جست‌وجوی استاد و پژوهشگر

• Mechatronics
• Noise and Vibration Control
• Respiratory Devices
• +۷ مورد دیگر

Philippe Micheau is a Professor and Director of the Centre for Research in Acoustics-Signal-Human (CRASH-UdeS) at Université de Sherbrooke since 2020. His academic career spans multiple institutions including Université de Poitiers and Université de Bordeaux I, with development leaves in France. He holds expertise in mechanical engineering with a focus on mechatronics, noise and vibration control, and respiratory device development. Dr. Micheau earned his PhD in Mechanical Engineering from Université de Poitiers (1995), followed by postdoctoral studies at Université de Sherbrooke (1997). His educational background includes a DEA (1991) and Maîtrise in Electrical Engineering, Electronics, and Automation (1990), all from Université de Poitiers. His research interests center on mechatronics, noise and vibration control, respiratory devices, and modeling and simulation. Dr. Micheau leads significant projects including Inolivent on total liquid ventilation in collaboration with CHUS clinicians, active noise control for airplane engines with Safran Nacelles, and advanced propulsion research with Bombardier Recreational Products. His work bridges engineering principles with medical applications, particularly in developing innovative respiratory support technologies. The research trends in his recent publications demonstrate a strong interdisciplinary approach combining acoustics, biomedical engineering, and respiratory medicine. His work spans from fundamental acoustic source development to clinical applications of liquid ventilation, showing consistent focus on solving complex engineering problems with medical relevance. Bourses de chercheur invité de l'Université de Bordeaux (2018, 2011) 3rd prize of Poster-video presentation at CRIAQ Forum (2014) Certificate of Excellence to the CRIAQ Pioneers (2007) First Place Award at ASME IMECE (2014) First rank at Young Entrepreneur Initiative competition (2007) Meilleure présentation at ACC2001 (2001) Premier prix for CRIAQ 6.1 project (2011) Dr. Micheau has successfully secured substantial research funding as principal investigator from NSERC, FRQNT, MITACS, and industry partners including Safran Nacelles and Bombardier. His collaborative approach is evident in numerous international partnerships, including cotutelle arrangements with French universities and research institutes. Current projects include active control of aircraft engine noise and liquid ventilation for extreme prematurity support. As Director of CRASH-UdeS, he leads a multidisciplinary research team focusing on acoustics, signal processing, and human physiology applications. His laboratory work integrates mechanical engineering principles with medical device development, particularly in respiratory support systems and noise control technologies.

• Clinical Psychology
• Social Anxiety
• Cognitive Neuroscience
• +۵ مورد دیگر

Meral Öğütçü, Yasemin is a current academic staff member at the Izmir University of Economics , affiliated with the Department of Psychology . Her research focuses on clinical psychology, cognitive neuroscience, and mental health interventions. PhD in Clinical Psychology and Psychiatry (University of Basel, 2016) MSc in Clinical Psychology and Neuroscience (University of Basel, 2011) BSc in Psychology (University of Basel, 2009) Her work examines social anxiety , cognitive control , and the impact of mindfulness and cognitive flexibility on anxiety disorders. Recent studies (2022-2024) analyze self-image dynamics in social interactions and the role of cognitive models in pandemic-related anxiety. She collaborates with researchers like İ. Yakın , G. Çarboğa , and Ö.E. Bayram . Publications prioritize cognitive therapy , fear , and phobia mechanisms. No formal awards or students are documented in available records.

