جستجوی استاد راهنمای دانشگاهی

Andrea Cavallaro is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and Director of the Idiap Research Institute. He holds dual appointments in the School of Engineering (STI) within the Institute of Electrical Engineering and Measurements (IEM) and the School of Engineering's Education Unit (SEL-ENS). His research focuses on machine learning for multimodal perception, privacy-preserving AI, and autonomous systems. Cavallaro earned his PhD in Electrical Engineering from EPFL in 2002 and has held leadership roles including Director of Research at Queen Mary University of London and Turing Fellow at The Alan Turing Institute. Education: PhD in Electrical Engineering (EPFL, 2002) Leadership: Idiap Director, Affiliate at ELLIS Society Editorial Roles: Editor-in-Chief of Signal Processing: Image Communication (2020–2023), Senior Area Editor for IEEE Transactions on Image Processing Research Interests: Machine learning for audio-visual sensing, privacy in AI, autonomous systems perception, and ethical AI frameworks. Key projects include AlignAI (trustworthy AI alignment) and CORSMAL (multimodal object manipulation). Recent articles explore privacy-aware AI models, adversarial attacks, and multimodal perception systems. His work bridges theoretical advancements with practical applications in robotics, healthcare, and education. Awards include the Royal Academy of Engineering Teaching Prize and IAPR Fellowship. Teaching: Leads courses on deep learning ethics and multimodal AI at EPFL. Advising: Supervises 11 PhD students in areas like privacy-preserving algorithms and autonomous systems. Labs/Teams: Coordinates Idiap’s Audiovisual Intelligence and Learning Lab (LIDIAP) and collaborates on projects like GraphNEx (explainable AI via graph neural networks).

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

Sabine Süsstrunk is a Full Professor and Director of the Images and Visual Representation Laboratory (IVRL) at EPFL's School of Computer and Communication Sciences. She holds a BS in Scientific Photography from ETH Zürich, MS from Rochester Institute of Technology, and PhD from University of East Anglia. Her career includes positions at Hewlett-Packard Labs and Corbis Corporation. Her research explores computational imaging, computational photography, color processing, computer vision, and image quality. Key interests include near-infrared applications, multispectral imaging, and computational aesthetics. Her work bridges hardware and software solutions for imaging challenges. Publications demonstrate consistent focus on advancing generative models (diffusion models, neural cellular automata), 3D reconstruction (NeRF variants), and media integrity (DeepFake detection). Recent trends show increased emphasis on 3D vision, robustness in generative AI, and video analysis. Awards & Honors: IS&T/SPIE Electronic Imaging Scientist of the Year (2013) Raymond C. Bowman Teaching Award (2018) EPFL AGEPoly IC Polysphere Award (2020) 8 Best Paper/Demo Awards Fellowships: IEEE, IS&T, ELLIS, AIIA She leads the IVRL lab and advises PhD candidates while serving as President of the Swiss Science Council. Research is supported through competitive grants and industry collaborations.

• Computational Imaging
• Computational Photography
• Color Image Processing
• +۴ مورد دیگر

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

• Signal Processing
• Audio Processing
• Image Processing
• +۵ مورد دیگر

Prof. Helmut Grabner is a Professor at the Zurich University of Applied Sciences (ZHAW), leading the Visual Intelligence and Applications Group and the Entrepreneurship initiatives within the School of Engineering. His work bridges computer science, medical technology, and visual communication, with a focus on Extended Reality (XR), surgical training simulations, and AI-driven decision making. Education: PhD in Computer Science (Graz University of Technology, 2008), Master's in Computer Science (2008), and a Certificate of Advanced Studies in Higher Education (ZHAW, 2021). Prior to academia, he held roles including CTO at Logitech and co-founder of upicto, applying computer vision in industry and startups. Research spans augmented reality medical training tools, NMR spectrum analysis via deep learning, and understanding visual engagement in advertising. Awards include the prestigious Koenderink Prize (2018) for contributions to computer vision. Projects include Immersive Education frameworks, bias-mitigation in venture capital algorithms, and surgical proficiency measurement systems. Teaching includes courses on Visual Computing, Machine Learning, and Deep Learning. His work integrates academic research with practical applications in healthcare, education, and entrepreneurship.

