جست‌وجوی استاد و پژوهشگر

Renaud Gabetمشاهده پروفایل

استادیار

• Optical Telecommunications
• Distributed Fiber Optic Sensors
• Photonics Components
• +۲ مورد دیگر

Renaud Gabet is an Assistant Professor (HDR) at Télécom Paris (formerly École Nationale Supérieure des Télécommunications) in the Department of Communications and Electronics (Comelec). He is part of the Optical Telecommunications research team within the Information Processing and Communication Laboratory (LTCI). His academic background includes an engineering degree from ENSSAT, a Master's in Electronics and Optronics from the University of Western Brittany, a Ph.D. from the University of Rennes, and an HDR from Pierre and Marie Curie University. His research focuses on optical telecommunications, distributed fiber optic sensors (Brillouin, Rayleigh, Raman), structural characterization of photonic components, and novel optical metrology techniques (OLCR, OFDR). He teaches courses such as Optics and Photonics (COM 101), Optical Communications (TELECOM 203), Optical Systems (COM 340), and Optical Devices and Nanotechnologies (COM 341). Gabet's publications emphasize advanced fiber sensing, photonic crystal waveguides, laser dynamics, and optical characterization methods. His work frequently addresses industrial applications like pipeline vibration monitoring and telecommunications.

• Image and Video Quality Assessment
• Visual Attention Modeling
• 3D Television (3DTV) and Stereoscopic Imaging
• +۵ مورد دیگر

Patrick Le Callet is a full professor at Polytech Nantes (University of Nantes), leading the Image & Video Communication (IVC) group at the CNRS IRCCyN lab. His academic journey includes roles as an assistant professor (1997–1999) and lecturer (1999–2003) at the University of Nantes. He earned credentials in electronics from École Normale Supérieure de Cachan. His research focuses on human vision modeling applied to image/video processing, including 3D quality assessment, visual attention modeling, watermarking, and medical imaging. He coordinates major projects (e.g., EU Marie Curie ITN PROVISION, UHD4U) totaling over $5M in grants. He co-chairs VQEG’s HDR and 3DTV initiatives and serves on editorial boards for IEEE Transactions and EURASIP journals. Key contributions include databases like IRCCyN/IVC-Toyama and Eyetracker SD 2009, advancing standards in 3DTV and QoE. Over 20 students have been advised, with notable alumni working on topics like medical imaging and 3DTV discomfort metrics. Projects involve collaborations with Orange Labs, Thomson, and cultural heritage institutions. Labs/teams: IVC group at IRCCyN, managing a 3D visualization platform and eyetracking facilities. Research emphasizes interdisciplinary applications in consumer electronics, healthcare, and cultural preservation.

• Computer Vision
• Steganography/Steganalysis
• Remote Sensing
• +۴ مورد دیگر

Marc Chaumont is an Associate Professor (HDR Hors-Classe) at the Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) in Montpellier, France, and teaches at the University of Nîmes. He also collaborates with the IRISA laboratory in Vannes since 2024. His primary research focuses on visual data analysis, remote sensing, and steganography. Chaumont holds a Ph.D. in Computer Science from INSA Rennes and has authored nearly 100 international articles. He is a Senior Member of IEEE and an associate editor of the IEEE Transactions on Information Forensics and Security (TIFS). His work spans steganalysis, underwater imaging, coral reef ecology, and medical imaging applications. Chaumont leads collaborations with institutions like MARBEC, CIRAD, and the Astrophysics and Cosmology Laboratory (CPPM). Key achievements include developing the Yedroudj-Net CNN for steganalysis and pioneering deep learning methods for LiDAR urban object classification. He has been recognized with the Top-3% Best Reviewer award at IEEE ICIP 2020. His research bridges computer science with environmental and medical domains, emphasizing reproducibility in deep learning experiments and societal applications like wastewater network mapping (Cart'EAUX).