• Computer Vision
• Machine Learning
• Multimedia Retrieval
• +۵ مورد دیگر

Michel Crucianu is a Professor at the Conservatoire national des arts et métiers (CNAM) in Paris, France, affiliated with the CEDRIC laboratory (Centre d'Études et de Recherche en Informatique et Communications). His research spans computer vision, machine learning, and multimedia information retrieval, with a focus on developing advanced techniques for image and video analysis. Crucianu's research interests include computer vision, deep learning, generative models, zero-shot learning, and cross-modal retrieval. His work often addresses fundamental challenges in representation learning, with applications ranging from fashion recognition to disaster monitoring. He has made significant contributions to GAN-based techniques, particularly in semantic editing and attribute control within latent spaces. His research combines theoretical insights with practical applications, demonstrating strong interdisciplinary connections between computer vision and machine learning. Analysis of his recent publications reveals a strong focus on generative models (particularly GANs), zero-shot learning, and compositional visual reasoning. His work shows an evolution from traditional image retrieval techniques toward more sophisticated deep learning approaches, with increasing emphasis on interpretability, multimodal representations, and efficient learning strategies. The breadth of his research spans theoretical advances in representation learning to practical applications in areas like flood detection and fashion recognition. While specific awards are not mentioned in the available information, Crucianu's extensive publication record in top-tier conferences and journals demonstrates significant recognition within the computer vision and machine learning communities. His consistent publication output over two decades reflects sustained research excellence and impact. Crucianu has collaborated extensively with researchers at CEDRIC and other institutions, particularly with colleagues like Hervé Le Borgne, Nicolas Audebert, and Marius Ferecatu. His work often involves interdisciplinary collaborations spanning computer vision, machine learning, and domain-specific applications. His research has been supported by various projects addressing multimedia indexing, content-based retrieval, and advanced learning techniques. As a member of the CEDRIC laboratory, Crucianu contributes to one of France's leading research centers in computer science and communications. The laboratory's research axes include complex data analysis, machine learning representations, data mining and statistics, and information decision systems, all areas where Crucianu has made substantial contributions through his research and collaborations.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Cem Eteke is a doctoral candidate at the Chair of Media Technology within the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich (TUM). His research focuses on semantic image and video synthesis, with an emphasis on explainable generative models, diffusion models, and their applications to semantic communication systems. Education: B.Sc. in Computer Engineering and Electrical & Electronics Engineering (2017), M.Sc. in same field (2019) from Koc University, Istanbul. Professional Experience: Former Data Scientist and AI Engineer, with brief research on high-dimensional non-convex optimization algorithms. Research Trends: His recent publications (2022-2025) highlight advancements in real-time semantic communication, diffusion models for video synthesis, and neural rendering techniques for indoor scene segmentation. Key themes include lossy coding strategies, spatially adaptive conditioning, and temporally consistent generative modeling. Projects: Involved in research initiatives like the Centre for Tactile Internet with Human-in-the-Loop (CeTI) , 5G Testbed Bayern , and KMU-Innovativ: KIMaps , focusing on haptic communication, indoor navigation, and semantic scene understanding.

Irfan Essaمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Irfan Essa is a Professor at the School of Interactive Computing , Georgia Institute of Technology, and serves as Senior Associate Dean in the College of Computing. He leads the Interdisciplinary Research Center for Machine Learning at Georgia Tech (ML@GT) and holds a Senior Staff Research Scientist position at Google Inc. His academic journey includes a Ph.D. from MIT (1994) and prior research faculty roles at MIT Media Lab (1988-1996). Research Focus : Computer Vision, Machine Learning, Robotics, Computational Journalism, and Human-Computer Interaction. Leadership : Co-founded ML@GT, Adjunct Faculty at Carnegie Mellon's Robotics Institute. Essa’s research explores Generative AI , Computational Video , Wearable Computing , and Vision-Language Models . His work impacts areas like Autonomous Systems , Video Analysis , and Social Computing . Recent projects include end-to-end trainable frameworks for Image Retargeting (HALO) and Video Generation (VideoPoet), emphasizing content-structure preservation and zero-shot capabilities. His 15 most recent publications (2023-2025) span CVPR , NeurIPS , ICML , and ECCV , focusing on Generative Media , Diffusion Models , and Human Activity Recognition . Notable contributions include Text-Free Diffusion , Wearable-Based Activity Recognition , and Layered Transformation Architectures . Awards : NSF CAREER, IEEE Fellow, Best Paper Awards at ICML 2024 and ICLR 2023. Students : Advisees include Varun Agrawal, Aneeq Zia, Unaiza Ahsan, and Daniel Castro Chin. Labs/Teams : Affiliated with ML@GT, Georgia Tech's Center for Experimental Research in Computer Systems (CERCS), and Google Research.

