جست‌وجوی استاد و پژوهشگر

Joseph McKayمشاهده پروفایل

دانشیار

• Interactive Art-Games
• VR/AR Development
• Computer History
• +۳ مورد دیگر

Joseph McKay is an Associate Professor of New Media at Purchase College (SUNY), specializing in interactive art-games and digital media. He holds a BFA from Nova Scotia College of Art & Design (1993) and an MFA from UC Berkeley (2007), with additional studies at the Whitney Independent Study Program (2000-2001). His work bridges fine art and playful interactivity, focusing on video games as modern storytelling mediums. He teaches courses like Experimental Game Lab, Programming for Visual Artists, and Physical Computing. Key research areas include VR/AR development, computational art, and the intersection of technology with everyday life. Notable projects include Awwful Tree Farmer (2024), Peckish (2023), and Zamboni Simulator . He has exhibited widely, including at MoMA, Berkeley Art Museum, and The Wrong Biennale. Awards include the Juanita & Joseph Leff Distinguished Professorship (2016–17) and the Eisner Award (2006). As an educator, he emphasizes hands-on technical skills and conceptual innovation. His exhibitions often explore themes like environmental interaction, surveillance, and playful critique of technology. He previously taught at UC Berkeley, Stanford, and Diablo Valley College, and directed the Woodstock Digital Media Festival (2011–2012).

Thinh Nguyenمشاهده پروفایل

استاد

• Stochastic Processes
• Signal Processing
• Video Coding
• +۳ مورد دیگر

Thinh Nguyen is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University . His research spans theories and applications of stochastic processes, with focus areas in signal processing, video coding, networking, communication systems, and quantum information theory. He co-authored multiple award-winning papers and received the NSF CAREER Award (2009) and Engelbrecht Young Faculty Award (2007) . Education : Ph.D. in Electrical Engineering and Computer Science from UC Berkeley (2003), B.S. in Computer Engineering from University of Washington (1995) Research Focus : Information representation, processing, and transmission with applications in multimedia streaming optimization Key Contributions : Network coding theories for efficient multimedia transmission His recent publications address topics in machine learning optimization, fluid dynamics in medical systems, and microfabrication techniques. Outside academia, he maintains strong musical interests as a classical violinist and emphasizes family values. Scientific Awards NSF CAREER Award (2009) Engelbrecht Young Faculty Award (2007) Currently advises graduate students in information theory and signal processing while leading research at the intersection of electrical engineering and computer science.

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

Dr. Maryam Haghighat is a Lecturer at QUT's School of Electrical Engineering & Robotics, specializing in Machine Learning, Computer Vision, and Robotics. She holds a PhD from UNSW's School of Electrical Engineering and Telecommunications, where her thesis earned the 2020 UNSW Award for Outstanding Doctoral Thesis. Her postdoctoral research at the University of Oxford's Big Data Institute (2020–2022) focused on medical image analysis for the PathLAKE project. Currently, she leads AI projects with over $10M in funding, including ARC grants and industry collaborations. Her research spans applications in robotics, healthcare, and remote sensing. Notable contributions include developing machine learning algorithms for histopathology image analysis and robust multi-robot localization systems. Awards include the UNSW Doctoral Thesis Award (2020). She actively supervises PhD students in topics like multimodal fusion and 3D visual understanding. Publications highlight advancements in AI-driven medical imaging, hyperspectral transformers, and illumination-aware video compression. Her work bridges theoretical machine learning with practical applications in healthcare, robotics, and environmental monitoring.

Ifeoma Nwoguمشاهده پروفایل

دانشیار

• Human Behavior Modeling
• Sign Language Understanding
• Probabilistic Modeling
• +۵ مورد دیگر

Ifeoma Nwogu is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo. She holds an appointment in the School of Engineering and Applied Sciences, specializing in research areas including human behavior modeling, sign language understanding, and probabilistic modeling. Her work spans computer vision, robotics, and AI with applications in healthcare, education, and social signal processing. Her research focuses on advancing technologies for sign language interpretation, infant vocal analysis, and human-robot interaction. Key projects include developing automated code generation frameworks for social robots, 3D motion synthesis systems for sign language avatars, and methodologies for emotion recognition in facial interactions. She teaches courses such as CSE 455/555 (Pattern Recognition), CSE 610 (Special Topics), and CSE 706 (Seminars), reflecting her expertise in AI and machine learning. Nwogu's recent publications emphasize cross-disciplinary applications of machine learning, including medical imaging anonymization, multi-agent systems, and ethical considerations in data privacy. Her work bridges computational methods with societal needs, particularly in education accessibility for deaf communities and stroke rehabilitation technologies.

