جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Prof. Fabio Galasso is a Full Professor in the Department of Computer Science at Sapienza University of Rome, where he heads the Perception and Intelligence Lab (PINLab). His research focuses on fundamental innovation in computer vision and machine learning, with particular emphasis on distributed intelligent systems, perception frameworks, and general intelligence within sustainable and interpretable AI contexts. His research interests span multiple domains of computer vision including video segmentation , motion forecasting , distributed intelligent systems , and shape reconstruction . Galasso's work emphasizes sustainable AI approaches that operate within constrained computational resources while maintaining interpretability and verifiability. His research bridges theoretical foundations with practical applications across retail, smart cities, and industrial settings. His recent publications demonstrate a clear progression toward increasingly complex human motion understanding and forecasting, with a strong emphasis on real-world applications. The research trajectory shows movement from foundational video segmentation techniques toward sophisticated motion prediction systems and anomaly detection frameworks that integrate multiple modalities. Key themes include temporal consistency, computational efficiency, and practical deployability in resource-constrained environments. His scientific achievements have been recognized with prestigious awards: 2019 IoT/WT Innovation World Cup 2019 Digital Champions Award 2018 Deutscher Digital Award Galasso has coordinated significant research initiatives including a Marie Sklodowska-Curie Actions project (Horizon 2020) and served as Principal Co-Investigator in multiple German-funded projects from the Ministry of Education and Ministry of Economics. His industry experience includes founding and directing OSRAM's Computer Vision Department in Munich, where he led R&D efforts connecting AI research with smart lighting applications, resulting in successful innovation transfers like the award-winning VISN product. He leads the Perception and Intelligence Lab (PINLab) at Sapienza University of Rome, fostering research that spans fundamental computer vision techniques to practical implementations in retail, smart cities, and industrial applications. The lab maintains strong connections with both academic institutions (including previous collaborations with University of Cambridge and Max Planck Institute) and industry partners.

Sorin Mitranمشاهده پروفایل

استاد

• Computational simulation of multiscale, multiphysics systems
• Data-driven constitutive relations for hyperelastic materials
• Nonlinear mode reduction
• +۴ مورد دیگر

Sorin Mitran is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His research focuses on computational simulation of multiscale and multiphysics systems, data-driven constitutive relations for hyperelastic materials, and information geometry for reduced stochastic models. PhD in Aerospace Engineering from Politehnica University Bucharest (1995) Professional background includes fellowships at University of Tokyo (1993), Karlsruhe Institute of Technology (1998-1999), and University of Washington (1999-2002) His research develops numerical tools to predict macro-scale behavior from micro-scale interactions, such as plastic deformation of metals from lattice defect dynamics, microtubule mechanics from molecular dynamics, and protein folding from atomic-level simulations. Mathematical approaches include adaptive computation, machine learning for constitutive law prediction, and information geometry for stochastic process analysis. Recent publications (2023-2018) span computational biology, multiscale fluid dynamics, and medical applications of continuum mechanics. Articles frequently explore data-driven modeling, wave propagation in biological systems, and GPU-accelerated numerical methods like Lattice Boltzmann and Lattice Fokker-Planck formulations.

• Signal and Image Processing
• Machine Learning
• Computer Science
• +۶ مورد دیگر

Yannick Berthoumieu is a Professor at the Université de Bordeaux , affiliated with the IMS Bordeaux laboratory. He leads the MOTIVE team under the Signal and Image Processing research group. His work spans Signal and Image Processing , Machine Learning , and Remote Sensing , with a focus on SAR image analysis, generative models, and geometric learning.