• Computer Vision
• Extended Reality (XR)
• Artificial Intelligence
• +۷ مورد دیگر

Guillaume Chanel is a researcher at the University of Geneva, affiliated with the Department of Computer Science under the Faculty of Sciences, and the Centre interfacultaire en sciences affectives. His work spans affective computing, emotion recognition, and physiological signal analysis in human-computer interaction contexts. Affective Computing Emotion Recognition Brain-Computer Interfaces Multimodal Interaction Physiological Computing Human-Computer Interaction Chanel's research focuses on integrating physiological signals (e.g., EEG, electrodermal activity) with computational models to enhance emotion detection and adaptive systems. Recent work includes attention mechanisms in U-Net for medical imaging, variational autoencoders for PPG denoising, and biofeedback-driven gaming systems. His publications emphasize multimodal fusion, transfer learning, and latent representation techniques. His 15 most recent articles (2025–2020) analyze advanced deep learning architectures for emotion and impression recognition, physiological signal processing, and adaptive game design. Key themes include attention mechanisms, variational autoencoders, multimodal synchronization, and biofeedback applications. Chanel collaborates with the Computer Vision and Multimedia Laboratory and the Centre universitaire d'informatique, contributing to open datasets and tools like the Toolbox for Emotional feAture extraction from Physiological signals (TEAP).

• Affective Computing
• Emotion Recognition
• Brain-Computer Interfaces
• +۳ مورد دیگر

Prof. Dr. Sascha Schneider is an Assistant Professor of Educational Technology at the University of Zurich's Institute of Educational Science, affiliated with the Digital Society Initiative (DSI). His research focuses on optimizing digital learning media design to enhance cognitive, emotional, and motivational processes in education. He holds a PhD in Media and Instructional Psychology and has held postdoctoral positions at TU Chemnitz. His work investigates topics like decorative elements' impact, motivational effects of choice, emotional design, and adaptive learning environments, targeting K-12 students, university learners, and adults. Education: Bachelor's: Educational Science & English, University of Erfurt Master's: Continuing Education & Educational Technology, University of Erfurt PhD: Media and Instructional Psychology, TU Chemnitz (2017) Habilitation: Educational Psychology, TU Chemnitz (2022) His research emphasizes experimental and meta-analytical methods to explore multimedia learning theory extensions, including social processes and instructional design principles. Key projects include the DEEP initiative for differentiated instruction and the LiT+ educational transfer program. Prof. Schneider's awards include the Erik De Corte Award (2021), a Dissertation Prize (2019), and the Commerzbank Prize (2018). He actively contributes to academic communities, organizing conferences and serving on editorial boards. Advising & Grants: Supervises doctoral research in educational technology Leads projects funded by BMBF and Swiss National Science Foundation His team at the Chair of Educational Technology collaborates on labs like NARRATID , focusing on narrative-driven instructional design.

• Educational Technology
• Multimedia Learning
• Cognitive Processes
• +۳ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Jürgen Wassner is a Professor of Edge Computing and Co-Head of the Competence Center for Intelligent Sensors and Networks at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a master's degree from Technical University of Dresden and a PhD from ETH Zurich. His career spans industry roles in Silicon Valley Group Inc. and telecommunication R&D before joining academia in 2007. His research focuses on embedded systems, AI at the edge, FPGA/VHDL design, real-time systems, and powerline communication . He leads projects such as 'Visual-Servoing Testbed with AI-Hardware in the Loop' and 'Low-Cost High-Performance Intelligent Camera for Space Debris Mitigation,' collaborating with companies like Diehl Aerospace. Recent publications emphasize wire fault detection using powerline communication, AI-based odor classification systems, and hardware acceleration for CNNs. His work bridges theoretical research with practical applications in avionics, rail freight, and space debris mitigation. Wassner advises Master of Science in Engineering students and has been recognized for his media engagement including a feature in the Luzerner Zeitung. He teaches Digital Design (FPGA, VHDL) and Digital Signal Processing , emphasizing hands-on implementation of cutting-edge systems.