Vincent Moureauمشاهده پروفایل

پژوهشگر

• Combustion Modeling
• Fluid Dynamics
• Computational Fluid Dynamics (CFD)
• +۳ مورد دیگر

Vincent Moureau is a CNRS Research Fellow (HDR) at the CORIA laboratory, specializing in advanced computational fluid dynamics and combustion modeling. His research focuses on Large-Eddy Simulation (LES) of turbulent flows, spray dynamics, and thermo-acoustic instabilities in complex geometries. He is a core developer of the YALES2 solver, a high-order unstructured code for multiphase reactive flows. Positions: Research Fellow at CORIA, HDR, and affiliated with INSA de Rouen for teaching. Key Expertise: LES in gas turbines, piston engines, and wind turbines; numerical methods for HPC systems. He has taught courses on numerical methods, aerodynamics, and CFD software training. His work earned awards including the 2018 Grand Prix ONERA and the Digital Simulation Collaboration Award. His research includes industrial collaborations with SAFRAN and INRIA. Labs/Teams: Leads the YALES2 development team and contributes to the SIAME project for exascale computing. Active in the SIAME and MATI projects for aero-thermal systems and combustion modeling.

• Video Coding
• Energy Efficiency
• HEVC
• +۴ مورد دیگر

Christian Herglotz is a researcher affiliated with the University of Erlangen-Nuremberg , Germany. His work focuses on energy efficiency in video coding and decoding systems, with a particular emphasis on HEVC and VVC standards. He has published extensively in IEEE journals and conferences like ICIP, ICASSP, and QoMEX, often collaborating with André Kaup and Matthias Kränzler. Key research themes: energy-aware video compression, decoding power optimization, rate-energy-distortion modeling. Co-edited special sections on deep learning-based video coding. Recent Publications (2022-2025): Explored power reduction in HDR video encoding, motion prediction for 360-degree video, and heterogeneous quantization for DNN accelerators. His studies integrate machine learning with traditional codec design to improve energy efficiency. Technical Contributions: Developed models for decoding energy estimation, analyzed carbon impact of streaming devices, and proposed methods for viewport-adaptive motion compensation. Collaborative work spans thermal imaging for power analysis and reliability-aware DNN hardware optimization.

Monica Danileviczمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Computer Vision
• Remote Sensing
• +۶ مورد دیگر

Monica Danilevicz is a Research Fellow at the Australian Herbicide Resistance Initiative (AHRI) within the UWA School of Agriculture and Environment at the University of Western Australia. Her research focuses on integrating emerging technologies like machine learning, computer vision, and remote sensing to support sustainable agriculture, including smart weed management and climate-resilient crop development. She also leverages bioinformatics to incorporate molecular markers and biochemical measurements into her analyses. Education: PhD in Biological Sciences (2023): 'Application of deep learning to leverage high-throughput phenotyping for crop breeding,' University of Western Australia. Master's in Plant Biotechnology and Bioprocesses (2019): 'Uncovering non-coding RNA in copaiba and maize,' Universidade Federal do Rio de Janeiro. Bachelor's in Biotechnology (2016): 'MicroRNA expression analysis from maize inoculated with endophytic bacteria under hydric stress,' Universidade Federal do Rio de Janeiro. Research Interests: Sustainable agricultural technologies, including precision farming and AI-driven solutions. Machine learning applications in crop phenotyping, disease detection, and genomics. Climate-resilient crop development through genomic and phenotypic integration. Herbicide resistance management and weed ecology. Articles Trends: Her work emphasizes machine learning in crop improvement, genomics-driven solutions, and predictive modeling for yield stability. Recent studies explore AI applications in disease detection, genomic feature selection, and genotype-environment interactions. Awards: Forrest Research Foundation Scholarship (PhD). Advising & Grants: Accepting PhD/HDR students in agricultural biotechnology and AI applications. Collaborates on global projects like the Maize Yield Prediction Competition. Labs/Teams: Active in the Australian Herbicide Resistance Initiative (AHRI) and leads projects on crop phenotyping and genomic analysis.

