جست‌وجوی استاد و پژوهشگر

• Embedded Systems
• Computer Engineering
• Electronics
• +۶ مورد دیگر

Prof. Akash Kumar is a Professor at the Chair of Embedded Systems at Ruhr University Bochum, Germany. He previously held professorships at TU Dresden (2015–2024) and the National University of Singapore (NUS; 2011–2015). His research focuses on design automation of embedded systems, reliability optimization, and approximate computing, with a strong emphasis on FPGA and emerging technologies. He leads projects such as Lean-MICS (DFG-funded) and SecuREFET-II, addressing cross-layer reliability and secure circuits. Education: PhD in Multimedia Multiprocessor Systems from Eindhoven University of Technology (TUe) and NUS (2005–2009), Master of Technological Design (Embedded Systems) from NUS (2003–2004), and B.Eng (Computer Engineering) from NUS (1999–2002, First Class Honours). Research interests span embedded systems, reconfigurable architectures, and hardware-software co-design. His work includes optimizing energy efficiency, fault tolerance, and cross-layer approximation techniques. Recent publications highlight advancements in FPGA-based accelerators, machine learning optimizations, and mixed-criticality systems. Active in grants and leadership, Kumar is Principal Investigator on multiple DFG and industry-funded projects, emphasizing collaborative research in distributed computing and approximate architectures. His contributions bridge theory and practice, with applications in edge AI, IoT, and cybersecurity.

Latifur Khanمشاهده پروفایل

استاد

• Data Mining
• Cybersecurity
• Machine Learning
• +۲ مورد دیگر

Dr. Latifur Khan is a Professor in the Department of Computer Science at the University of Texas at Dallas' Erik Jonsson School of Engineering and Computer Science. He directs the Database and Data Mining Laboratory and conducts research in data mining, cybersecurity, and semantic web technologies. Research domains include: Large language models for threat detection Fairness in machine learning Vulnerability analysis in software systems Graph-based information retrieval Recent publications focus on AI security applications in transportation systems, political conflict analysis using NLP, and federated learning for IoT security. His work consistently bridges theoretical algorithms with practical cybersecurity implementations. Research grants include funding from NSF, NASA, Raytheon, Nokia, and SUN Microsystems. Teaching includes courses in Plant Breeding (PBG 450/550) and Breeding Clonal Crops (PBG 551).

Jing Yangمشاهده پروفایل

استاد

• Data Visualization
• Visual Analytics
• High Dimensional Data Analysis
• +۷ مورد دیگر

Jing Yang is a full Professor and Director of MS CS Program in the Computer Science Department at the University of North Carolina at Charlotte (UNCC). She has been actively involved in data visualization and visual analytics research since joining UNCC in 2005 after completing her PhD at Worcester Polytechnic Institute. Dr. Yang earned her Bachelor's degrees in Engineering Mechanics and Computer Science from TsingHua University in 1997 and completed her Ph.D. in Computer Science from Worcester Polytechnic Institute in May 2005 under advisors Matthew O. Ward and Elke A. Rundensteiner. Her research focuses on developing visual analytics techniques for abstract data including multidimensional data, time-oriented data, networks, hierarchies, text documents, and trajectory data. She conducts design studies for application domains such as sports, bioinformatics, finance, network security, and health, while exploring fundamental visualization topics like interactions, insight management, clustering-based approaches, and animations. Her recent work emphasizes sports analytics, urban data, and multivariate time series visual analytics. Analysis of Dr. Yang's publication record reveals a strong focus on practical applications of visualization techniques across diverse domains. Her work consistently bridges theoretical visualization frameworks with real-world data challenges, particularly in transportation (taxi trajectories), sports (tennis match analysis), financial transactions, and bioinformatics. The publications demonstrate an evolution from foundational visualization techniques to increasingly sophisticated domain-specific applications. Dr. Yang has secured substantial research funding from NSF, EPA, DHS, and industry partners including Google and Bank of America. Her grants portfolio includes significant projects like TrajAnalytics (NSF, $200,950), Visualizing Event Dynamics (NSF EAGER, $75,317), and Visualizing High Dimensional Categorical Datasets (Google Faculty Research Award, $60,000), among others totaling over $4 million in research support. As an educator, Dr. Yang has taught numerous courses including Visual Analytics, Information Visualization, and Database Design. She directs the Charlotte Visualization Center and has mentored numerous students through research projects. Her collaborative approach is evident in her extensive co-authorship network spanning multiple institutions and disciplines. Current research directions include narrative animation for streaming text visualization and advanced techniques for exploring high-dimensional categorical datasets.