Patricia Shawمشاهده پروفایل

مدرس ارشد

• Robotics
• Developmental Robotics
• Cognitive Developmental Systems
• +۱۲ مورد دیگر

Patricia Shaw is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. Her research lies at the intersection of robotics, cognitive science, and developmental psychology, with a focus on creating intelligent systems inspired by infant learning. She actively contributes to projects in human-robot interaction, assistive technologies, and developmental robotics. Her research interests include: Developmental and cognitive robotics Schema-based learning and hierarchical behavior modeling Human-robot interaction and assistive robotics Object recognition and multi-modal perception Autonomous navigation using environmental data Infant psychology-inspired AI Her recent publications demonstrate a strong trend in using humanoid robots to model infant cognitive development, developing smart assistive devices like wheelchairs and robotic companions, and advancing perception and navigation in unconstrained environments. These works span journals such as IEEE Transactions on Cognitive and Developmental Systems and conference proceedings from TAROS and UKRAS. Her scientific awards include: EPSRC Photo Competition 2014 EPSRC Photo Competition 2015 EPSRC Photo Competition 2016 She has supervised multiple PhD students including Tobi Alagbe, Cloud Coppola, Sam Heron, and Nikola Nikolov, and has been involved in externally funded research grants from the EPSRC and the Royal Society. She has also led projects on robotic assistive living and smart home technologies. Her leadership extends to managing research infrastructure such as the iCub humanoid robot and the Smart Home Facility. She is actively involved in research labs and teams including: iCub Humanoid Robot research group Smart Home Facility team Aberystwyth Robotics Lab Human-Robot Interaction research group Developmental Robotics Lab Assistive Technology Research Group

Jose Manuel Claver Iborraمشاهده پروفایل

استاد

• Parallel and Distributed Computing
• Cloud Computing
• Video Coding
• +۷ مورد دیگر

Dr. José Manuel Claver Iborra is a Full Professor at the Department of Computer Science, School of Engineering (ETSE-UV), University of Valencia. He holds a PhD in Computer Science (Parallel and Distributed Computing program) from the Technical University of Valencia and an MSc in Physics (specialized in Electronics and Computer Science) from the University of Valencia. As an IEEE Senior Member, he focuses on cloud computing, video coding, parallel/distributed systems, reconfigurable computing, and network protocols for real-time applications. Current Research: Cloud-based video encoding, GPU acceleration for DNA analysis, FPGA-based network protocols, and indoor localization systems Academic Leadership: Coordinator of the UV-Tirant node in the Spanish Supercomputing Network (RES) His recent publications analyze GPU-based motion estimation, heterogeneous computing for video standards (H.264/AV1), and QoS scheduling algorithms. He supervises PhD and Master's theses on sensor networks, FPGA programming platforms, and parallel applications. Scientific Awards IEEE Senior Member He has directed funded projects on cloud infrastructure, distributed video processing, and reconfigurable systems since the 1990s.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +۶ مورد دیگر

Prof. Dr. Didier Stricker is a distinguished Professor of Computer Science at Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU) and serves as Scientific Director and Head of the Augmented Reality Research Department at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Augmented Vision Group, which comprises approximately 30 researchers working across various domains of computer vision and augmented reality. His work bridges academic research with industrial applications through collaborations with major companies including Sony, Google, and John Deere. His educational background includes electrical engineering studies at the Polytechnic Institute of Grenoble and the Technical University of Karlsruhe. He earned his doctorate from the Technical University of Darmstadt in 2002 with a dissertation on "Computer Vision-Based Calibration and Tracking Methods for Augmented Reality Applications." Prof. Stricker's research spans virtual and augmented reality, computer vision, human-computer interaction, cognitive interfaces, and on-body sensor networks. His work focuses on developing practical applications that enhance human capabilities through advanced visual computing technologies. He has pioneered approaches in video and sensor analytics, particularly in creating cognitive interfaces that respond intelligently to user needs and environmental contexts. His recent publications reveal a strong emphasis on 3D scene understanding, real-time processing for augmented reality applications, and the integration of large language models with spatial reasoning capabilities. There's a clear trend toward more sophisticated multimodal approaches that combine vision, language, and spatial understanding to create more natural and intuitive human-computer interactions. Among his notable achievements: Innovation Prize of the German Society of Computer Science (2006) Organized the first IEEE & ACM International Symposium on Mixed and Augmented Reality (ISMAR) in 2002 Member of the ISMAR steering committee from 2000-2007 Multiple best paper and demonstration awards at major conferences Several registered patents in tracking and augmented reality technologies Prof. Stricker has supervised numerous PhD and Master's students through his leadership of the Augmented Vision Group. His research is supported by significant funding from both European and national research organizations, as well as through industrial partnerships. He serves as an expert reviewer for various research funding bodies and contributes to the academic community through editorial roles for journals and conferences in VR/AR and computer vision. The Augmented Vision Group under his direction maintains strong connections with industry partners and participates in numerous collaborative research projects including LUMINOUS, SHARESPACE, I-Nergy, BIONIC, and VIDETE. These projects span applications in language-augmented XR systems, social experiences in hybrid spaces, AI for energy systems, personalized body sensor networks, and 4D scene analysis.

