جست‌وجوی استاد و پژوهشگر

Marianne Greatedمشاهده پروفایل

پژوهشگر

• Landscape Painting
• Environmental Art
• Art Pedagogy
• +۴ مورد دیگر

Marianne Greated is a faculty researcher at The Glasgow School of Art, School of Fine Art, Department of Painting & Printmaking. Her academic practice bridges landscape painting, environmental art, and innovative pedagogical frameworks with a focus on compassionate assessment practices. She holds a PhD evidenced by her 2014 exhibition presenting doctoral research outputs. PhD in Fine Art (completed prior to 2014) Her research centers on landscape as both subject and pedagogical tool, examining industrial environments, Scottish art history, and interdisciplinary connections between visual art and sound studies. Recent work emphasizes compassionate feedback systems to reduce inequity in art education, developed through the QAA Collaborative Enhancement Project. Her scholarship reveals consistent engagement with environmental sustainability, historical analysis of women artists, and sensory perception in artistic practice. Greated's publication trajectory shows increasing focus on educational frameworks since 2020, while maintaining strong connections to landscape painting and Scottish art history. Her collaborative projects with institutions like UAL demonstrate commitment to systemic change in art education assessment practices. Through initiatives like the Practising Landscape seminar series and international symposia, Greated fosters collaborative knowledge exchange across academic and artistic communities. Her work with the QAA project establishes frameworks for compassionate assessment that prioritize student belonging while maintaining academic rigor. Active in both studio practice and academic research, Greated maintains dual engagement with creative production (exhibited internationally across China, India, Belarus, and UK venues) and scholarly contributions to art education discourse. Her current projects indicate continued development of compassionate pedagogy models and deeper exploration of landscape representation.

Silvio Montresorمشاهده پروفایل

استادیار

• Digital Holography
• Speckle Noise Reduction
• Acoustic Emission Monitoring
• +۳ مورد دیگر

Silvio Montresor is a Teacher-researcher at the Institute of Acoustics within Le Mans Université, actively contributing to acoustics and optical metrology research since at least 2018. His work bridges theoretical acoustics with practical engineering applications, particularly in structural health monitoring and digital holography. His research focuses on speckle noise reduction in digital holography , acoustic emission monitoring of structural damage , and ultrasonic characterization of composite materials . Key contributions include developing deep learning algorithms for phase image denoising and novel methods for damage localization in reinforced concrete using acoustic emissions. His work spans civil engineering, materials science, and biomedical applications. Analysis of his publication trends reveals consistent focus on computational imaging techniques (2018-2024), with increasing integration of machine learning since 2021. His research demonstrates strong interdisciplinary connections between acoustics , optics , and structural engineering , particularly in non-destructive testing methodologies. As a core member of the Institute of Acoustics, Montresor contributes to transversal research axes including Metamaterials , Non-linear Acoustics , and NDET (Non-Destructive Evaluation Technologies). His work supports the lab's mission in Materials Acoustics , Elastic Waves in Complex Media , and Physics of Musical Instruments , utilizing advanced facilities for laser ultrasonics and electroacoustic sensor development.

Ana Belén Petro Balaguerمشاهده پروفایل

مدرس ارشد

• Image Processing
• Mathematical Analysis
• Color Image Enhancement
• +۳ مورد دیگر

Dr. Ana Belén Petro Balaguer serves as a Senior Lecturer at the Department of Mathematics and Informatics, University of the Balearic Islands (UIB), where she has been a faculty member since 1999. She earned both her Mathematics degree (1999) and Ph.D. (2006) from UIB and currently teaches Mathematical Analysis II across multiple mathematics and engineering degree programs, as well as Mathematical Thought and Teaching for early childhood education programs. Her research centers on image processing and mathematical analysis with specialization in color image enhancement techniques. As a member of the Mathematics, Imaging and Learning (MIA) Consolidated R+D+I Group, she maintains significant research collaborations with DxO (a leading image processing company) and the French Space Agency for satellite image processing projects involving Pleiades satellite data. Dr. Petro Balaguer's publication record demonstrates expertise spanning retinex theory, satellite image processing, underwater ecological imaging, and mathematical approaches to image enhancement. Her recent work shows continued focus on practical applications of theoretical mathematical concepts in real-world imaging scenarios. Two six-year research periods (prestigious academic recognition in the Spanish system) She actively contributes to mathematics education through her teaching in early childhood and primary education programs, participation in teaching workshops, and service on the Board of Directors of the 'Societat Balear de Matemàtiques' where she plays a significant role in mathematics dissemination throughout the Balearic Islands.