Yalong Yangمشاهده پروفایل

استادیار

• Immersive Analytics
• Virtual Reality
• Data Visualization
• +۳ مورد دیگر

Yalong Yang is an Assistant Professor at the School of Interactive Computing at Georgia Institute of Technology. His research focuses on immersive analytics, virtual reality (VR), and augmented reality (AR) interfaces, with a particular emphasis on spatial interaction, hybrid user interfaces, and data visualization techniques. He explores how embodied interactions and immersive environments enhance understanding in domains like education, sports analytics, and collaborative decision-making. Key research themes include hybrid immersive systems (combining VR/AR with physical devices), asymmetric collaboration in mixed environments, and AI-driven tools for programming education. His work spans both theoretical frameworks and applied systems, such as SPHERE for scalable personalized feedback in coding classrooms and VizGroup for collaborative learning analytics. Yang’s recent publications highlight trends in spatial hybrid interfaces, navigation in immersive environments, and the integration of generative AI in educational technologies. His projects often involve evaluating interaction techniques (e.g., label placement in AR) and comparing performance across desktop and VR platforms. Notable contributions include the SportsXR initiative for immersive analytics in sports, and systems like CompositingVis for creating complex visualizations in 3D spaces. His work addresses challenges in situated analytics, wearable technologies for outdoor activities, and audience analysis in VR exhibitions.

• Digital Forensics
• Image Processing
• Multimedia Analysis
• +۴ مورد دیگر

Giulia Boato is an Associate Professor at the University of Trento’s Department of Information Engineering and Computer Science (DISI). She teaches courses in Probability and Multimedia Data Security. Her expertise spans Cyber Security, Digital Forensics, and Multimedia Analysis, focusing on image and signal processing for data protection, forensics, and anti-forensics. She collaborates internationally with institutions like Tampere University of Technology and Dartmouth College, co-advising PhD students and contributing to European projects like LIVINGKNOWLEDGE and GLOCAL. Education: PhD in Information and Communication Technology (2005), M.Sc. in Mathematics (2002), Scientific Lyceum (1998) with bilingual Italian-German certification. Past roles include Assistant Professor at DISI (2006–2018) and visiting researcher at the University of Vigo (2006) and University of Innsbruck (2018). Research interests include multimedia data protection, image forensics (tampering detection, computer vs. natural data discrimination), and intelligent data management. She leads projects on social media forensics, event-based retrieval, and synthetic media detection. Awards include Best Paper at IEEE WIFS 2012 and Top 10% Paper at MMSP 2012. Professional contributions include roles as co-chair of workshops, Technical Program Committee member for ICIP and ICC, and reviewer for journals like IEEE Transactions on Information Forensics and Security. She has advised PhD theses and contributed to datasets like TrueFace and WILD for synthetic media analysis.