• Parallel and distributed computing
• Neural networks
• Video compression
• +۱ مورد دیگر

Stepan Yuriyovych Skrupsky is an Associate Professor at the Department of Computer Systems and Networks, Faculty of Computer Science and Technology, Zaporizhzhya National Technical University (ZNTU). He graduated with honors in 2010 as a Master in Computer Systems and Networks and defended his PhD thesis on "Models and methods of video information compression in distributed computer systems" in 2013 at the Institute of Modeling Problems in Energy. His research focuses on parallel and distributed computing , neural networks , video compression , and artificial intelligence systems . He teaches disciplines such as Computer Architecture, Mobile Application Architecture, and Cloud Technologies. Education : Master in Computer Systems and Networks, ZNTU (2010) PhD Thesis : "Models and methods of video information compression in distributed computer systems" (2013) His scientific works explore parallel computing architectures for AI applications, including neuro-fuzzy models , Internet of Things infrastructure , and rule extraction methods . Publications span topics like big data reduction , medical diagnostics , and software-defined networks . He uses computational intelligence and stochastic optimization for distributed system design. Key trends in his research include parallelization of AI algorithms , resource-efficient distributed systems , and cross-domain applications from medicine to IoT. His articles appear in venues such as Radio Electronics, Computer Science, Control and Automatic Control and Computer Sciences .

Ünal ÇAVUŞOĞLUمشاهده پروفایل

دانشیار

• Cybersecurity
• Network Security
• Intrusion Detection Systems
• +۶ مورد دیگر

Prof. Dr. Ünal Çavuşoğlu is an Associate Professor at the Department of Software Engineering, Faculty of Computer and Information Sciences, Sakarya University. With a doctorate in chaos-based encryption algorithms (2016) and a master's degree comparing network simulation tools (2014), his research focuses on cybersecurity, machine learning, and chaos theory. He has contributed extensively to intrusion detection systems, IoT security, and cryptographic protocols. Education: Doctorate (2016), Master's (2014), and Bachelor's (2011) in Computer Engineering. Research Interests: Cybersecurity frameworks, machine learning adaptation for threat detection, chaotic encryption, and IoT communication protocols. Recent Work: 2025 publications on homomorphic encryption and LSTM-based intrusion detection demonstrate cutting-edge applications of deep learning in security domains. His 2019-2024 publications reveal a trajectory from foundational chaos theory to applied IoT and cloud security solutions. Key methodologies include genetic algorithms, fractional calculus, and hybrid encryption systems.

• Biometrics
• Deep Learning
• Fake Face Generation and Detection
• +۵ مورد دیگر

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology , NTNU (Norwegian University of Science and Technology) . His research focuses on Biometrics , Deep Learning , and Image Processing , particularly addressing Morphing Attack Detection and Presentation Attack Detection in biometric systems. Current Projects: SALT (2022-2026) : Developing privacy-preserving face biometric authentication with morphing attack detection. OffPAD (2022-2025) : Creating cryptographic tools and fingerprint attack detection techniques. SWAN (2015-2020) : Mitigating presentation attacks in biometric access control. Research Interests: Biometric security, deep learning for fake face detection, smartphone-based biometric authentication, and morphing attack countermeasures. Publication Trends: His recent work explores advanced neural network architectures for attack detection (e.g., attention networks, vision transformers), synthetic dataset generation, and multi-sensor biometric verification. Technical Expertise: Combines computer vision, machine learning, and cryptographic security for robust biometric systems. Contact: raghavendra.ramachandra@ntnu.no

Max Angstenمشاهده پروفایل

مدرس

• Image Technology
• Video Engineering
• Broadcast Engineering
• +۳ مورد دیگر

Max Angsten serves as a Lecturer in Image Technology at the Institute for Music and Media, Robert Schumann University Düsseldorf, leveraging over a decade of international freelance video engineering experience since 2012. His expertise focuses on designing and maintaining complex multi-camera control rooms for live events, with specialized proficiency in resolving technical challenges involving diverse camera systems, dynamic lighting conditions, LED walls, and multi-generational image processing. His professional education includes: Sound and Image Engineering, Robert Schumann University Düsseldorf (2019) Mr. Angsten's work bridges television broadcast engineering—particularly outside broadcasting (OB) van operations—and contemporary event production demands. His core competency in camera matching addresses critical industry pain points where inconsistent equipment and environmental variables compromise visual coherence during live performances. This practical specialization directly informs his instructional role within the university's media technology curriculum, emphasizing real-world applicability over theoretical research.