• Embedded Systems
• AI Algorithms
• FPGA/VHDL Design
• +۵ مورد دیگر

Martin Vetterli is a Full Professor and currently serves as President of the Swiss Federal Institute of Technology Lausanne (EPFL). He holds positions in the School of Computer and Communication Sciences, specifically within the Institute of Electrical Engineering and the Audiovisual Communications Laboratory (LCAV). His academic appointments include teaching roles in SSC (Schooling and Student Community) and SIN (School of Computer and Communication Sciences) departments. His research spans wavelet theory, signal processing, telecommunications, and communication systems. Vetterli's work has significantly impacted image and video compression technologies, sensor networks, and self-organizing communication systems. His theoretical contributions in wavelet analysis have found practical applications across multiple domains of information technology. Latsis National Prize (1996) Fellow of the Association for Computing Machinery Fellow of the Institute of Electrical and Electronics Engineers Member of the National Academy of Engineering Vetterli has mentored over 60 doctoral students across Switzerland and the United States, maintaining active interest in their academic and entrepreneurial careers. He directs the Audiovisual Communications Laboratory (LCAV) at EPFL and was founding director of the National Center of Competence in Research in Mobile Information and Communication Systems. His research has yielded approximately fifty patents, leading to startup companies including Dartfish and Illusonic, as well as technology transfers to companies like Qualcomm.

• wavelet theory
• signal processing
• telecommunications
• +۶ مورد دیگر

Prof. Dr. Matthias Rosenthal is a Professor of Multiprocessor and Real-Time Systems at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW), where he also serves as Head of the Research/Focus Area Realtime Platforms. He holds a PhD and MSc in Electrical Engineering from ETH Zurich (1993–1997). His research focuses on multiprocessor systems, hybrid multicore architectures, distributed signal processing, embedded GPU computing, and real-time embedded systems. Key projects include In-Flight GNSS Interference Detection, dAIrector (automated multi-camera live production), and novel AFM techniques for industrial quality control. He has led over 15 industry-focused projects, including collaborations with Innosuisse and companies like Harman International. His work emphasizes real-time systems, FPGA-GPU co-design, and embedded AI solutions. Education: PhD (ETH Zurich, 1997), MSc (ETH Zurich, 1993) Awards: CTI Startup Label (2005) Teaching: Lectures on digital systems, real-time computing, and information theory Notable contributions include advancements in embedded machine learning for food waste management, secure boot concepts for Zynq MPSoC, and low-latency wireless video systems. His research bridges theoretical computer engineering with practical industrial applications.

• Multiprocessor Systems
• Real-Time Embedded Computing
• Distributed Signal Processing
• +۳ مورد دیگر

Touradj Ebrahimi is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the Multimedia Signal Processing Group (MMSPG) within the School of Engineering's Institute of Electrical Engineering. He holds a Ph.D. in Electrical Engineering from EPFL and serves as the Convenor of the JPEG standardization committee. His research spans AI-powered imaging, multimedia signal processing, and media security. Research Interests: Professor Ebrahimi's work focuses on next-generation image/video compression (including JPEG AI and DNA-based storage), quality of experience in immersive media, trustworthy media authentication (JPEG Trust), and explainable AI for biometric systems. His group pioneers methods for point cloud compression, deepfake detection, and privacy-preserving multimedia. Publication Trends: Recent articles emphasize AI-driven compression standards, quality assessment for emerging media (point clouds, HDR), and explainability in computer vision. Key themes include JPEG AI standardization, DNA storage for visual data, and robustness evaluation of deepfake detectors. Awards & Honors: IEEE Star Innovator Award in Multimedia IEEE and Swiss National ASE Awards ISO Certificates for MPEG-4/JPEG 2000 contributions Best Paper Award (IEEE Trans. Consumer Electronics) Fellow of IEEE and SPIE Leadership: He founded RayShaper SA, Genista SA, and Emitall SA. Represents Switzerland in ISO/IEC JTC1/SC29 and ITU, advising European Commission initiatives and venture capital firms on multimedia technologies. Facilities: Leads the MMSPG lab at EPFL, focusing on intelligent multimedia systems. The group collaborates globally on standardization (JPEG, MPEG) and EU projects.