Anjali Adukiaمشاهده پروفایل

استادیار

• Educational inequality
• Economics of education
• Child development
• +۵ مورد دیگر

Anjali Adukia is an assistant professor at the University of Chicago Harris School of Public Policy and the founder and director of the MiiE Lab (Messages, Identity, and Inclusion in Education). She is a faculty research fellow at the National Bureau of Economic Research (NBER) in the Economics of Education and Children and Families groups, a research fellow at the IZA Institute of Labor Economics, a non-resident fellow at the Center for Global Development, and an affiliate of the Bureau for Research and Economic Analysis of Development (BREAD). Dr. Adukia earned her doctoral degree from Harvard University Graduate School of Education with a focus on the economics of education, along with master's degrees in international education policy and higher education from Harvard. She completed her undergraduate studies in molecular and integrative physiology at the University of Illinois at Urbana-Champaign. Her interdisciplinary background bridges the natural sciences with social science research methods. Her research explores how to reduce inequalities so children from historically disadvantaged backgrounds have equal opportunities to develop their potential. She examines factors influencing educational decisions and the role of institutions in improving child outcomes, particularly at the intersection of education and health. Adukia's work draws on large-scale data, often using artificial intelligence methods to expand social science tools and data sources. Her research spans educational infrastructure, representation in children's literature, restorative justice practices, and urban-rural educational disparities. Her publications reveal consistent themes in educational equity, with particular focus on how school infrastructure (like sanitation facilities), representation in educational materials, and disciplinary practices impact student outcomes. She employs innovative computational methods to analyze large datasets, including applying AI tools to measure representation in children's books and examining textbook content across different educational systems. NSF CAREER Award in economics (2024-2029) SREE Early Career Award (2023) William T. Grant Foundation Scholar Award (2018-2023) NAEd/Spencer Postdoctoral Fellowship (2018) APPAM PhD Dissertation Award (2014) AEFP Jean Flanigan Outstanding Dissertation Award (2015) 2021 Google Customer Award for Education Dr. Adukia serves on the editorial boards of Education Finance and Policy, Journal of Research on Educational Effectiveness, and Journal of Social Computing (IEEE). She organizes the annual AI in Social Sciences conference at the University of Chicago and continues to work with international non-governmental organizations including UNICEF and Manav Sadhna in Gujarat, India. Her research has been featured in major media outlets including The Economist, Wall Street Journal, Washington Post, and NPR.

Anna Maria Paola Totiمشاهده پروفایل

دانشیار

• Epistemology of social sciences
• History of sociological thought
• Critical social theory
• +۵ مورد دیگر

Anna Maria Paola Toti is an Associate Professor in the Department of Social and Economic Sciences at Sapienza University of Rome, where she teaches "History of Sociology" and "General Sociology" within the Faculty of Political Science, Sociology, and Communication. Her academic work is deeply rooted in theoretical and historical sociology with a strong emphasis on visual methodologies and spatial analysis. Her primary research interests span across several interconnected domains of sociological inquiry: Epistemology of social sciences and visual sociology History of sociological thought from the 18th century to contemporary theories Critical social theory and responsibility sociology Urban sociology, with particular focus on space, marginality, and stigmatization processes Social inclusion/exclusion dynamics in smart cities and post-disaster contexts Gender representations and visual narratives in social research Professor Toti's recent scholarly output demonstrates a clear trajectory connecting historical sociological frameworks with contemporary social challenges. Her work bridges classical theoretical perspectives with pressing issues like digital transformation, climate change impacts, pandemic responses, and urban inequality. A distinctive feature of her research is the integration of visual methodologies with traditional sociological approaches, particularly in examining how spaces are experienced, represented, and transformed by different social groups. She serves as Delegate of the Faculty of Political Science, Sociology, and Communication for Disability Services, Equal Opportunities and Inclusion, and the Third Mission. Additionally, she is Secretary of the "Sociological Theories and Social Transformations" section of the Italian Sociological Association (AIS) for the 2023-2025 term. Professor Toti actively supervises thesis work, requiring students to request thesis supervision at least six months before their intended graduation session. Her office is located on the first floor, room 107, in the Department of Social and Economic Sciences building (Piazzale Aldo Moro 5 - Building CU002).