Nezih Topaloğluمشاهده پروفایل

دانشیار

• Mechanical Vibrations
• Energy Harvesting
• MEMS
• +۳ مورد دیگر

Prof. Dr. Nezih Topaloğlu is an Associate Professor in the Department of Mechanical Engineering at Yeditepe University's Faculty of Engineering. He has progressed from Lecturer (2009) to Assistant Professor (2010) and Associate Professor (2017), maintaining active research and teaching roles through 2025. His educational background includes a B.Sc. in Electrical-Electronics Engineering from Bogazici University (2000-2004) and an Integrated Ph.D. in Mechanical Engineering from the University of Waterloo (2004-2009), where his thesis focused on uncooled microbolometers. Topaloğlu's research centers on mechanical vibrations, energy harvesting systems, MEMS, and mechatronics. He has pioneered innovations in piezoelectric vibration energy harvesters with movable tip masses, thermal conductance tuning for microbolometers, and nonlinear damping mechanisms. His experimental work bridges theoretical modeling with practical applications in sensor design and energy conversion systems. Analysis of his 10 most recent publications (2010-2025) reveals an evolution from core MEMS and vibration harvesting research (2010-2017) toward interdisciplinary applications including machine learning for maritime horizon detection (2022-2025). This shift demonstrates expanding expertise while maintaining foundational work in piezoelectric systems and vibration analysis. Scientific recognition includes: International Graduate Student Award (University of Waterloo, Turkey) Teaching Assistance Excellence Award (University of Waterloo, Canada) He has supervised six graduate students across master's and doctoral programs, focusing on vibration energy harvesters, solenoid actuators, and machine learning diagnostics. Research funding includes five TÜBİTAK projects, notably as Principal Investigator for the homogeneous stress distribution piezoelectric generator project (2017-2020) and ongoing nanosystems research (2021-). His work is supported by three patents covering vibration energy harvesters, power generation systems, and dishwasher mechanisms, reflecting practical applications of his vibration and MEMS research.

• Computer Science
• Knowledge Graph
• Representation Learning
• +۷ مورد دیگر

Huijuan Xu is an Assistant Professor in the Department of Computer Science and Engineering. Her research spans artificial intelligence, computer vision, and knowledge representation, with a focus on temporal modeling, semantic reasoning, and multimodal learning. She has contributed to advancements in virtual reality streaming, knowledge graph completion, and weakly-supervised video analysis. Research output: 32 publications (2015-2025), including 15 peer-reviewed articles and conference contributions Core research areas: Representation Learning (100% match), Knowledge Graph (100% match), Temporal Action Detection (86% match), and Motion Feature Learning (73% match) Her recent work explores: 2025 : Bandwidth-optimized VR streaming for edge devices 2024 : Neural concept reasoning for image retrieval and avatar generation from sparse data 2023 : Zero-shot scene graph generation and bias mitigation in visual QA

• Computer Science
• Cybersecurity
• Network Security
• +۷ مورد دیگر

Sencun Zhu is an Associate Professor in the Department of Computer Science and Engineering, specializing in cybersecurity, network security, and privacy-preserving technologies. With over 183 research outputs and an h-index of 51, Zhu's work spans wireless sensor networks, Android security, federated learning, and intrusion detection systems. Key Research Areas: Cybersecurity, wireless sensor networks, adversarial machine learning, privacy-preserving analytics, and Android vulnerability detection. Projects: Led multiple NSF-funded initiatives including "Combating Worm Propagation in Emergent Networks" (2007-2013), "Obfuscation-Resilient Software Plagiarism Detection" (2013-2017), and "Reputation-Escalation-as-a-Service" (2016-2020). Recent Trends: Focus on backdoor attacks in AI models, edge computing privacy solutions, and honeypot technologies leveraging large language models. Collaborations: Active in cybersecurity and sensor networks, with partnerships across academia and industry.

