جست‌وجوی استاد و پژوهشگر

Olivier Canévetمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Olivier Canévet is a Lecturer at EPFL, affiliated with both the School of Engineering (STI) and the School of Life Sciences (VPA-AVP-DLE) divisions. He holds a dual role as a Research Engineer at the Idiap Research Institute and serves as a Lecturer in the Department of Electrical Engineering (IEM) and the EDEE-ENS teaching unit. His primary affiliations include the Learning and Information Systems Laboratory (LIDIAP) at EPFL. Education: PhD in Electrical Engineering from EPFL (2012–2016), supervised by Dr. François Fleuret. Engineering diploma from Télécom Bretagne (2007–2012). Additional experiences include a CNES internship on satellite imagery and CERN projects on Invenio ranking systems. He also spent a year managing geophysical observatories in the French Southern and Antarctic Lands. Research Interests: Focuses on Computer Vision, Machine Learning, and Deep Learning applications in human-robot interaction, security systems, and real-time perception. Specializes in depth-based networks for human pose estimation, multi-person tracking, and socially intelligent robotic systems. Article Trends: Recent work emphasizes real-time CNN architectures for depth-based human pose estimation, multi-party human-robot interaction datasets (MuMMER), and security systems leveraging depth maps (UNICITY database). Prior contributions include efficient sample mining techniques and domain adaptation methods. Grants & Funding: His PhD was funded by the Swiss National Science Foundation (DASH project). Current research benefits from EPFL's interdisciplinary infrastructure and Idiap's resources in machine learning and robotics. Labs & Teams: Leads projects at LIDIAP lab (EPFL) focusing on computer vision applications, collaborates with Idiap on security systems, and contributes to teaching initiatives in the EDEE-ENS unit.

• Autonomous Robotics
• Computational Intelligence
• Human-machine Interaction
• +۳ مورد دیگر

Dr. Wai-keung Fung is a Senior Lecturer in Electronics, Robotics & Control Engineering at Cardiff Metropolitan University's Cardiff School of Technologies. He holds a PhD in Mechanical and Automation Engineering from the Chinese University of Hong Kong (2001) and has held academic roles at the University of Manitoba (Canada) and Robert Gordon University (UK). He is Programme Director for the BEng/MEng in Electronics and Computer Systems Engineering and co-leads the EUREKA Robotics Centre. He is a Fellow of the Higher Education Academy (FHEA), IEEE Senior Member, and IET member. His research focuses on Autonomous Robotics , Computational Intelligence , and Human-machine Interaction , with recent work spanning gender bias in speech processing, underwater communication systems, and medical diagnostics via EEG analysis. His interdisciplinary contributions bridge robotics, machine learning, and engineering applications. Awards: Fellow of the Higher Education Academy (FHEA) since 2015 IEEE Senior Member since 2009 IET Member since 2017 Labs & Affiliations: EUREKA Robotics Centre (Cardiff School of Technologies) His research has been published in IEEE conferences and journals, addressing challenges in robotics, medical AI, and pipeline monitoring. He continues to advance applied technologies with industry and academic collaborations.

Athanasios Koutrasمشاهده پروفایل

دانشیار

• Digital Signal Processing
• Biomedical Engineering
• Machine Learning
• +۸ مورد دیگر

Athanasios Koutras is an Associate Professor at the Department of Electrical and Computer Engineering, University of Peloponnese. He holds a PhD in Electrical and Computer Engineering from the University of Patras (2001), focusing on blind speech separation. His career includes roles as Deputy Head and Head of the Department of Informatics and Media at TEI of Patras (2008–2018) and academic positions at institutions like the University of Ioannina and Aristotle University of Thessaloniki. He has supervised over 160 theses and teaches courses in digital signal processing, biomedical engineering, and multimedia technologies. Research Interests: Digital sound/image processing, biomedical signal analysis, machine learning, computer-aided diagnosis, EEG/MEG analysis, deep learning, brain-computer interfaces (BCI), and tourism data forecasting. Founding Director of the Signal, Image Processing & Pattern Recognition (SIPPRE) Research Group, he explores applications in healthcare, audiovisual technologies, and smart cities. His work spans interdisciplinary collaborations, including medical neuroscience and tourism analytics. Awarded editorial roles in journals like Advances in Signal Processing and MDPI Electronics , he actively mentors through IEEE programs. Office hours: Monday 13:00–14:00, Tuesday 11:00–13:00 (Teams).