• computer vision
• computer graphics
• visual perception

Giuseppe Claudio Guarnera serves as a Senior Researcher at the Norwegian University of Science and Technology (NTNU) in the Department of Computer Science, leading the Research Council of Norway-funded "Spectraskin" project on spectral skin modeling. He concurrently holds a Lecturer position in Computer Vision and Graphics at the University of York (UK) and has been a Guest Researcher at Justus-Liebig University Giessen's Department of Psychology since 2017. His academic background includes: Ph.D. in Computer Science from the University of Catania, Italy, with a dissertation in computer vision and pattern recognition Foundational research experience at the USC Institute for Creative Technologies (US) Guarnera's research centers on computer vision, computer graphics, and visual perception applications, with specialized expertise in spectral skin reflectance modeling, BRDF representation, and material acquisition for virtual environments. His work integrates perceptual validation to enhance realism in digital face rendering and fabric modeling, bridging theoretical computer graphics with practical VR applications. Analysis of his 2017-2020 publications reveals a consistent focus on material and skin reflectance measurement techniques, perceptual validation frameworks, and cross-renderer parameter remapping. Key trends include the development of spectral skin modeling for realistic digital faces (notably in the Spectraskin project), practical BRDF acquisition methods, and single-image fabric reconstruction—demonstrating strong interdisciplinary connections between computer vision, graphics, and human perception studies. Guarnera secures competitive research funding as Principal Investigator of the "Spectraskin" young research talents project awarded by the Research Council of Norway, which investigates spectral skin modeling and rendering for realistic digital faces. He directs the Spectraskin project team at NTNU, which specializes in advancing spectral skin modeling techniques through interdisciplinary collaboration between computer vision, graphics, and perceptual psychology researchers.

Sotiria Fotopoulouمشاهده پروفایل

مدرس

• Astrophysics
• Active Galactic Nuclei
• Galaxy Clusters
• +۴ مورد دیگر

Sotiria Fotopoulou is a Lecturer in the School of Physics at the University of Bristol. Her research focuses on astrophysics, particularly in active galactic nuclei (AGN), quasars, galaxy clusters, and cosmic web structure. She contributes to the Euclid mission's scientific objectives through studies of photometric redshifts, galaxy evolution, and environmental effects. Current Project: Principal Investigator for 'Co-evolution or co-existence? Growth of supermassive black holes in next generation surveys' (2023-2024) Research Themes: Quasar census, slitless infrared spectroscopy for high-redshift objects, cosmic web reconstruction, intracluster light analysis, and gravitational lensing detection. Her work integrates Euclid data with machine learning techniques for galaxy morphology and photometric studies. Award: Recipient of Undergraduate Bursary Grant (2020)

• Nuclear Engineering
• Radiation Monitoring
• Environmental Remediation
• +۳ مورد دیگر

Dr. Yannick S R Verbelen is a Senior Research Associate at the University of Bristol, affiliated with the Interface Analysis Centre and the Cabot Institute for the Environment . With a PhD in nuclear engineering and materials science, his work bridges robotics, radiation monitoring, and environmental remediation. Education: MSc, PhD His research focuses on nuclear industrial archaeology , radiation detection using drones, and robotic systems for nuclear environments. Key projects include airborne gamma-ray mapping and LiDAR integration for uranium mine analysis. Recent publications highlight innovations in radiation mapping algorithms , betavoltaic recycling , and robotic inspection systems . His work has been recognized with a Superior Paper Rating in 2022. He collaborates across disciplines, contributing to datasets on Fukushima Daiichi analysis and Chernobyl exclusion zone mapping. His technical expertise spans UAV deployment , scintillator materials , and energy-harvesting systems .