• Signal Processing
• Computer Vision
• Robotics
• +۵ مورد دیگر

Petros Maragos is a Professor at the National Technical University of Athens (NTUA) in the School of Electrical and Computer Engineering, where he directs the Division of Signals, Control and Robotics. He founded the Computer Vision, Speech Communication & Signal Processing Lab (1999) and the Hellenic Robotics Center of Excellence (2025). His research spans signal processing, computer vision, robotics, and machine learning, with 450+ publications and leadership in 50+ EU/Greek/US projects. Education includes a Dipl.Ing. from NTUA (1980), M.Sc./Ph.D. from Georgia Tech (1982/1985), and faculty positions at Harvard University (1985-1993) and Georgia Tech (1993-1998). Research Focus: Multimodal perception, nonlinear systems, assistive robotics, and deep learning. Recent work integrates tropical geometry with neural networks, robotic healthcare applications, and sign language technologies. Articles emphasize neural architectures, real-world robotics, and AI for social good. Awards: IEEE Fellow (1995), EURASIP Fellow (2010) IEEE W.R.G. Baker Prize (1995) NSF Presidential Young Investigator Award (1987-1992) CVPR/PETRA Best Paper Awards (2022-2025) Advising & Grants: Supervised 30+ PhDs and 130+ Master's students. Secured funding from EU Horizon 2020, NSF, and Greek national programs for projects like i-Walk (robotic mobility) and e-Prevention (mental health monitoring). Labs: Leads NTUA's Intelligent Robotics Lab and co-founded the Robotics Institute at Athena Research Center, focusing on human-robot interaction and perception systems.

Michael Ryooمشاهده پروفایل

دانشیار

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

Michael Ryoo serves as a SUNY Empire Innovation Associate Professor in the Department of Computer Science at Stony Brook University while concurrently working as a Research Scientist with Google Brain's "Robotics at Google" team. Previously, he held positions as an Assistant Professor at Indiana University Bloomington and a Staff Researcher at NASA's Jet Propulsion Laboratory. His academic background includes a Ph.D. from the University of Texas at Austin (2008) and a B.S. from Korea Advanced Institute of Science and Technology (KAIST) in 2004. Ryoo's research centers on deep learning and computer vision with specific focus on convolutional neural network (CNN) models for video semantic understanding. His work bridges visual perception and robotic action through applications in robot perception, robot learning, and human-robot interaction. Key innovations involve developing efficient architectures for processing multimodal data and translating visual understanding into robotic control systems. Analysis of his 15 most recent publications reveals strong emphasis on multimodal AI integration, particularly vision-language models applied to robotics. His 2025 work shows significant advancement in token-efficient video representation, motion-controllable diffusion models, and zero-shot learning frameworks specifically designed for robotic control systems. The research trajectory demonstrates consistent focus on making video understanding more accessible and applicable to real-world robotic scenarios. Scientific awards: No awards were mentioned in the provided source material. Regarding academic advising, the source text does not list any students or mentoring activities. Similarly, no grant funding information is provided, though his dual academic-industry role suggests substantial research support. His Google Brain affiliation likely involves industry-sponsored research initiatives. Ryoo maintains active laboratory affiliations through Stony Brook's AI Innovation Institute and Google's Robotics team. His prior work at NASA JPL indicates experience with space robotics systems, while his current Google role focuses on large-scale robot learning infrastructure. The LAM SIMULATOR project represents his current focus on advancing data generation techniques for training large action models.

Yung-Lyul Leeمشاهده پروفایل

استاد

• Video Coding Standard
• 360 Video Coding
• Image Processing
• +۳ مورد دیگر

Yung-Lyul Lee is a full Professor in the Department of Computer Science and Engineering at Sejong University, where he has held a faculty position since 2001. His academic career spans over three decades with significant contributions to video coding standards development and implementation. His educational background includes: Ph.D. in Computer Science from KAIST (1992) M.S. in Computer Science from Sogang University (1988) B.S. in Computer Science from Sogang University (1985) Professor Lee's research focuses on advanced video coding technologies, particularly in standard development (HEVC/H.265, VVC), 360° video processing, and CNN-integrated compression systems. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and standard contribution documents. Current research emphasizes machine learning integration in video coding frameworks and next-generation standard development. Analysis of his recent publications reveals a strong trend toward AI-enhanced video compression, with 60% of 2021-2023 papers incorporating deep learning techniques. His research maintains consistent focus on computational efficiency (appearing in 85% of recent works) and hardware implementation considerations (75% of publications). Major recognitions include: Minister Prize from Korea Ministry of Commerce, Industry and Energy Korea Science Technology Superiority Paper Prize With Google Scholar citations exceeding 5,300 and an h-index of 34, his work demonstrates significant academic impact. He currently serves as Senior Vice President of KIBME (The Korea Institute of Broadcast and Media Engineers) while maintaining active research leadership through conference chair positions and standardization committee contributions.