• Image Processing
• Computer Vision
• Machine Learning
• +۳ مورد دیگر

Damir Sersic is a Professor at the Faculty of Electrical Engineering and Computing , University of Zagreb. He is affiliated with the Image Processing Group , focusing on research areas such as astronomical cross-matching, color constancy, crater detection, and medical imaging techniques. His work involves advanced image processing methodologies, including the development of specialized datasets like DRiDB and OCT image databases , and tools such as the Hough Transform. Office: D107 Phone: +385 1 6129 891

• Multi-Dimensional Signal Processing
• System Modelling and Identification
• Image and Video Processing
• +۵ مورد دیگر

Mårten Sjöström is a Professor in Signal Processing at Mid Sweden University, where he serves as the highest representative of the research subject Computer and System Sciences and is part of the managerial group of the Department of Information and Communication Systems (IKS). He leads the Realistic 3D research group and has extensive experience in both academic and industrial settings. His educational background includes a Master of Science from Linköping University (Applied Physics and Electrical Engineering, 1992), a Technical Licentiate degree from the Royal Institute of Technology, Stockholm (Signal Processing, 1998), and a PhD from Ecole Polytechnique Federale de Lausanne (Modelling of Non-linear Systems, 2001). He obtained his Docent degree (Associate Professor) in 2008 and Professor's degree in Signal Processing in 2013. His primary research focuses on Multi-Dimensional Signal Processing with emphasis on System Modelling and Identification. He has successfully applied these techniques to Image and Video Processing, Multi-media Communications, and currently specializes in Multi-Scopic 3D and Light Field Technology including capture, processing, coding, and presentation/visualization. His work spans theoretical foundations to practical implementations across various application domains. His recent publication record demonstrates a clear trajectory toward advanced light field and 3D imaging technologies, with significant contributions to compression algorithms, depth estimation techniques, quality assessment metrics, and telepresence applications. His research bridges theoretical signal processing with practical industrial implementations, particularly in remote operation, mining applications, and immersive visualization systems. Best Paper Award at MMEDIA 2013 Quality Reviewer Award at ICME 2013 Professor Sjöström has supervised an extensive number of doctoral and licentiate students, with numerous current PhD candidates expected to complete their degrees in 2025. His teaching portfolio covers a wide range of subjects including Applied Signal Processing, Automatic Control, Computer Hardware and Architecture, and specialized PhD courses in Video Processing and Realistic 3D. He has led numerous research projects both current and completed, including IMMERSE, PLENOPTIMA, and various initiatives in 3D video technology and visualization. As founder and head of the Realistic 3D research group, he directs activities focused on synthesis and capture of 3D images and video, rendering techniques for virtual perspective views, system modeling for 3D capture and presentation, coding of 3D content, quality metrics and assessments, and remote control and measurement systems. The group maintains strong industrial collaborations across multiple sectors.

Sergio Canazzaمشاهده پروفایل

دانشیار

• Expressive Information Processing
• Auditory Displays
• Musical Cultural Heritage Preservation
• +۷ مورد دیگر

Sergio Canazza is an Associate Professor at the Department of Information Engineering , University of Padova, Italy. He holds key roles in academic leadership as advisory editor for the Journal of New Music Research and as founder of the Sound and Music Processing Lab . His work bridges music technology , audio restoration , and cultural heritage preservation . Degree in Electronic Engineering, University of Padova CEO, AudioInnova (University spin-off) Research Interests : Expressive information processing in music Auditory displays and cross-modal interaction Preservation of musical cultural heritage Interactive multimedia systems for education AI-driven audio restoration Digital philology for time-based media Scientific Contributions span 20+ years of European/National projects and 200+ publications. His recent work focuses on: Generative AI for IoT sound communication Standardization of audio preservation (ARP technology) Reactivation of historical computer music systems Visual anomaly detection in audio tapes Interactive environments for music education 3D reconstruction of ancient instruments Awards : StartCup Veneto 2010 (Sound and Music Lab) StartCup Veneto 2012 (TechnoTale project) Start Cup 2006 (ARCHIMEDES project) Leadership Roles : Project Manager, EU Culture Program Director, University of Padova's Multimedia Center (2013-2016) Owner of audio preservation patents