• Quantum Optics
• Quantum Information
• Diamond
• +۳ مورد دیگر

Professor Andrew Greentree is a theoretical physicist at RMIT University, specializing in quantum physics, diamond-based technologies, and neurocognition in insects. He holds the position of Professor in the School of Science and is a Chief Investigator in the ARC Centre of Excellence for Nanoscale BioPhotonics. His roles include teaching, research coordination for Higher Degree Research (HDR) in Physics, and involvement in RMIT's ERA submission and Research Integrity Advisory Network. Research interests span quantum optics, quantum information, diamond magnetometry, and the cognitive abilities of bees. Notable achievements include pioneering the Jaynes-Cummings-Hubbard Model and spatial adiabatic passage, leading to novel quantum devices. Collaborations include work with Prof. Brant Gibson on diamond-glass hybrid materials and Prof. Adrian Dyer on bee numerical cognition. Greentree has developed advanced magnetometry techniques and contributed to understanding bees' ability to perform arithmetic, published in high-impact journals like Science . Awards: RMIT Award for Research Excellence (2019), ARC Future Fellow (2017), Fellow of the Institute of Physics (2012). Advising: Supervises PhD projects in quantum AI, diamond magnetometry applications, and bee cognition. Lab/Teams: ARC Centre of Excellence for Nanoscale BioPhotonics, Theoretical Chemical and Quantum Physics Group.

Axel Huttمشاهده پروفایل

پژوهشگر

• Neural Systems
• Nonlinear Stochastic Processes
• Data Assimilation
• +۴ مورد دیگر

Dr. Axel Hutt is a senior researcher (Directeur de Recherche) at INRIA Grand Est in Strasbourg, France, leading the MIMESIS team. His work focuses on neural systems modeling, nonlinear dynamics, and data assimilation in complex systems. He holds a PhD from the University of Stuttgart (2001) and an HDR (Habilitation) from the University of Nice (2013). Hutt’s research bridges neuroscience, mathematics, and engineering, with contributions to anesthesia modeling, neurostimulation, and EEG analysis. He has held roles at the Max Planck Institute, Humboldt University Berlin, and the University of Ottawa, and received the Schloessmann Fellowship (2000) and ERC Starting Grant (2011). His current projects include PhD supervision of T. Nette and participation in high-profile conferences like Re:publica and ICMNS. Recent work explores myelination effects, closed-loop neurostimulation, and AI ethics. Education: Physics Diploma (Stuttgart, 1997), PhD (Stuttgart, 2001), HDR (Nice, 2013) Key Roles: INRIA Team Leader (MIMESIS, 2019–present), NeuroSys Team Head (2013–2015) Research Themes: Neural field theory, stochastic processes, clinical neurostimulation applications Publications: Over 180 peer-reviewed articles across journals like Communications Physics and PLoS Computational Biology , focusing on noise-driven dynamics, EEG modeling, and computational psychiatry. Awards: ERC MATHANA Grant (2011), Schloessmann Fellowship (2000)

Kalin Stefanovمشاهده پروفایل

پژوهشگر ارشد

• Affective Computing
• Artificial Intelligence
• Accessible Computing
• +۵ مورد دیگر

Kalin Stefanov is an ARC DECRA Fellow and Research Fellow in the Department of Human Centred Computing at Monash University. He holds a PhD in Computer Science from KTH Royal Institute of Technology and an MSc in Artificial Intelligence from the University of Amsterdam. His research focuses on Affective Computing, exploring systems that recognize and simulate human affects, with applications in social robotics, neurodiverse communication, and multimodal interaction. He has led projects on sign language translation and large-scale deepfake detection datasets. Key collaborations include work at the University of Southern California’s Institute for Creative Technologies and National Institute of Informatics. He has received accolades such as the Discovery Early Career Researcher Award (2023) and Best Paper Awards (2019, 2024). His research also contributes to UN SDG 4 (Quality Education) through accessible technologies for neurodiverse groups and visually impaired learners. Projects include the 'Active Generation of fingerspelling in Australian Sign Language' and 'Research Towards automated Australian Sign Language translation,' funded by the Australian Research Council. Stefanov’s work spans AI ethics, multimodal data platforms (e.g., OpenSense), and systems for social signal processing in human-robot interaction.

