جست‌وجوی استاد و پژوهشگر

Thomas Jean-Hughمشاهده پروفایل

استاد

• Acoustic Imaging
• Signal Processing
• UAV Acoustic Localization
• +۷ مورد دیگر

Thomas Jean-Hugh is a Professor and Teacher-Researcher at Le Mans Université , affiliated with the Laboratoire d’Acoustique de l’Université du Mans (LAUM) , a leading research institute in acoustics in France. His role involves both teaching and active research in acoustic imaging, signal processing, and structural acoustics. Education PhD in Acoustics and Signal Processing (exact institution and year not specified in text) Research Interests Thomas Jean-Hugh’s research focuses on acoustic source localization and imaging , particularly through the use of microphone arrays , beamforming , and inverse problem solving . His work spans: Acoustic holography for non-stationary and confined sources UAV acoustic detection and tracking using sparse sensor arrays Vibro-acoustic diagnostics for automotive and naval structures Speech processing in meeting environments using distant microphone arrays Non-destructive testing using acoustic emission and imaging techniques Research Trends His recent publications (2022–2025) reveal a strong focus on machine learning-enhanced acoustic imaging , real-time UAV detection , and robust speech processing . He integrates genetic algorithms , Bayesian regularization , and circular harmonics into acoustic array processing, with applications in autonomous systems , marine biology , and automotive acoustics . Scientific Contributions Over 70 peer-reviewed publications in journals and conferences Active supervision of PhD students and postdocs in acoustics and signal processing Participation in international conferences such as Interspeech , ICSV , GRETSI , and ICA Collaborations & Labs He collaborates extensively within LAUM and with external partners including CNRS , ISCA , and international research groups. His lab work involves experimental acoustics , sensor array design , and real-time signal processing .

Gelareh Naseriمشاهده پروفایل

پژوهشگر ارشد

• Deep Learning
• Music and Audio Generation
• Music Representation Learning
• +۲ مورد دیگر

Gelareh Naseri is a Post-Doctoral Associate at the Center for Data Science (CDS) at New York University, collaborating with Professor Yann LeCun. Her research bridges deep learning and music/audio generation, focusing on self-supervised representation learning via Joint Embedding Predictive Architectures (JEPA). Ph.D. in Music Composition, University of California, Riverside Master’s in Music Composition, University of Art, Iran Bachelor’s in Computer Engineering, Shahid Beheshti University, Iran Her research spans deep learning , self-supervised learning , and music representation learning , with applications in audio synthesis and machine learning . She works within the CDS lab at NYU, contributing to advanced AI research in music contexts.

Sagie Benaimمشاهده پروفایل

استادیار

• computer vision
• machine learning
• computer graphics
• +۴ مورد دیگر

Sagie Benaim is an Assistant Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Previously, he was a postdoctoral researcher at DIKU (Department of Computer Science, University of Copenhagen) working with Professor Serge Belongie and was a member of the Pioneer Center for AI. He completed his PhD at Tel Aviv University in the Deep Learning Lab under the supervision of Professor Lior Wolf. Dr. Benaim's research spans computer vision, machine learning, and computer graphics, with a particular emphasis on generative models, neural signal representations, and inverse graphics. His work explores how disentangled representations can be leveraged to better understand and manipulate visual content. He has made significant contributions to 3D scene manipulation, image-to-video generation, and neural rendering techniques. His recent publications reveal a strong trajectory toward advancing generative AI capabilities, particularly in 3D understanding, multimodal generation, and video synthesis. His research consistently bridges theoretical foundations with practical applications, demonstrating innovation in neural representation manipulation for both creative and analytical purposes. Dr. Benaim is actively seeking excellent students and postdocs to join his research group, indicating his commitment to mentoring the next generation of researchers in computer vision and machine learning. His position at the Hebrew University of Jerusalem places him within a vibrant academic community focused on advancing the frontiers of computer science.

