In 2024, he received the ICREA Acadèmia distinction for his research on future computer systems integrating cognitive tasks and autonomous decision-making. His work emphasizes energy-efficient architectures, memory optimization, and hardware acceleration for AI and real-time applications. He holds a Doctorate in Computer Science and has over 1000+ publications, including top-tier journals and conferences. His research spans topics like GPU microarchitecture, compiler-assisted caching, DNN acceleration, and autonomous driving hardware. Major awards include the HiPEAC 2024 Paper Award and EU-funded projects under Horizon 2020. He advises numerous PhD students on topics such as point cloud processing for autonomous systems, GPU rendering optimizations, and neural network efficiency. His contributions bridge theoretical computer architecture with practical hardware-software co-design, addressing challenges in modern computing systems.

Xiao Guمشاهده پروفایل

پژوهشگر

• AI for Healthcare
• Biomedical Signal Processing
• Pervasive Healthcare
• +۳ مورد دیگر

Xiao Gu is a Researcher at the Computational Health Informatics Lab, Department of Engineering Science, University of Oxford. He holds a PhD in Computing from Imperial College London (2023), an MRes in Medical Robotics and Image Guided Intervention (2019), and a BEng in Electronic Engineering from Fudan University (2018). His research focuses on integrating pervasive sensing and deep learning for healthcare applications, addressing challenges in generalization and data scarcity. He has been recognized with awards including the BMVC Outstanding Reviewer (2024) and IEEE ICRA Student Travel Grant (2023). Key research interests include AI-driven healthcare solutions, biomedical signal processing, and the development of robust models for real-world medical applications. His work spans datasets like CFP for biomedical sensing and contributions to workshops on wearable intelligence. Xiao Gu has authored over 30 peer-reviewed publications, with recent work focusing on medical imaging, domain adaptation, and vision-language models. He actively contributes to academic activities, including editorial roles and conference reviewing.

Christopher Nowotarskiمشاهده پروفایل

دانشیار

• Severe Convective Storms
• Tornado Dynamics
• Boundary Layer Meteorology
• +۹ مورد دیگر

Dr. Christopher Nowotarski is an Associate Professor in the Department of Atmospheric Sciences at Texas A&M University . He joined the university in 2014 following his PhD, MS, and BS studies in Meteorology at Pennsylvania State University. His research focuses on improving the prediction of severe convective storms and their hazards, particularly tornadoes associated with supercells and tropical cyclones. Key tools include idealized cloud-resolving simulations and analysis of field experiment data (e.g., TRACER, DCOTSS, VORTEX-SE). Dr. Nowotarski’s educational background includes: Ph.D. Meteorology, Pennsylvania State University M.S. Meteorology, Pennsylvania State University B.S. Meteorology, Pennsylvania State University His research group investigates supercell dynamics , tropical cyclone tornadoes , boundary layer interactions , and machine learning applications . Current projects include DOE-funded TRACER studies on aerosol impacts in Houston and collaborative NSF work on overshooting tops in DCOTSS. Past work explored Southeast US tornado environments, permafrost effects on Arctic meteorology, and data assimilation techniques using the ADRAD radar. Students advised include David Topping, Seth Thompson, and others listed in detail. Key collaborative partnerships span the College of Geosciences , College of Engineering , and College of Architecture at TAMU, as well as external institutions like NCAR, Penn State, and OU. He has organized sounding programs in data-sparse regions and developed VR visualization tools for weather data. Teaching responsibilities include courses such as Radar Meteorology , Severe Weather and Mesoscale Forecasting , and Convective Storms Field Studies . His work emphasizes bridging research and operational forecasting, with contributions to improving model analyses of tropical cyclones and evening transition dynamics.