• Image and Video Compression
• Media Security
• Multimedia Signal Processing
• +۵ مورد دیگر

Pina Marziliano is the Executive Director of the Centre for Biomedical Imaging (CIBM) at École Polytechnique Fédérale de Lausanne (EPFL), a leading interdisciplinary research center involving five major institutions: HUG, UNIGE, EPFL, UNIL, and CHUV. She is affiliated with the School of Engineering and the Department of Electrical Engineering at EPFL, where she holds the academic rank of Associate Professor. Her leadership roles include Director and Deputy within the CIBM-GE and CIBM-AIT units under the VPA-AVP-CP structure at EPFL. Bachelor of Science in Applied Mathematics, Université de Montréal (1991–1994) Master of Science in Computer Science and Operations Research, Université de Montréal (1994–1996) Doctoral School in Communications Systems, EPFL (1996–1997) PhD in Communications Systems, EPFL (1997–2001) Her research focuses on biomedical signal and image processing , with deep expertise in digital signal processing , finite rate of innovation , and medical imaging . She has pioneered perceptual quality metrics for multimedia and developed advanced signal reconstruction techniques. Her work bridges engineering with clinical applications, particularly in ophthalmology and radiology. The available article highlights her foundational work in sampling non-bandlimited signals, a breakthrough in signal processing theory. This research has broad implications in communications, imaging, and data acquisition, influencing both academic and industrial developments, including technology transfer to Qualcomm Inc. IEEE Signal Processing Society 2006 Best Paper Award 2009 Honourable Mention Women in Engineering Affinity Group of the Year Award (IEEE WIE Committee, USA) Pina Marziliano has supervised graduate students, including Amrish Nair, CEO of BIORITHM. She has secured significant research funding, including a US$200K grant from Qualcomm and over SGD$3M in joint medical research funding. She has served as Associate Editor for IEEE journals and as a leader in IEEE Singapore’s Women in Engineering group. Her leadership extended to organizing the 9th International Conference on Sampling Theory and Applications at NTU in 2011. She co-founded two companies: PABensen , a design firm integrating art, science, and technology, and BIORITHM , a biotech spin-off developing the fetal heart monitoring device FEMOM. Her role at CIBM leverages a unique ecosystem of clinicians, academics, and state-of-the-art technology to advance biomedical imaging globally.

• Biomedical Signal Processing
• Biomedical Image Processing
• Digital Signal Processing
• +۷ مورد دیگر

Dr. Philipp Ackermann is a researcher and academic leader at ZHAW Zurich University of Applied Sciences, School of Engineering. His roles include Deputy Head of the Human-Centered Computing Research Group, Head of the Visual Computing Lab, Deputy Programme Director for Medical Informatics, and Delegate for Entrepreneurship in the School of Engineering. His research focuses on Visual Computing (especially Augmented Reality), Medical Informatics , and Digital Health . He leads projects like AR Patterns (design patterns for AR experiences) and Automated Recognition of Modular Product Structures in AR , which explore circular economy applications. His work bridges technical innovation with practical implementation, emphasizing collaboration in immersive technologies and data-driven solutions. Key research trends in his articles include: Advancements in XR technologies (e.g., 3D inference pipelines, physically-based rendering) Collaborative systems in AR/VR environments Applications of AR in healthcare and industrial settings Systematic reviews of collaboration in virtual spaces He has contributed to projects as both leader and team member, addressing topics from surgical proficiency training to intelligent viticulture. His work often emphasizes real-world impact through partnerships between academia and industry. Philipp leads the Visual Computing Lab , fostering interdisciplinary innovation in computer graphics and immersive technologies. His educational contributions include teaching the CAS Advanced Statistical Data Analysis course.