Nikolaos Petrellisمشاهده پروفایل

دانشیار

• Embedded Systems
• Medical Applications
• Automation
• +۳ مورد دیگر

Nikolaos Petrellis is an Associate Professor in the Electrical and Computer Engineering Department at the University of Peloponnese. He holds a PhD in Electrical Engineering and Computer Technology from the University of Patras (1999). Previously, he served as an Associate Professor at the University of Thessaly and TEI of Thessaly until 2019 and was the head of the Computer Engineering Department from 2016 to 2018. His research focuses on embedded systems design for telecommunications, medical applications, and automation, with a particular emphasis on mobile applications leveraging image processing and machine learning. His research interests include the design of embedded systems for telecommunications, medical applications, and automation. He has developed mobile applications using image segmentation and classification techniques, as well as mixed-signal design, particularly in analog-digital converter (ADC) design. His work often integrates machine learning for medical diagnostics and automation solutions. Recent publications highlight his contributions to embedded systems, including frameworks for real-time software verification, FPGA implementations for driver state monitoring, and mobile disease classification tools. His work spans applications in healthcare, environmental monitoring, and telecommunications, emphasizing efficient signal processing and system reliability. He has advised numerous students and contributed to over 100 peer-reviewed publications and 9 patents. His research has been supported by collaborations with industries like Analogies SA and GiGA Hellas. He is affiliated with research groups focusing on embedded systems and cyber-physical systems within the University’s Department of Electrical and Computer Engineering.

Dan Andrei Iliescuمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Generative Models
• +۴ مورد دیگر

Dan Andrei Iliescu is a fifth-year PhD Student at the Department of Computer Science and Technology , University of Cambridge. His research focuses on self-supervised methods for generative models , particularly in representation learning of grouped data and latent-space disentanglement . Education: BSc in Computer Science (University of Manchester), MPhil in Advanced Computer Science (University of Cambridge), PhD in progress (University of Cambridge, funded by the Alan Turing Institute). Dan's research spans machine learning , artificial intelligence , generative models , and computer vision . His work addresses missing data imputation , unsupervised domain adaptation , image translation , and collaborative filtering . His recent publications at conferences like ICLR , ICASSP , and WACV highlight trends in disentangled representations , prosody control , and loss function optimization . Dan also contributes to open-source projects and has developed tools like visualizing-GAN-layers and ai-painting . Artistic Achievement: His ML-based art won a competition at St Edmund’s College, Cambridge. Teaching roles include supervising Cambridge undergraduates in Information Theory , Machine Learning , Data Science , and Economics, Law, and Ethics courses. He also explores ML tools for artists and maintains a GitHub profile with repositories like visualizing-GAN-layers (Python, TensorFlow, DCGAN analysis) and ai-painting (image reconstruction via patches).

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۵ مورد دیگر

Dr. Miaomiao Liu is a Research Fellow at the School of Computing, The Australian National University. Her research focuses on computer vision, 3D reconstruction, and neural rendering, with applications in robotics, autonomous systems, and renewable energy forecasting. She leads multiple projects including Next-Generation Aviation Safety Net Air Traffic Management, Machine Vision Techniques for Solar Power Forecasting, and 3D Vision Geometric Optimization in Deep Learning. Her work integrates cutting-edge techniques such as neural radiance fields, depth estimation, and self-supervised learning to address challenges in dynamic scene reconstruction, motion forecasting, and image deblurring. She has pioneered methods for mining supervision signals in dynamic regions and developing language-driven deblurring networks. Key Projects: Aviation safety systems, solar irradiance prediction, and geometric optimization in deep learning Research Themes: 3D scene understanding, human motion prediction, and neural rendering Technical Expertise: Neural networks, multi-view stereo, and physics-based modeling Dr. Liu's research has been published in top-tier venues like CVPR and IEEE conferences, with over 2,450 citations. While not explicitly listed as part of a lab, her work demonstrates strong collaboration with industry partners such as CSIRO and aerospace stakeholders. She actively supervises research students in areas like 3D vision and energy systems.