• Computational Psychiatry
• Social Neuroscience
• Systems Biology
• +۵ مورد دیگر

Guillaume Dumas is a Professor at the Faculté de médecine of Université de Montréal, specializing in computational psychiatry and social neuroscience. He leads the Laboratoire de Psychiatrie de Précision et de Physiologie Sociale at the CHU Sainte-Justine Research Center. His work bridges neuroscience, AI, and clinical practice, focusing on social cognition, brain synchronization, and neurodevelopmental disorders. Education includes a PhD in Cognitive Neuroscience from Sorbonne Université and an HDR in Clinical Neurosciences from Université de Paris. He has held roles at Institut Pasteur and Florida Atlantic University. Research interests span computational psychiatry, interbrain connectivity, and AI-driven diagnostic tools. Notable projects include the SCALE initiative studying autism across levels and InterBrain Synchronization mechanisms. His lab develops serious games for clinical assessment and biomarker discovery. Recipient of FRQS J1/J2 awards, IVADO affiliation, and grants from CIHR and FRQNT. Supervised 7 Master’s students recently, focusing on EEG, genomic variants, and AI models.

Shuchin Aeronمشاهده پروفایل

دانشیار

• Data Science
• Statistical Signal Processing
• Inverse Problems
• +۸ مورد دیگر

Shuchin Aeron is an Associate Professor in the Department of Electrical and Computer Engineering at Tufts School of Engineering, with joint appointments in the Departments of Computer Science and Mathematics. He holds a Ph.D. from Boston University (2009) and completed postdoctoral research at Schlumberger Doll Research, focusing on borehole acoustic signal processing. His research spans statistical signal processing, machine learning, compressed sensing, and information theory, with applications in geophysics, bioengineering, and imaging. Aeron has authored over 175 publications and holds patents in acoustic signal processing. He received the NSF CAREER Award (2016) and is a Senior Member of the IEEE. Educations: Ph.D., Electrical Engineering, Boston University, 2009 M.S., Electrical Engineering, Boston University, 2004 B.Tech., Indian Institute of Technology, 2002 Research Interests: Statistical signal processing (SSP), inverse problems, compressed sensing, information theory, convex optimization Machine learning applications in geophysical signal processing, imaging, and bioengineering His work emphasizes optimal sampling and recovery of multidimensional signals, with contributions to compressed sensing architectures and generative models for particle physics experiments. He leads NSF-funded projects on data science and domain generalization, and collaborates with industry partners like Schlumberger and Mitsubishi Electric Research Labs. Awards: NSF CAREER Award (2016) Mitsubishi Electric Research Lab Research Gift (2015) Grants and Funding: NSF HDR TRIPODS (2019–2023) AFOSR: Enabling Trusted Human-Like Artificial Teammates (2018–2023) NSF: Optimal Sampling and Recovery for Multilinear Signals (2013–2016) Aeron teaches advanced courses in probabilistic systems analysis, information theory, and machine learning. He directs the Tufts Data Science undergraduate and graduate programs, and serves on editorial boards of journals including Frontiers in Signal Processing and IEEE Transactions on Geoscience and Remote Sensing .

Oh Jin Kwonمشاهده پروفایل

استاد

• Image Processing
• Image Coding
• Digital Watermarking
• +۷ مورد دیگر

Professor Oh Jin Kwon is a faculty member in the Department of Electronic Engineering at Sejong University since 1999. His research focuses on image/video analysis, compression algorithms, image quality enhancement, fusion techniques, watermarking, and steganography. He has developed patented algorithms for region adaptive image coding, image watermarking, backward compatible HDR image coding, and progressive image coding. Education: Ph.D. in Electrical and Computer Engineering from University of Maryland, College Park (1994) M.S. in Electrical Engineering from University of Southern California, Los Angeles (1991) B.S. in Electronic Engineering from Hanyang University, Seoul (1984) His extensive research includes image processing , watermarking , and cyber resilience . Recent publications explore deep learning applications in cybersecurity , image coding standards , and 360-degree imaging . His work has been published in Electronics (Switzerland) , Applied Sciences (Switzerland) , Drones , and other journals. Scientific Awards & Grants NSF Grant 91-00655 DACA 76-92-C-0079 DACA 76-89-C-0019 DACA 76-92-C-0009 Professor Kwon has advised numerous PhD and MS students in areas like Point Cloud Coding , JPEG Privacy & Security , and AI-based image coding . He leads research projects funded by institutions including the Korea Aerospace Research Institute and Electronics and Telecommunications Research Institute . His laboratory, the Video Communication Research Lab , focuses on JPEG systems standards , image security , and smart store technologies . Current research includes operator-friendly interface technology for unmanned vehicles and composite material cultural heritage data encryption.