Seunghwa Jeongمشاهده پروفایل

استاد

• Video Processing
• Virtual Reality
• Machine Learning
• +۷ مورد دیگر

Seunghwa Jeong is a Professor in the Department of Contents Software at Sejong University. He holds a Ph.D. (2024) and M.S. (2015) from the Korea Advanced Institute of Science and Technology (KAIST), and a B.S. (2006) from Pusan National University. His professional experience includes serving as CEO at KAI Inc. (2022-2023) and Technical Lead at the same company (2015-2021). His research focuses on two interconnected domains: Immersive Video Processing including VR systems, multi-view environments, and 4D free-viewpoint technologies, and ML-based Video Processing covering video compression, super-resolution, and object detection/segmentation techniques. Recent publications demonstrate a strong focus on real-time video enhancement using deep learning, interactive 360° viewing systems, and consistent object segmentation across multiple viewpoints. His work appears in premier journals including IEEE Transactions on Pattern Analysis and Machine Intelligence and ACM Transactions on Graphics.

• Medical Imaging
• Diffuse Optical Tomography
• Near-infrared Spectroscopy
• +۵ مورد دیگر

Hamid Dehghani is a Professor of Medical Imaging at the University of Birmingham, affiliated with the Computer Science department, Healthcare Technologies Institute, and The Centre for Human Brain Health (CHBH). With an extensive research portfolio spanning electromagnetic wave propagation within biological tissue and image reconstruction from physiological measurements, he has established himself as a leading figure in biomedical optics. His primary research interests include: Diffuse Optical Tomography Near-infrared Spectroscopy Image Reconstruction Engineering Electromagnetic Wave Propagation in Biological Tissue Multi-Modality imaging techniques Near Infrared Imaging for breast cancer detection Diffuse Optical Imaging of the human brain Professor Dehghani has produced 358 research outputs throughout his career, with consistent publication activity since 1998. His recent work demonstrates a strong focus on functional near-infrared spectroscopy applications for studying brain hemodynamics in response to auditory stimuli, development of real-time imaging techniques, and standardization of terminology in the field. His research bridges engineering principles with clinical applications across neuroscience and oncology. Current research projects include: BioSMART: BIOreactor Spatial Mapping and Actuation in Real Time (EPSRC, 2023-2026) Live monitoring of foreign-body response in animals by diffuse Raman spectroscopy (NC3Rs, 2021-2025) Next generation deep tissue quantitative optical imaging (NIH, 2021-2025) Open, Extensible, Standardized, and Customizable Computational Tools for Optical Brain Mapping (NIH, 2024-2025) Mild Traumatic Brain Injury Biomarker Study (mTBI-Predict) (US Army, 2023-2028) His research fingerprint shows significant expertise in Diffuse Optical Tomography (100%), Near-infrared techniques (68%), Image Reconstruction (62%), and Near-infrared Spectroscopy (56%). Professor Dehghani is actively accepting PhD students and continues to make substantial contributions to medical imaging technology development.

• Urban Planning
• Architectural Planning
• Urban History
• +۳ مورد دیگر

Yoichi Sato is a Professor at Waseda University's School of Social Sciences, where he has been a full-time faculty member since 2010, directing research on urban space, photographic documentation, and historical urban transformation. His academic journey began with engineering studies at Waseda University, culminating in a Master's and Doctorate in Engineering, establishing his interdisciplinary foundation. Professor Sato's research interests span urban space documentation through photography, visual archives of urban history, and the analysis of post-war urban reconstruction, particularly focusing on Tokyo during the Occupation period. His work uniquely bridges architectural planning with visual studies, exploring how photographic evidence reveals historical urban transformations that official records often miss. His recent publications demonstrate a clear trend toward repatriating and analyzing American-held photographic archives of occupied Japan, with significant focus on Ginza district in 1945 Tokyo. These works combine historical urban studies with archival science, creating new methodologies for understanding urban space through photographic evidence. Best Presentation Award, 5th Digital Archive Society Research Conference (2020.10) for research on Oliver L. Austin bird collection photographs Best Presentation Award, 4th Digital Archive Society Research Conference (2020.04) for research on repatriating US-held post-war Japanese photographs Professor Sato leads significant research grants including the Japan Society for the Promotion of Science's 'Examining Diverse Perceptions and Memories of the Cold War from the Perspective of Local Communities' (2021-2025) and 'Vernacular Art Theory' project (2019-2022), demonstrating his leadership in interdisciplinary research. He directs Waseda's Institute of Participatory Design (2024-2025), where his team develops innovative approaches to community-based urban research through visual documentation and participatory methods.