Jure Leskovecمشاهده پروفایل

استاد

• Machine Learning
• Social Networks
• Data Mining
• +۴ مورد دیگر

Jure Leskovec is a Professor of Computer Science at Stanford University, affiliated with the Stanford AI Lab, Machine Learning Group, and the Center for Research on Foundation Models. He holds academic appointments in the Department of Computer Science and is a member of Bio-X, the Institute for Human-Centered Artificial Intelligence (HAI), and the Wu Tsai Neurosciences Institute. Leskovec earned his BSc from the University of Ljubljana (2004), PhD from Carnegie Mellon University (2008), and postdoctoral training at Cornell University. His research focuses on social networks, data mining, machine learning, and computational biomedicine, with contributions to graph neural networks, drug discovery, and AI applications in healthcare. His work has been applied to combat the COVID-19 pandemic and integrated into products at major tech companies. Leskovec’s publications reflect his expertise in network analysis, medical AI, and biological systems. His recent work includes foundational contributions to graph neural networks (e.g., PyG) and medical AI frameworks. His research has garnered numerous awards, including the Microsoft Research Faculty Fellowship and ICDM Research Contributions Award. Leskovec advises numerous doctoral and postdoctoral researchers, contributing to over 200 publications. His interdisciplinary collaborations span computational biology, healthcare analytics, and social systems, with a focus on leveraging AI to address real-world challenges.

Kobus Barnardمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Scientific Applications
• +۳ مورد دیگر

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

• Accessibility Research
• Web Accessibility
• Mobile Accessibility
• +۵ مورد دیگر

Shari Trewin is a prominent researcher in accessibility and human-computer interaction at IBM Research with over 25 years of scholarly contributions. Her work focuses on making digital technologies accessible to people with disabilities, particularly in web and mobile contexts. She has published extensively in top-tier venues including ACM SIGACCESS conferences (ASSETS, W4A) and journals, CHI, and other leading HCI publications. Dr. Trewin's research interests span web accessibility, mobile accessibility for users with physical and cognitive disabilities, inclusive design methodologies, and the application of artificial intelligence to improve accessibility. Her work addresses both theoretical foundations and practical implementations of accessible technologies, with particular emphasis on user-centered design approaches and evaluation methodologies. She has made significant contributions to understanding how people with disabilities interact with digital interfaces and how to design systems that accommodate diverse user needs. Her publication record shows a clear progression from foundational work on input devices and keyboard accessibility in the 1990s to contemporary research on AI fairness for people with disabilities. Recent publications demonstrate her leadership in addressing emerging challenges at the intersection of AI and accessibility, particularly around algorithmic fairness and inclusive design practices for AI systems. Among her notable contributions are editorial work for ACM Transactions on Accessible Computing and co-editing conference proceedings for the ASSETS conference. She has collaborated extensively with leading researchers in the field including Vicki L. Hanson, Gregg Vanderheiden, and Calvin Swart. Dr. Trewin has advised junior researchers including Jessica J. Tran, and her work has influenced both academic research and industry practices in accessibility. Her research has practical implications for web developers, designers, and policy makers working to create more inclusive digital experiences.

Slim Essidمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۷ مورد دیگر

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.