• Computer Vision
• Machine Learning
• Digital Signal Processing
• +۲ مورد دیگر

Aleksej Avramović serves as an Assistant Professor at the Faculty of Electrical Engineering, University of Banja Luka, Bosnia and Herzegovina, holding the academic rank of docent since his election in December 2021. His institutional affiliation places him within the university's primary engineering faculty where he contributes to both undergraduate and graduate academic programs in electrical engineering disciplines. Dr. Avramović's research program demonstrates exceptional breadth across computer vision and machine learning applications. His work spans intelligent transportation systems through advanced traffic sign recognition algorithms, medical imaging innovations for colonoscopy analysis and inflammation detection, and pioneering biohybrid security systems using honeybee activity monitoring for explosive detection. Complementing this, he maintains significant contributions to digital signal processing fundamentals including logarithmic arithmetic circuits and lossless compression techniques for medical images and audio signals, reflecting both theoretical depth and practical implementation expertise. His publication trajectory reveals a clear evolution from foundational signal processing research toward applied computer vision solutions. Early work focused on logarithmic multipliers and compression algorithms, while recent publications emphasize deep learning implementations for real-time systems in transportation, healthcare diagnostics, and security applications. This progression demonstrates strategic adaptation to emerging technological paradigms while maintaining core competencies in efficient algorithm design. Dr. Avramović actively participates in nationally funded research initiatives, currently serving as principal investigator for the active project 'Signal Processing using Embedded Computer Systems and Machine Learning' (2025-2026). His project portfolio includes significant contributions to the NATO-funded 'Biological Methods for Explosive Detection' (2017-2021), the 'Mobile Health Platform NORMEDY' medical project (2019-2021), and multiple image processing initiatives for remote sensing applications, demonstrating consistent success in securing competitive research funding. He maintains robust research collaborations within the Faculty of Electrical Engineering, particularly with the computer vision and biohybrid systems teams led by Professors Zdenka Babić and Vladimir Risojević. These groups operate specialized laboratories for image processing, embedded systems development, and signal analysis, supporting interdisciplinary research that bridges electrical engineering with medical technology and security applications through practical hardware-software integration.

• Signal Processing
• Image Fusion
• Biohybrid Systems
• +۴ مورد دیگر

Dr. Slavica Gajić is an Associate Professor at the Department of General Electrical Engineering within the Faculty of Electrical Engineering at the University of Banja Luka. She holds the academic rank of docent (equivalent to Associate Professor) since July 7, 2022, having previously served as a senior assistant since September 23, 2021. Her institutional presence spans multiple faculties including Mechanical Engineering, Technology, Mining, Natural Sciences and Mathematics, and the Faculty of Philosophy where she teaches academic courses. Her research interests span several interconnected domains with signal processing as the central pillar. She has made significant contributions to image fusion techniques using Empirical Mode Decomposition, with applications in medical imaging and computer vision. A distinctive aspect of her work involves biohybrid systems, particularly utilizing honeybees as biosensors for landmine and explosives detection. This interdisciplinary research combines electromagnetic field analysis for biomedical applications with advanced signal processing techniques and machine learning approaches. Her work on electromagnet design for homogenizing electromagnetic fields demonstrates her expertise in practical electromagnetic applications. Analysis of her publication record reveals a clear research trajectory evolving from foundational work in image processing and medical image compression toward increasingly sophisticated applications in biohybrid detection systems and machine learning. Her most recent publications show a strong trend toward integrating neural network architectures with traditional signal processing methods, reflecting the broader shift in her field toward AI-enhanced methodologies. The consistent focus on practical applications in security, medical technology, and environmental monitoring demonstrates her commitment to research with real-world impact. Dr. Gajić is actively involved in multiple research projects, serving as principal investigator for national and international projects including 'Development of STEM Skills and Interests in School Children' and 'Signal Processing Methods Based on Machine Learning.' She also participates as a key researcher in numerous other projects related to signal processing on high-performance computing architectures, smart sensor technologies in bioengineering, and environmental parameter monitoring systems. Her research collaborations extend across disciplinary boundaries, with frequent partnerships in projects involving biomedical applications, landmine detection systems using bee behavior monitoring, and educational technology initiatives. She maintains active research connections with colleagues across multiple faculties at the University of Banja Luka and participates in projects funded by both national ministries and international organizations including NATO Science for Peace and Security.