Louise Ritchieمشاهده پروفایل

مدرس

• Theatre Practice
• Digital Media in Performance
• Performance Pedagogy
• +۴ مورد دیگر

Louise Ritchie is a Lecturer in Theatre & Theatre Practice at Aberystwyth University 's Department of Theatre, Film, and Television Studies. Her practice-based research interrogates face-to-face pedagogy in workshops, focusing on digital tools' role in transmitting performance-making pedagogies through video, photography, and pre-recorded instructions. BA (1st class hons), MA (with distinction), PGCE, PhD (Cymru) Organized Brith Gof archive dissemination events at National Library of Wales Awarded AHRC doctoral scholarship for practice-based research Ritchie's research explores the creative possibilities of digital notation systems for physical theatre, culminating in her 2012 thesis Digital notation: new approaches to physical theatre and its documents . Her work bridges digital media and performance pedagogy , examining spatial dynamics in site-specific installations . Selected publications (2004-2013) reveal research trends in: Digital archiving of performance practices Interactive workshop methodologies Time-based documentation techniques Media integration in physical theatre Spatial transformation in performance Cultural memory transmission Scientific achievements include: AHRC doctoral scholarship recipient Ritchie actively collaborates with experimental theatre company Almost Human as an associate artist, and has participated in international exchanges like Human Dialog (Norway) . Her research outputs span peer-reviewed articles, performances, and digital installations .

• Medical Imaging
• Magnetic Resonance Imaging
• Positron Emission Tomography
• +۶ مورد دیگر

Paul Han is an Assistant Professor in the Department of Radiology and Biomedical Imaging at Yale School of Medicine, specializing in advanced medical imaging techniques. His research focuses on developing innovative computational methods for medical imaging, particularly in the areas of MRI and PET imaging, with applications in cardiac imaging, brain tumor imaging, and metabolic imaging. Dr. Han received his PhD in Bio and Brain Engineering from KAIST in 2016. His educational background provided him with a strong foundation in both biological systems and engineering principles, which he applies to solve complex problems in medical imaging. Dr. Han's research interests center around developing advanced computational methods for medical imaging. His work primarily focuses on Linear Tangent Space Alignment (LTSA) models for image reconstruction and processing. He has made significant contributions to cardiac imaging , developing techniques for free-breathing 3D cardiac extracellular volume mapping and T1 mapping. In neuroimaging , he works on multimodal approaches combining PET and MRSI for brain tumor characterization. His research also includes metabolic imaging , particularly deuterium metabolic imaging denoising techniques, and motion correction methods for PET/MR imaging. Dr. Han's approach often integrates machine learning and deep learning techniques to solve challenging problems in quantitative imaging. Analysis of Dr. Han's recent publications reveals a strong focus on developing computational methods for quantitative medical imaging. His work consistently applies manifold learning techniques, particularly Linear Tangent Space Alignment (LTSA), to solve challenging problems in cardiac imaging, neuroimaging, and metabolic imaging. A notable trend is the increasing integration of deep learning approaches with traditional image processing methods. His research shows a progression from method development to clinical applications, particularly in cardiac imaging and brain tumor characterization. Many of his recent papers involve multimodal imaging approaches, combining the strengths of different imaging modalities to provide more comprehensive diagnostic information. Dr. Han collaborates extensively with researchers across multiple disciplines. His most frequent collaborators include Chao Ma, Georges El Fakhri, Thibault Marin, Jinsong Ouyang, and Yanis Djebra. These collaborations span various aspects of medical imaging, from basic method development to clinical applications. His research is conducted within the Division of Bioimaging Sciences at Yale School of Medicine, which provides state-of-the-art imaging facilities and a collaborative environment for interdisciplinary research.