Zhanyang Zhangمشاهده پروفایل

استاد

• Wireless Communication and Networks
• Cyber-Physical Systems & IoT/Sensor Networks
• Numerical Modelling and Simulation
• +۶ مورد دیگر

Dr. Zhanyang Zhang is a Professor of Computer Science at the City University of New New York, jointly appointed at the College of Staten Island and the CUNY Graduate Center. His expertise bridges wireless networking, cyber-physical systems, IoT, numerical simulation and high-performance computing, with recent emphasis on 5G/edge intelligence, ocean oil-spill modelling, and assistive technologies for the visually impaired. Education: Ph.D. in Computer Science, City University of New York M.S. in Computer Science, College of Staten Island B.E. in Computer Engineering, Jilin University, Changchun, China Research Interests: Dr. Zhang's research integrates communication theory, data science and large-scale simulation. He develops protocols for next-generation wireless networks, designs energy-efficient algorithms for underwater and terrestrial sensor systems, and applies lattice-Boltzmann methods to environmental disasters such as oil-spill propagation and urban flooding. His group also explores deep-learning-driven anomaly detection in 5G and edge-computing offloading strategies using game-theoretic and reinforcement-learning frameworks. Publication Trends: Across the past decade his scholarly output has shifted from foundational sensor-network coverage and localisation toward interdisciplinary applications: assistive smart-city services, GPU-accelerated ocean-surface transport models, and AI-based traffic and flood prediction. A recurrent methodological thread is the fusion of stochastic modelling (Markov, game, queueing) with high-performance simulation to derive scalable, real-time solutions. Grants & Projects: He currently leads or co-leads projects funded by the U.S. National Science Foundation and multiple CUNY schemes, totalling more than USD 1 M since 2020. Topics range from assistive cyber-connections for the visually impaired to next-generation oil-spill forecasting assimilating large sensor datasets. Labs & Teams: Dr. Zhang heads the Wireless & Cyber-Physical Systems Research Group at CSI, maintains affiliations with the CUNY High-Performance Computing Center, and supervises doctoral students within the Ph.D. Program in Computer Science at the Graduate Center.

John Darbyمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Human Movement Analysis
• +۵ مورد دیگر

Dr. John Darby is a Senior Lecturer in the Department of Computing, Mathematics and Digital Technology at Manchester Metropolitan University (MMU). He serves as the programme leader for the MSc Computer Science and teaches AI and machine learning modules. His research focuses on non-invasive Computer Vision techniques for analyzing human movements, spanning from skeletal muscle analysis to behavior classification and human-object interactions. He holds a PhD from MMU and has industry experience as a Software Engineer at Echostar Europe. Education: BSc Hons. in Computational Physics (University of Edinburgh, 2000–2003), MSc in Mobile and Distributed Computer Networks (Leeds Metropolitan University, 2003–2004), and a PhD from MMU (2006–2010). Postdoctoral roles included Research Assistant at IRM (2010–2011) and Research Associate at SCMDT (2011–2013). Research Interests: His work integrates Machine Learning, Image Processing, and Particle Filtering to address challenges in surveillance, biomechanics, and healthcare. Key applications include markerless motion tracking, implement recognition, and clinical decision support systems. He collaborates with the Image and Sensory Computation Group on interdisciplinary projects. Advising & Grants: While specific student names or grant details are not listed, his role as a Senior Lecturer and programme leader implies involvement in supervising postgraduate students and securing research funding. His projects often involve clinician-led approaches and industry partnerships. Labs/Teams: Active contributor to the Image and Sensory Computation Group, focusing on human movement analysis and healthcare technology applications.

James Krauseمشاهده پروفایل

مدرس ارشد

• Media Production
• Visual Design
• Documentary Production
• +۱۲ مورد دیگر

James Krause is a Senior Lecturer and Director of Media Arts & Production at The Media School, Indiana University, where he has been teaching since 1999. His expertise spans media production, visual design, documentary filmmaking, and music composition. Dr. Krause holds an M.S. in Instructional Systems Technology from IU School of Education, a B.A. in Telecommunications from Indiana University, and an A.S. in Audio Technology from IU School of Music. His teaching portfolio includes multi-camera TV studio production, video field and post-production, graphics and animation, and cinematography courses, with additional specialty areas in audio production, lighting, and motion graphics. His professional experience encompasses 30 years of video production and two years as an audio engineer. He directs Tabletop Productions, a digital project studio serving corporate, broadcast, and educational clients. His creative work demonstrates a strong commitment to documenting Indiana's cultural heritage through projects like The Indiana Theatre at 100 (2023), The Spirit of Brown County , and Elkinsville: Washed Away by Progress . Dr. Krause's recent output shows consistent productivity across documentary filmmaking, music composition, and educational media. His 2023 documentary on the historic Buskirk-Chumley Theatre represents the culmination of his expertise in historical documentation and public broadcasting, premiering on WTIU PBS. His work spans traditional documentaries, concert films, institutional promos, and artistic profiles, demonstrating versatility across media formats. Documentary filmmaking focusing on Indiana's cultural heritage Music composition blending classical, folk, and indigenous traditions 360-degree video production for educational and festival contexts Collaborative projects with artists across disciplines As a musician, Dr. Krause has released two acclaimed instrumental albums, Crossing the Inland Sea (2009) and Madeleine Bay (2010), featuring his guitar compositions paired with cellist Anne Hurley. These collections, inspired by travels under sail and by foot, have received praise for their "exceptional beauty and artistry." He recently formed the Longwood Trio with bassist Dave Bruker and is working on an upcoming project tentatively titled Ontario .