François Brémondمشاهده پروفایل

پژوهشگر

• Computer Vision
• Cognitive Vision
• Video Understanding
• +۲۲ مورد دیگر

François Brémond is a Research Director (DR1) at INRIA Sophia Antipolis, where he leads the STARS research team, which he founded on January 1, 2012. He was previously head of the PULSAR team starting September 2009. He is also a co-founder of the CoBTeK team at Nice University in collaboration with Nice Hospital, focusing on behavioral disorders in elderly patients with dementia. His research is centered on dynamic scene interpretation using video and sensor data, with applications in surveillance, healthcare, transportation, and ambient intelligence. Research Interests: Computer Vision: video processing, object detection and tracking, motion analysis, pattern recognition Cognitive Vision: video understanding, scene understanding, event recognition, behavior analysis, multi-sensor fusion, multimedia interpretation Machine Learning: deep learning architectures, self-attention, knowledge distillation, contrastive learning, self-learning, lifelong learning, knowledge-based systems, spatio-temporal reasoning Autonomous Systems: real-time systems, system evaluation, parameter tuning, system design, 3D visualization His work bridges low-level pixel data with high-level semantic behavior modeling, enabling systems to detect and interpret complex human and vehicle activities in real-world environments. Applications include crowd monitoring, fraud detection, airport operations, homecare for the elderly, and biological monitoring. He has authored or co-authored over 200 scientific papers and has (co-)supervised 18 PhD theses. He has participated in 12 European projects (e.g., FP6, FP7), 12 French national projects (ANR, DGE), and numerous industrial collaborations with companies such as Thales, SNCF, RATP, STMicroelectronics, and Alstom. He also serves as an expert reviewer for ANR and the European Commission. Scientific Leadership and Technology Transfer: Co-founder of Keeneo (acquired by Digital Barriers), Ekinnox, and Neosensys — startups in intelligent video monitoring and business intelligence Reviewer for top-tier journals (PAMI, CVIU, AIJ) and conferences (CVPR, ICCV, AVSS) Contributor to the ARDA workshops on video event ontology He has taught numerical classification at Nice University and video understanding at a Master’s level engineering school. His research program emphasizes generic, scalable systems for behavior modeling and long-term activity mining. Research Projects: Stress ID dataset (ECG and video for stress detection) Toyota Smarthome (Activities of Daily Living) SafEE2 (Homecare for elderly with autonomy loss) Praxis dataset (RGB-D upper-body gestures) GER'HOME, CARETAKER, RATP Project, ETISEO, AVITRACK, CASSIOPEE, ADVISOR, PASSWORDS

Lihi Zelnik-Manorمشاهده پروفایل

استاد

• Haptics
• Robotics
• Computer Vision
• +۴ مورد دیگر

Lihi Zelnik-Manor is a Professor at the Faculty of Electrical and Computer Engineering at the Technion - Israel Institute of Technology . Her research focuses on digitizing the sense of touch, integrating Haptics , Robotics , and Computer Vision to create digital representations of physical properties and develop haptic feedback devices for virtual interactions. Executive Vice President for Innovation and Industry Relations (2023-2026) Vice Dean for Graduate Studies (2022-2023) General Chair: CVPR’21, ECCV’22 Her work spans Neural Architecture Search (NAS) , 3D Reconstruction , and Image Processing , with recent publications on haptic devices (2025), diffusion models (2025), and soft-tissue simulation (2024). She actively contributes to academic leadership through roles in top conferences and community initiatives like the Schmidt Postdoctoral Award steering committee.

Ahmet Tekalpمشاهده پروفایل

استاد

• Digital Image Processing
• Video Compression
• Motion-Compensated Video Filtering
• +۵ مورد دیگر

Ahmet Tekalp is a Professor in the Department of Electrical and Computer Engineering at Koc University's College of Engineering since 2001. He holds dual citizenship in Turkey and the USA, with prior academic roles at the University of Rochester (1986-2005) and research positions at Eastman Kodak (1984-1987) and Rensselaer Polytechnic Institute (1981-1984). He chairs the Electronics and Informatics Group at TUBITAK since 2004 as a part-time position. B.S. (1980) in Electrical Engineering & Mathematics, Bogaziçi University M.S. (1982) and Ph.D. (1984) in Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute His research focuses on digital image and video processing, including video compression, motion-compensated filtering for high-resolution applications, video segmentation, object tracking, content-based video analysis, multi-camera surveillance processing, and digital content protection. He has led numerous European and U.S. grants, including FP7 STREP projects and NSF awards, emphasizing applications in sensor networks, visual databases, and medical imaging. His scholarly work spans diverse areas such as superresolution reconstruction, head gesture animation, 3DTV streaming, and reversible data hiding. He has played pivotal roles in editorial boards, including serving as Editor-in-Chief of Signal Processing: Image Communication, and has contributed to major standards bodies like ISO MPEG and ANSI NCITS. Member, Turkish Academy of Sciences (TUBA) Fellow, IEEE Fulbright Senior Scholarship (1999) TUBITAK Science Award (2004) IEEE Signal Processing Society Distinguished Lecturer (1998) He has led multiple international research collaborations and projects, including European FP6/FP7 networks and NATO programs, with substantial grant funding from NSF, NYSTAR, and industry partners like Eastman Kodak, Xerox, and Siemens.