Jiří Žáraمشاهده پروفایل

استاد

• Virtual Reality
• Computer Graphics
• Computational Geometry
• +۳ مورد دیگر

Jiří Žára is a Professor at the Czech Technical University in Prague, currently serving as Head of the Department of Computer Graphics and Interaction within the Faculty of Electrical Engineering. With a career spanning over four decades at the institution, he has held roles from PhD student (1982-1986) through Assistant Professor (1986-1997), Associate Professor (1997-2007), to Full Professor status since 2007. Department Head: 2008-present Computer Graphics Group Leader: 2002-2008 Supervisor: Computer Graphics Laboratory His research focuses on Virtual Reality , 3D Modeling , and Computational Geometry , with particular emphasis on VR educational applications, historical city visualization, and geometric algorithms. He has contributed significantly to VRML standards and interactive visualization techniques. Key research trends evident in his 96+ publications include real-time deformation techniques for 3D models, adaptive caching systems in rendering, and automated colorization algorithms for historical cartoons. His work demonstrates strong interdisciplinary connections between computer graphics , cultural heritage preservation , and educational technology . As academic supervisor, he has guided numerous students including: Ondřej Pluhař (Interactive Tokamak Golem Model, 2011) Daniel Šrám (Virtual Gallery in St. Peter & Paul Church, 2010) Jan Zimandl (Castle Park VRML Tour, 2009) He leads multiple EU-funded projects including: ARiSE (Augmented Reality in School Environments) VHCE (Virtual Heart of Central Europe for cultural heritage) V3C (Visual Computing Competence Center) RCI (Research Center for Informatics)

Shakeel Ahmadمشاهده پروفایل

دانشیار

• Multimedia Communications
• Computer Networks
• Network Optimization
• +۵ مورد دیگر

Dr. Shakeel Ahmad is an Associate Professor in the Department of Science and Engineering at Solent University since 2015. He leads the BSc (Hons) Cyber Security Management, BSc (Hons) Computer Systems and Networks Engineering, and MSc Applied AI and Data Science programs. His research focuses on optimizing multimedia communications and computer networks to maximize user quality of experience, particularly addressing challenges in video streaming over mobile and wireless networks. He holds a PhD (Dr.-Ing) from the University of Konstanz (2008), an MSc from TUHH, and a BSc from UET Lahore. Research Interests: Multimedia Communications Network Optimization for Video Streaming 4K/8K Video Transmission Virtual Reality Network Challenges Error Resilience Techniques His recent work explores digital content strategy in higher education, including website design, student recruitment tactics, and simplification of educational content ecosystems. He has secured significant funding, including £500k from the Office for Students (OfS) in 2020 for an AI/Data Science conversion course and £50k in 2021 for a National Data Skills Pilot project. Awards/Fellowships: Fellow of the Higher Education Academy PG Certificate in Higher Education Teaching & Advising: He teaches multimedia communications, AI, data science, and cybersecurity. Supervised multiple PhD completions and contributed to successful research projects like the EU-funded 'Community Network Game' (2010-2012). Active member of IEEE and IET.

Renjie Liaoمشاهده پروفایل

استادیار

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

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

• Image Processing
• Video Compression
• Computer Vision
• +۲ مورد دیگر

Maggie Zhu (Fengqing Maggie Zhu) is an Assistant Professor at Purdue University's Department of Electrical and Computer Engineering, College of Engineering. She holds a Ph.D. in Electrical and Computer Engineering from Purdue (2011) and has focused on image processing, video compression, computer vision, and computational photography since joining the faculty in 2015. Ph.D. in Electrical and Computer Engineering (2011) Assistant Professor at Purdue (2015–present) Staff Researcher at Huawei Technologies (2012) Her research bridges machine learning with practical applications in image compression , 3D reconstruction , and nutrition analysis , particularly through wearable technologies and edge-cloud systems. Recent work includes class-incremental learning for 3D perception and low-rank adaptation for efficient vision models. Scientific awards include: Huawei Certification of Recognition (2012) NIH mHealth Summer Institute Participant (2011) Charles C. Chappelle Graduate Fellowship Motorola Foundation Fellowship She has contributed to food portion estimation using monocular imaging , neural video compression , and domain adaptation methods, with publications spanning learned compression techniques and healthcare applications. Recent grants focus on technology-enabled dietary assessment and collaborative computing frameworks.