Laurent Cleret de Langavantمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Neurology
• Clinical Neurology
• +۳ مورد دیگر

Laurent Cleret de Langavant is an Assistant Professor in Neurology at Paris Est University and a senior Atlantic Fellow for Equity in Brain Health at the Global Brain Health Institute (GBHI). He holds a faculty position at the NeuroPsychologie Interventionnelle laboratory, affiliated with the Ecole Normale Supérieure (Paris) and Henri Mondor Hospital (Créteil). His research focuses on improving care for patients with cognitive disorders, particularly social disturbances and dementia, using experimental psychology, neuroimaging, and machine learning. He leads studies on Huntington’s disease progression, dementia prediction through population data analysis, and the impact of Annonaceae consumption on neurodegenerative conditions. Clinical roles include neurology at AP-HP (Assistance Publique – Hôpitaux de Paris). Education details are not explicitly stated, but his academic trajectory is reflected in his roles and publications. Research interests emphasize translational neurology, machine learning applications in healthcare, and clinical trial methodologies. His articles highlight work on dementia epidemiology using unsupervised clustering, Huntington’s disease biomarkers, and the role of striatal dysfunction in timing deficits. Recent studies explore modifiable risk factors and care burden in neurodegenerative diseases. His lab collaborations span neuroimaging, behavioral studies, and interdisciplinary clinical trials.

Dengsheng Zhangمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Big Data
• Image Classification
• +۵ مورد دیگر

Dr. Dengsheng Zhang is a Senior Lecturer at Federation University Australia (formerly Monash University) and a Guest Professor at Xi'an University of Posts & Telecommunications, China. He holds a PhD in Computing (2002) and a GCHE (2006). His expertise spans over 25 years in artificial intelligence, big data, image processing, and music classification. He has published over 100 refereed papers (10,000+ citations) and authored the award-winning book Fundamentals of Image Data Mining (Springer, 2019). His research focuses on image classification, object detection, deep learning, and feature extraction. Dr. Zhang leads the Computational Science and Mathematics Group, contributing to projects like SIRBOT (Semantic Image Retrieval) and VFR (Visiting Friends and Relatives Travel Research). He serves as an Associate Editor for World Scientific's Transactions on Signal Processing. His teaching spans calculus, web design, computer networks, and project management. He has supervised 8 completed PhD students and actively engages in grants, including two ARC Discovery projects. His lab explores innovations in image retrieval, music classification, and machine learning applications.

Dario Prandiمشاهده پروفایل

پژوهشگر

• Sub-Riemannian Geometry
• Neural Fields
• Control Theory
• +۴ مورد دیگر

Dario Prandi is a Researcher at the Laboratoire des Signaux et Systèmes (L2S) within CentraleSupélec, located in Gif-sur-Yvette, France. His work bridges mathematical analysis, control theory, and neuroscience, with a focus on geometric models of vision and auditory processing. He holds a PhD from École Polytechnique (2013) and has authored numerous high-impact publications in journals like SIAM and IEEE conferences. Research interests span sub-Riemannian geometry, neural field equations, and mathematical neuroscience. Notable contributions include cortical-inspired models explaining visual illusions (e.g., MacKay and Poggendorff effects), sound reconstruction via bio-inspired geometric frameworks, and the application of control theory to image processing and neural dynamics. His work often combines advanced mathematical techniques with applications in biomedical engineering and artificial perception. Recent activities include developing homogeneous observers for cortical activity models and analyzing decay rates in nonlinear systems. He collaborates extensively with institutions like Inria and the University of Paris-Saclay, contributing to transversal research axes in energy, Industry 4.0, and health technologies. Labs/Teams: Member of L2S's research groups Modélisation et Estimation, Problèmes Inverses, and the Automatique et Systèmes department. His research aligns with interdisciplinary projects in geometric science of information and hypoelliptic diffusion processes.