• Visual Computing
• Augmented Reality (AR)
• Medical Informatics
• +۴ مورد دیگر

Javier Montoya is a Senior Lecturer at the Lucerne School of Computer Science and Information Technology, part of the Lucerne School of Applied Sciences and Arts (HSLU). He holds a PhD in Computer Vision and Machine Learning from ETH Zurich (2016), with earlier Master’s degrees from INRIA/Grenoble Institute of Technology (France) and UNICAMP (Brazil). His research focuses on Deep Learning, Machine Learning, and Computer Vision, particularly in healthcare applications such as medical image processing and digital health solutions. Education: PhD: Swiss Federal Institute of Technology (ETH Zurich), 2016 Master’s: INRIA/Grenoble Institute of Technology (France), 2010 Master’s: University of Campinas (UNICAMP, Brazil), 2007 Montoya’s professional expertise spans Machine Learning & Deep Learning, Image Processing & Computer Vision, and Digital Health. His work bridges academic research with applied projects, including roles as Senior Research Scientist at Zurich University of Applied Sciences (ZHAW, 2020–2021) and Research Scientist at ETH Zurich (2017–2019). His recent publications emphasize medical imaging advancements, motion artifact mitigation, and deep learning applications in healthcare. His research trends highlight innovations in medical image segmentation (e.g., polyp detection, cardiac imaging) and cross-disciplinary projects like solar energy systems and road network analysis. While no specific grants or awards are listed, his extensive publication record underscores contributions to both healthcare and engineering domains.

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

Svyatoslav Voloshynovskyy is a Professor at the University of Geneva's Faculty of Science, Department of Computer Science, with an impressive publication record of 195 works and supervision of 26 graduate projects. His extensive research portfolio demonstrates significant contributions to signal processing, information theory, and multimedia security, evolving into cutting-edge applications in medical imaging and privacy-preserving technologies. Dr. Voloshynovskyy's research interests span multiple interconnected domains at the intersection of theoretical foundations and practical applications. His work has progressed from foundational research in data hiding and watermarking to contemporary applications in privacy-preserving medical imaging. Key research areas include: Information-theoretic approaches to multimedia security Advanced signal processing techniques for image and data analysis Federated learning frameworks for healthcare applications Privacy-preserving machine learning methodologies Medical imaging analysis with focus on PET/CT technologies Theoretical foundations of data hiding and watermarking Analysis of his recent publications (2022-2024) reveals a strategic shift toward healthcare applications, particularly in medical imaging with privacy preservation. His work integrates federated learning, differential privacy, and deep learning to address critical challenges in healthcare data analysis while maintaining patient confidentiality. This represents a natural evolution from his earlier theoretical work in signal processing and information theory to impactful real-world healthcare applications requiring multi-institutional collaboration. With 26 supervised works documented in the University of Geneva archive, Dr. Voloshynskyy has demonstrated significant commitment to academic mentoring and research supervision. His publication pattern suggests successful grant acquisition for projects at the intersection of signal processing, machine learning, and healthcare applications, though specific funding details aren't provided in the available information. His research trajectory indicates leadership in a specialized research group focused on signal processing and machine learning applications, with recent emphasis on medical imaging. The consistent mention of multi-institutional collaborations in his latest publications suggests an active research team working on federated learning approaches for medical image analysis, likely involving partnerships with healthcare institutions across multiple countries.

• Signal Processing
• Information Theory
• Multimedia Security
• +۵ مورد دیگر