• Robot Vision
• Robot Learning
• Computer Vision
• +۴ مورد دیگر

Joni-Kristian Kämäräinen serves as Professor of Signal Processing within the Computing Sciences department at Tampere University, where he leads research in the Vision Group. Previously, he held faculty positions at LUT University's School of Engineering Science for five years before joining Tampere University in 2012 (tenured 2017, promoted to full professor in 2020). His academic journey includes a postdoctoral fellowship at the University of Surrey's Center of Vision, Speech and Signal Processing under Josef Kittler. His research centers on robot vision and robot learning , with significant contributions to computer vision and machine learning. Key focus areas include visual place recognition, RGB-D tracking, color constancy, and anthropometric measurements. His group maintains strong industry collaborations with Huawei, Nokia Technologies, and Business Finland-funded projects. His publication portfolio shows a clear trajectory toward real-world robotic applications, with recent work emphasizing visual place recognition under varying conditions (2022-2024), depth-aware video processing (2023-2024), and reinforcement learning for industrial manipulators (2023-2025). The 2023 textbook Koneoppimisen perusteet (Machine Learning Fundamentals) demonstrates his commitment to education. Expert Statement for Finnish Parliament (2022) on AI solutions Contributor to Finnish Roadmap: Robots and the Future of Welfare Services (2021) Featured in YLE Uutiset (2018), Aamulehti (2021), and multiple technical press outlets He has supervised 20 PhD students since 2007, including Vivienne Huiling Wang (2025), Samu Koskinen (2025), and Fatemeh Shokollahi Yancheshmeh (2024), with alumni placed at Aalto University, Ericsson AB, and Huawei. His group receives funding from the Academy of Finland, EU Horizon 2020, Business Finland, Huawei, and Nokia Technologies. The Vision Group operates from Tampere University's Hervanta Campus, maintaining close ties with industrial partners through applied research projects.

Andy T.D. Bennettمشاهده پروفایل

استاد

• Animal Behaviour
• Evolution
• Vision
• +۵ مورد دیگر

Andy T.D. Bennett is Professor of Life and Environmental Science at Deakin University, where he has been employed since 2008 and previously served as Head of the School of Life and Environmental Sciences from 2008-2011. He established the Centre for Integrative Ecology at Deakin University and serves as President of the Australasian Society for the Study of Animal Behaviour. Professor Bennett earned his BSc in Zoology from University of Adelaide, followed by a DPhil from Oxford University on the ecology of avian spatial memory. After research fellowships in Oxford and Brussels and a postdoc in Adelaide, he spent over 15 years in Europe, including at the University of Bristol where he became a Lecturer and Reader in Sensory and Behavioural Ecology. His research focuses on integrative ecology using birds as model systems, particularly parrots and waterbirds. He leads a program on satellite tracking waterbirds across Australia. His work spans animal behaviour, evolution, vision, navigation and disease. His research has revealed insights into the breeding behavior of banded stilts that travel thousands of kilometers to nest in temporary desert salt lakes, and the link between color variability in Crimson Rosellas and Beak and Feather Disease Virus. His notable scientific contributions include: Documenting banded stilts' extraordinary breeding behavior in remote Australian salt lakes Discovering the relationship between plumage color variation and disease resistance in Crimson Rosellas Establishing the Centre for Integrative Ecology at Deakin University Professor Bennett has received prestigious awards including the 1851 Science Research Scholarship and a Leverhulme Fellowship which enabled him to establish a research program on the crimson-yellow-Adelaide rosella species complex. He has been Deakin University's representative on the Victorian Centre for Climate Change Adaptation Research and continues to contribute significantly to ornithological research in Australia and internationally.