Luca Cosmoمشاهده پروفایل

دانشیار

• Shape Analysis
• Geometric Deep Learning
• Spectral Geometry
• +۳ مورد دیگر

Luca Cosmo is an Associate Professor at Ca’ Foscari University of Venice, specializing in Shape Analysis, Geometric Deep Learning, and Spectral Geometry. His research focuses on 3D shape analysis, spectral methods, and generative models for computer vision tasks. He has contributed to projects like the Average Mixing Kernel Signature (AMKS) and isospectralization techniques, advancing robust shape representation and correspondence. Previously, he held postdoctoral positions at the Geometric Deep Learning group under Prof. Michael Bronstein and collaborated with the spin-off DigitalViews Srl on industrial projects for quality control. He actively serves on program committees for top conferences (CVPR, ICCV, ECCV) and journals (IJCV, PR). His work bridges spectral geometry with machine learning, addressing challenges in partial shape matching, adversarial attacks on deformable shapes, and latent space preservation in generative models. Key trends in his publications include advancements in 3D shape generation (e.g., UniMoGen, 3D-WAG), spectral methods for subgraph learning, and quantum-inspired approaches to shape analysis. He has organized tutorials on shape analysis and contributed benchmark datasets like SHREC’16/17. His research emphasizes practical applications in computer graphics, robotics, and industrial automation. Luca’s advising and grants involve collaborations with institutions like Oxford University (Antonio Norelli) and industry partners. His lab, GLADIA, focuses on geometric deep learning and generative models. Current work includes universal motion generation, high-fidelity 3D reconstruction, and scalable generative models for large datasets.

Zhen Gaoمشاهده پروفایل

دانشیار

• Automation Engineering Technology
• Robotics
• Industry 4.0
• +۵ مورد دیگر

Dr. Zhen Gao is an Associate Professor of Automation Engineering Technology at the W Booth School of Engineering Practice and Technology, McMaster University. He holds associate membership roles in the School of Computational Science & Engineering, Department of Civil Engineering, and McMaster School of Biomedical Engineering. His expertise spans Automation Engineering Technology, Industry 4.0, Health Technology, and Robotics. Research focuses on advanced manufacturing systems, smart systems, and biomedical engineering applications. Dr. Gao’s work integrates robotics, AI, and sensor technologies for practical applications like wound analysis, autonomous systems, and healthcare automation. His recent publications emphasize 3D object detection algorithms, machine learning for medical diagnostics, and robotics control systems. He advises students in emerging technologies, including Shiyu (Shannon) Chen, a Master of Engineering Systems and Technology student. His academic contributions include innovations in parallel robotic mechanisms, smart grid systems, and project-based learning methodologies. He actively collaborates with institutions like St. Joseph’s Healthcare Hamilton to advance healthcare technologies.

• Wearable Sensor Systems
• mHealth
• Biometric Sensor Technologies
• +۵ مورد دیگر

Dr. Masudul Imtiaz is an Assistant Professor in the Department of Electrical and Computer Engineering at Clarkson University, part of the Coulter School of Engineering & Applied Sciences. He leads the AVHBAC (AI Vision, Health, Biometrics, and Applied Computing) research lab and the Center for Advanced PCB Design and Manufacture (CAPDM). His research focuses on wearable sensor systems, mHealth technologies, biometric authentication, and assistive medical devices. He holds a PhD from the University of Alabama and has extensive postdoctoral experience in exoskeleton systems. Education: B.Sc. and M.Sc. in Applied Physics/Electronics (University of Dhaka, Bangladesh); PhD in Electrical & Computer Engineering (University of Alabama, 2019). Research Interests: Wearable sensors for health monitoring, AI-driven biomedical systems, pediatric rehabilitation robotics, and cybersecurity in biometric systems. His work addresses challenges in sensor design, machine learning for biosignals, and clinical applications such as gait analysis and prosthetic control. He collaborates with NSF-funded centers like CITeR and CREST. Advising: Currently supervising 4 PhD, 3 MS, and 23 undergraduate students. Funding sources include Clarkson University grants and NSF CITeR partnerships. His lab develops open-source pipelines for biomedical data and innovative devices like vision-enabled prosthetics and quantum-resistant file transfer systems. Labs/Teams: AVHBAC Lab, CAPDM Center, CITeR, CREST. Committees: ECE Curriculum Committees, Manufacturing Minor Committee, and CREST advisory board.