André Zaccarinمشاهده پروفایل

استاد

• Image and Video Coding
• Computer Vision
• Signal Processing
• +۷ مورد دیگر

André Zaccarin is a Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Université Laval, where he has been a faculty member since 1991. He holds a Ph.D. in Electrical Engineering from Princeton University and has maintained a strong research presence in image and video processing, computer vision, and coding algorithms. Ph.D. in Electrical Engineering, Princeton University (1991) M.A., Princeton University (1988) M.Sc. in Electrical Engineering, Université Laval (1987) B.Sc.A. in Electrical Engineering, Université Laval (1985) His research focuses on the study and development of advanced coding algorithms for still images and video sequences. Key areas include dense motion field estimation, model-based coding, 3D motion models, segmentation-based coding, and fast coding algorithms. He also investigates image segmentation, analysis and modeling, with applications in medical imaging and motion estimation for computer vision. Hyperspectral image processing is another significant area of interest. With 61 available publications, his scholarly output reflects sustained contributions in signal and image processing, particularly in compression and computer vision. The body of work shows consistent engagement with algorithmic innovation, motion analysis, and practical implementations in imaging systems. While specific scientific awards are not listed in the provided text, his long-standing academic career and industrial research role suggest recognition within the field. André Zaccarin has supervised multiple student projects, though specific names are not provided. He was also affiliated with Intel Corp. as a Senior Staff Researcher at the Microprocessor Research Labs from 2000 to 2001, indicating industry collaboration. He served as an Invited Researcher at Princeton University during summer 1992. He is affiliated with the Computer Vision and Systems Laboratory at Université Laval, where his research group conducts work in vision systems and image processing technologies.

Filippo Bergamascoمشاهده پروفایل

دانشیار

• Computer Vision
• Oceanography
• Image Processing
• +۳ مورد دیگر

Filippo Bergamasco is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. He is a member of the Scientific Committee of the Center for Studies on Port Economics and Management (DAIS). His work bridges computer vision, environmental monitoring, and oceanography, with a focus on developing advanced imaging techniques for ocean wave analysis and stereo reconstruction. He leads projects like the WASS pipeline, an open-source tool for 3D stereo reconstruction of ocean waves, and collaborates on studies involving rogue wave dynamics, low-cost sensors, and Arctic sea-state analysis. His research leverages machine learning, deep learning, and real-time systems to address challenges in environmental and marine sciences. Research interests include computer vision applications in oceanography, stereo imaging systems, wave dynamics modeling, and environmental monitoring technologies. He has contributed to over 80 peer-reviewed publications, with recent works focusing on real-time wave tracking, 3D reconstruction, and the integration of physics-driven models with neural networks. His expertise spans interdisciplinary collaborations, including projects on fish oil's impact on wind waves and the development of robust camera calibration methods. He actively publishes in top journals like IEEE Transactions on Image Processing, Ocean Modelling, and Physical Review Letters, and engages in international conferences such as ECCV and ICPRAM.

• Image and Video Processing
• Light Field Coding
• Multimedia Signal Processing
• +۶ مورد دیگر

Caroline Conti is an Assistant Professor in the Department of Information Science and Technology (ISTA) at ISCTE – University Institute of Lisbon, and an Associate Researcher at the Institute of Telecommunications - IUL, where she is part of the Multimedia Signal Processing Group. She holds a PhD in Information Science and Technology from Iscte (2017), a specialization from Instituto Superior Técnico (2013), and a Bachelor’s in Electrical Engineering from the University of São Paulo (2010). Her research focuses on image and video processing , particularly in light field coding , 3D holoscopic video , and immersive visual technologies . She is a pioneer in light field research in Portugal and has contributed extensively to scalable and robust coding frameworks. Her recent publications emphasize deep learning-based segmentation, disparity estimation, and adaptive over-segmentation for 4D light fields, published in top journals like IEEE Transactions on Image Processing and Signal Processing: Image Communication . IBM Scientific Prize (2017) Scientific Awards ISCTE-IUL (2016, 2014) Best Poster Award at COST Interaction 2014 She has supervised multiple PhD and Master’s students in areas such as deep learning for light field inpainting and saliency detection. She has led and participated in European and national research projects, including the European 3D-ConTourNet and FCT-funded LIMESA. She is actively involved in the academic community as a Guest Editor for Signal Processing: Image Communication and as an Area Chair for IEEE ICIP 2025. She is a founding member and secretary of the Portuguese Chapter of the IEEE Signal Processing Society.

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.