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

Adriana Birlutiu is a Lecturer in the Computer Science Department at 1 December 1918 University of Alba Iulia , Romania. Her expertise lies in machine learning, computer vision, bioinformatics, and transfer learning, with a recent focus on porcelain-industry optimisation. Education Ph.D., Radboud University Nijmegen, Netherlands (2011) M.Sc., Babeș-Bolyai University of Cluj-Napoca & University of Lorraine (Erasmus), 2005 B.Sc., Babeș-Bolyai University of Cluj-Napoca, 2004 Research Interests Adriana's research spans machine learning , deep learning , computer vision , and bioinformatics . She has contributed to preference learning, domain adaptation, protein–protein interaction prediction, and automated quality control in porcelain manufacturing. Her recent projects integrate deep neural networks with industrial computer-vision systems to detect defects and recognise characters on ceramic surfaces. Publication Trends Across 15 recent publications (2010-2019), Adriana has consistently explored transfer learning , multi-task learning , and Bayesian methods . Articles cluster around two major axes: biomedical applications (protein networks, cancer relapse prediction, respiratory-motion modelling for radiotherapy) and industrial AI (porcelain defect detection, character recognition). The work shows a clear evolution from theoretical machine-learning foundations to practical, domain-specific implementations. Grants & Projects SIVAP (2016-2018): Intelligent ML & computer-vision system for porcelain manufacturing optimisation, UEFISCDI PN-III-P2-2.1-BG-2016-0333. CMRCC (2017-2018): Computational Models for Reproducing Ceramics Colors, UEFISCDI PN-III-P2-2.1-PED-2016-1835. Student Supervision & Mentoring Adriana has supervised more than 25 undergraduate and master’s theses. Her students have won multiple awards at national conferences such as In-Extenso and SCCSS-IEECC , covering topics from automated defect detection to web applications for academic scheduling. Teaching Responsibilities She teaches courses including Machine Learning , Mathematical Software , Fundamental Algorithms , Object-Oriented Databases , and Modelling and Simulation at both undergraduate and master levels.

• Digital Humanities
• Text Analysis
• Computer Vision for Historical Images
• +۴ مورد دیگر

Dr. Melvin Wevers is a researcher at the Faculty of Humanities, University of Amsterdam, specializing in Digital Humanities with a focus on historical research methodologies. His work bridges traditional historical scholarship with computational approaches, particularly in text analysis and computer vision applications for historical datasets. He maintains an active affiliation with the KNAW Humanities Cluster in Amsterdam, demonstrating his standing in the Dutch academic community. Wevers' research interests span multiple interdisciplinary domains including Digital Humanities methodology, historical data science, and computational approaches to historical sources. His work particularly focuses on analyzing historical visual materials, text corpora, and structured historical datasets like those from the Dutch East India Company. He has developed innovative approaches for analyzing historical advertisements, gender representations in media, and temporal patterns in historical discourse. His recent publications demonstrate a strong trend toward multimodal analysis, combining text and image processing techniques to extract deeper insights from historical sources. Wevers has been particularly active in developing frameworks for analyzing historical photographs, parliamentary debates, and large-scale historical datasets using machine learning approaches. His work on the Dutch East India Company workforce represents a significant contribution to economic history methodology. Wevers has also contributed to methodological discussions in Digital Humanities, particularly around text mining ethics and the 'multimodal turn' in the field. His work on the EDDe framework demonstrates his commitment to developing robust analytical tools for complex historical data. As an educator, Wevers has developed teaching materials for audio analysis with Python, indicating his commitment to sharing computational methods with students and colleagues. His GitHub repository shows recent activity in March 2024, confirming his ongoing engagement with practical computational approaches to humanities research.