Felix Heideمشاهده پروفایل

استاد

• Computational Imaging
• Computer Vision
• Optics
• +۶ مورد دیگر

Felix Heide is a Professor of Computer Science at Princeton University , where he leads the Princeton Computational Imaging Lab . He also serves as Head of AI at Torc Robotics , focusing on full autonomy stacks for self-driving trucks. His research sits at the intersection of optics , machine learning , and computer vision , addressing imaging challenges in harsh environments like dense fog, ultra-low/high illumination, and scattering media. Ph.D. in Computer Science from the University of British Columbia Postdoctoral research at Stanford University His work on computational imaging spans physics-based vision, non-line-of-sight imaging , end-to-end camera design , and robust sensor fusion . He has pioneered techniques for inverse neural rendering , nanophotonic optics , and light-speed AI through optical computing. His recent papers in Nature Machine Intelligence , Science Advances , and top conferences ( SIGGRAPH , CVPR , ICCV ) focus on: Adverse weather imaging (fog, snow, rain) Multi-sensor fusion (LiDAR, radar, gated cameras) Light transport through scattering media Optical metasurfaces and diffractive optics End-to-end optimization of imaging pipelines Event-based vision and polarization cues He has received prestigious awards including the SIGGRAPH Significant New Researcher Award , Sloan Research Fellowship , and Packard Fellowship . His lab's open-source code and datasets enable real-world applications in autonomous driving, microscopy, and augmented reality.

• Image quality enhancement
• Image classification
• Human Visual System mechanisms
• +۷ مورد دیگر

Marie LUONG is a researcher at Université Sorbonne Paris Nord specializing in image processing and analysis. She is currently preparing her HDR (Habilitation à Diriger des Recherches), a prestigious post-doctoral qualification in the French academic system that demonstrates research independence and eligibility to supervise PhD students. Her research focuses on two major interconnected domains: Image Quality Enhancement : Developing techniques inspired by Human Visual System mechanisms to address coding artifacts, noise, and resolution limitations Image Classification : Creating innovative methods based on sparse representation in transform domains to improve classification accuracy Dr. LUONG's methodological contributions include: Four methods for addressing coding artifacts with a proposed Blockiness Visibility Measure Six innovative denoising approaches combining anisotropic filtering and machine learning Three example-based super-resolution methods, two integrating denoising in a unified optimization framework Four classification methods leveraging sparse representation in wavelet domains Her research demonstrates significant practical applications in digital cinema technology and medical image diagnosis systems , translating theoretical advances into real-world solutions. Academic leadership and mentoring: Co-supervised seven completed PhD theses Currently supervising two ongoing doctoral projects

Jan Fröhlichمشاهده پروفایل

استاد

• High Dynamic Range (HDR)
• Wide Color Gamut (WCG)
• Image Encoding
• +۵ مورد دیگر

Prof. Dr. Jan Fröhlich is a Professor for Motion Picture Engineering at Stuttgart Media University (HdM), specializing in advanced imaging technologies. His research focuses on high dynamic range (HDR) and wide color gamut (WCG) image encoding, color rendering, camera metrology, and HDR workflows. Prior to his academic appointment, he held key industry positions including Senior Image Scientist at ARRI Munich where he contributed to professional cinema camera systems, and Dolby Laboratories where he helped develop the ITU Rec.2100 ICtCp color space and Dolby Vision platform. He also served as Technical Director at CinePostproduction GmbH. His core research areas include: HDR/WCG encoding and color science Virtual production workflows Camera metrology and image sensor technology Perceptual aspects of high frame rate and HDR imaging Real-time HDR/SDR conversion systems Recent publications demonstrate extensive work in virtual production technologies, including LED volume workflows, HDR broadcast graphics, and light field displays. His research consistently addresses both theoretical foundations and practical implementation challenges in cinematography and broadcast engineering.