• Artificial Intelligence
• Pattern Recognition
• Biometrics
• +۶ مورد دیگر

Venu Govindaraju is a SUNY Distinguished Professor of Computer Science and Engineering at the University at Buffalo, where he also serves as Vice President for Research and Economic Development. He founded the Center for Unified Biometrics and Sensors (CUBS) in 2003 and leads the National AI Institute for Exceptional Education, funded by a $20M NSF grant. His research focuses on artificial intelligence, pattern recognition, biometrics, and handwriting analysis, with notable contributions to postal automation, cybersecurity, and early screening for dyslexia/dysgraphia in children. Govindaraju holds IEEE and ACM Fellowships, and was named Person of the Year by the Council of Heritage and Arts of India in 2024. He has advised 47 doctoral students and pioneered foundational AI technologies impacting global industries. Research interests include: AI-driven education tools, handwriting recognition systems, document analysis, and cross-domain biometric technologies. His work bridges AI with humanities through projects like the Marianne Moore Digital Archive. Recent initiatives focus on leveraging AI to address speech-language pathologies and enhance K-12 education accessibility. Leadership roles include co-chair of SUNY’s AI Task Group and advisor to the SUNY-IBM AI Research Alliance. He is a vocal advocate for responsible AI policy, advising lawmakers at White House events and contributing to legislative briefings. His innovations are highlighted in Empire AI, New York’s $400M computing consortium.

• Computer Vision
• Robotics
• Human-Computer Interaction
• +۴ مورد دیگر

Włodzimierz Kasprzak is a Professor at the Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology. His research focuses on computer vision, robotics, human-computer interaction, and machine learning. He has contributed to advancements in human action classification, skeleton-based feature analysis, and multimodal interface design. Research Highlights: Development of lightweight classification models for human actions in video using skeleton-based features. Advances in multi-stream fusion techniques for image and video analysis. Design of embodied agent systems for cybersecurity event visualization and control. Awards and Recognition: 2024: Individual First Class Rector's Award for Scientific Achievements (2022-2023) 2021: Medal of the Commission of National Education 2011: Golden Cross of Merit His work integrates theoretical contributions with practical applications in robotics, surveillance systems, and human-centered technologies.

Raphaël Troncyمشاهده پروفایل

استادیار

• Semantic Data Integration
• Knowledge Graphs
• Semantic Web
• +۳ مورد دیگر

Raphaël Troncy is an Assistant Professor at EURECOM's Data Science Department, specializing in Semantic Web technologies, Knowledge Graphs, and Natural Language Understanding. He teaches courses like 'Human-computer interaction for the Web' and 'Semantic Web technologies.' His research focuses on semantic data integration, knowledge graph applications, and recommender systems. Notable projects include DOREMUS (musical work graph), entity2rec (knowledge graph-based recommendations), and 3cixty (city exploration knowledge bases). He actively contributes to semantic web challenges and conferences, winning multiple awards including the 2018 Best Poster Award at ESWC and 2015 First Prize in the Semantic Web Challenge. Troncy's work spans cultural heritage digitization (e.g., Odeuropa olfactory data modeling), cybersecurity anomaly detection (NORIA-O ontology), and interdisciplinary projects like SILKNOW's silk textile knowledge graph. He leads development of tools like DAGOBAH for semantic table interpretation and KG Explorer for knowledge graph exploration. Education: Not explicitly stated in text Labs/Teams: Active in EURECOM's Data Science group, collaborating on projects involving knowledge graphs, AI, and semantic technologies