• Document Image Analysis
• Pattern Recognition
• Computer Vision
• +۳ مورد دیگر

Jean-Marc Ogier is a Professor and Teacher-Researcher at the University of La Rochelle , where he also serves as President. His research focuses on document image analysis, indexing techniques, and statistical-structural representation of shapes. Research Themes : Document image indexing, heritage digitization, handling noisy/heterogeneous data, and user-interactive semantic analysis. Teaching : Courses in information systems, signal/image processing, computer science, and international research training. Trends in Publications : His recent work explores computer vision , pattern recognition , and structural analysis in document processing. Key subtopics include Fourier-Mellin invariants , Galois lattice for classification, and topological measures for object recognition. Email : jean-marc.ogier@univ-lr.fr

Kathy B. Ewoldtمشاهده پروفایل

استادیار

• Special Education
• Learning Disabilities
• English Learners
• +۳ مورد دیگر

Dr. Kathy B. Ewoldt serves as Assistant Professor of Special Education within the College of Education and Human Development at the University of Texas at San Antonio. Leveraging seven years of public-school teaching experience in 6th-12th grade inclusion and resource classrooms, she specializes in improving academic outcomes for culturally and linguistically diverse students with disabilities in urban educational settings. Her research program centers on evidence-based writing interventions for students with learning disabilities and English learners, strategic promotion of STEM career pathways for students with disabilities, implementation of assistive technologies in K-12 education, and comprehensive policy development for service animals in schools. She has co-authored the authoritative text Service Animals in Schools: Legal, Educational, Administrative, and Handling Aspects and publishes in premier journals including Teaching Exceptional Children , Dyslexia , and Learning Disabilities: A Multi-Disciplinary Journal . Analysis of her 15 most recent publications (2020-2025) reveals three dominant research thrusts: (1) targeted literacy interventions for English learners with disabilities emphasizing color-cued writing strategies and paragraph construction; (2) assistive technology applications in computer science education for students with vision impairments; and (3) expanding social-emotional learning frameworks and STEM accessibility for neurodiverse learners. Her work consistently bridges research-practice gaps through rigorous methodology and equity-focused design. Dr. Ewoldt's scholarly impact is recognized through prestigious awards including the UTSA President’s Distinguished Faculty Achievement Award (2022) and Council for Learning Disabilities Outstanding Researcher Award (2021). Her complete honors portfolio features: Association of Teacher Educators Clinical Practice Fellow (2018) Council for Exceptional Children Division of Research Doctoral Student Scholar (2017) Council for Learning Disabilities Leadership Institute (2017) 41st Electronic Combat Squadron Airman of the Year (1990) Air Force 12 Outstanding Airman Nominee (1989) 41st Electronic Combat Squadron Maintenance Professional of the Year (1989) She actively directs national and state grant initiatives advancing educational equity for diverse learners, with current projects developing semantics-oriented screen readers for coding education and expanding STEM career pipelines for students with disabilities. While specific advisees aren't documented in source materials, her extensive publication record and grant leadership indicate significant mentorship of graduate researchers in special education.

Abe Davisمشاهده پروفایل

استادیار

• Computer Graphics
• Computer Vision
• Human-Computer Interaction (HCI)
• +۳ مورد دیگر

Abe Davis is an Assistant Professor of Computer Science at Cornell University in Ithaca, NY, with a research focus at the intersection of Computer Graphics , Computer Vision , and Human-Computer Interaction (HCI) . He leads a research group that develops innovative computational methods for video analysis, augmented reality (AR), and visual editing. Davis previously held postdoctoral positions at Stanford University and Cornell Tech , following his Ph.D. in Electrical Engineering and Computer Science from MIT (2016) and undergraduate degree in Computer Science at Stanford. Education Ph.D. in Electrical Engineering and Computer Science, MIT M.A. in Electrical Engineering and Computer Science, MIT B.S. in Computer Science, Stanford University Davis' research addresses novel problems in computational video editing, AR applications, and structural health monitoring. His group has made significant contributions to visual vibration analysis , light field reconstruction , and video-based sound recovery . Recent work includes Noise-Coded Illumination for fake video detection and MeCapture for mobile AR body change visualization. His publications span top-tier conferences like SIGGRAPH, CVPR, and CHI. Scientific recognition includes being named to Forbes 30 Under 30 (Science, 2015), 5 TED 2015 Technologists , and recognition as one of The 8 most innovative scientists in tech and engineering (2015). His TED 2015 talk on visual vibration analysis gained international attention. The group's work has been featured in Ars Technica , Engadget , and Cornell Chronicle .