• Computer Vision
• Robotics
• 3D Reconstruction
• +۴ مورد دیگر

El Mustapha Mouaddib is a Professor in the Perception and Robotics department at Universite de Picardie Jules Verne, affiliated with Laboratory Heudiasyc (UMR CNRS 7253). His research bridges advanced robotics with cultural heritage preservation, focusing on developing novel computer vision techniques for complex documentation challenges. His primary research interests include omnidirectional vision systems , hyperspectral imaging , and 3D reconstruction methodologies , with significant emphasis on applications for cultural heritage documentation. Mouaddib's work particularly addresses challenges in temporal illumination compensation , laser scanning registration , and multi-scale digitization of historical structures, as evidenced by his extensive Notre-Dame de Paris cathedral research. Analysis of his 15 most recent publications reveals a consistent trajectory toward heritage robotics - developing specialized computer vision algorithms for cultural preservation. His work demonstrates increasing sophistication in integrating multi-modal sensor data (TLS, hyperspectral, RGB-D) solving illumination challenges in historical documentation developing adaptive robotic systems for complex environments Notably, his Notre-Dame research forms a cohesive body of work examining structural changes through advanced 3D analysis. Mouaddib actively participates in major interdisciplinary projects including SAMURAI , ASSIDUITAS , SCANBOT , ADAPT , and SUMUM , which focus on heritage digitization and robotic exploration. His collaborative approach is evident through extensive co-authorship with institutions like CNRS and international partners in Japan and Italy. His laboratory work centers on the E-Cathedrale initiative, creating comprehensive digital twins of Gothic cathedrals through multi-temporal and multi-scale documentation. This involves developing specialized hardware (like the HDROmni camera system) alongside novel algorithms for processing challenging heritage environments.

• MRI development
• relaxation mapping
• susceptibility mapping
• +۵ مورد دیگر

Dr. Alan Wilman is a Professor in the Department of Radiology & Diagnostic Imaging within the Faculty of Medicine & Dentistry at the University of Alberta. He also holds a cross-appointment as a Professor in the Department of Biomedical Engineering in the Faculty of Engineering. His academic career focuses on the physics and engineering of Magnetic Resonance Imaging with applications in neurological disorders. Dr. Wilman's research interests center on MRI development, particularly in relaxation and susceptibility mapping techniques with clinical applications to multiple sclerosis and other neurological diseases. His lab operates a dedicated research MRI facility at the University of Alberta Hospital, housing a 3T Siemens Prisma and a 4.7T system. His work bridges physics, engineering, and clinical applications to improve diagnostic capabilities. Analysis of Dr. Wilman's recent publications shows a strong focus on quantitative MRI techniques, particularly susceptibility mapping and advanced image processing methods. His research increasingly incorporates machine learning approaches for disease detection and analysis, with significant work on neurological conditions including multiple sclerosis and amyotrophic lateral sclerosis. The trend shows movement toward more clinical applications of advanced MRI techniques. Dr. Wilman has served as Associate Chair (Grad Studies) in the Biomedical Engineering department for many years, demonstrating his commitment to graduate education. His teaching includes BME 513 Imaging Methods in Medicine and MRI Physics for Radiology residents, with most instruction focused on direct interaction with graduate students. His lab typically recruits students with backgrounds in biomedical engineering, electrical engineering, or physics. Dr. Wilman's research MRI facility includes specialized equipment for advanced imaging research, with a 3T Siemens Prisma dedicated to research and a 4.7T system. This infrastructure supports his work on developing new acquisition and processing methods for MRI applications in human health, particularly for neurological conditions.