• Signal Analysis
• Image Processing
• Video Analysis
• +۶ مورد دیگر

Luigi Celona is an Assistant Professor in the Department of Informatics, Systems and Communication (DISCo) at the University of Milano-Bicocca, Italy. He is affiliated with the Intelligent Sensing Laboratory under Prof. Paolo Napoletano and serves as Associate Editor for Elsevier Neurocomputing and Springer Image, Signal and Video Processing journals. Education: BSc in Computer Science from University of Messina; MSc and PhD in Computer Science from University of Milano-Bicocca (supervised by Prof. Raimondo Schettini and Prof. Paolo Napoletano). Research Interests encompass signal/image/video analysis and understanding, with a focus on perceptual metrics for quality, aesthetics, and memorability. His work includes: Visual quality assessment and enhancement Facial analysis and monitoring Speaker recognition and emotional state analysis Applications of computer vision and machine learning Technical expertise spans computer vision , image dehazing , pattern recognition , and deep learning methodologies.

Luxi Zhaoمشاهده پروفایل

پژوهشگر ارشد

• Time-Sensitive Networking
• Network Calculus
• Real-Time Systems
• +۳ مورد دیگر

Luxi Zhao is a Research Fellow at Technische Universität München's Embedded Systems and Internet of Things department, specializing in Time-Sensitive Networking (TSN) and real-time network calculus. Working under the supervision of Prof. Sebastian Steinhorst, Zhao contributes to projects like ReMiX and 6G-Life, focusing on security, performance analysis, and configuration optimization of deterministic networks. Research Focus: Worst-case latency analysis in TSN networks Runtime configuration and reconfiguration problems Hybrid scheduling of processing and communication Network calculus modeling for heterogeneous systems Security challenges in industrial IoT and autonomous systems Interoperability of IoT systems in Industry 4.0 Teaching Activities: Secure Autonomous Systems (2025) Software Architecture for Distributed Embedded Systems (2025) IoT Security (2025) IoT Remote Lab practical courses across multiple semesters Advanced Seminar series (2020-2025)

Jiahong Zhaoمشاهده پروفایل

مدرس

• Signal Processing
• Acoustics
• Spatial Audio
• +۲ مورد دیگر

Dr Jiahong Zhao is a Lecturer in Acoustics at the University of Southampton. His research focuses on signal processing and audio technologies. Research interests include microphone array signal processing, spatial audio, machine learning, and computer vision. Current supervisor for PhD student Yaoxiang Yu in Engineering and Environment. Contact: Jiahong.Zhao@soton.ac.uk

• Internet of Things (IoT)
• Radio-Frequency Identification (RFID)
• Mobile Health (mHealth)
• +۳ مورد دیگر

Elgar Fleisch is a Professor at the University of Zurich, specializing in interdisciplinary research at the intersection of Internet of Things (IoT), Radio-Frequency Identification (RFID), Mobile Health (mHealth), and Energy Informatics. His work bridges theoretical and applied domains, focusing on real-time data analytics, wearable technologies, and behavioral interventions. Key research areas include sensor-based health monitoring systems Development of transparent computing frameworks for AI/data collection Consumer behavior analysis through IoT-enabled retail and energy systems Machine learning applications for circadian rhythm analysis and respiratory disease detection Recent publications demonstrate expertise in federated learning for cough classification, circadian rhythm biomarkers, and driver behavior analytics. While no specific awards are listed in this dataset, his collaborations with institutions like ETH Zurich and University of St. Gallen indicate broad academic engagement.

Amin Karamiمشاهده پروفایل

دانشیار

• Artificial Intelligence and Data Science
• Big Data Technologies
• Blockchain and Cryptocurrencies
• +۴ مورد دیگر

Dr Amin Karami is an Associate Professor in the School of Computer Science and Digital Technologies (CDT) at the University of East London, within the School of Architecture, Computing and Engineering. He serves as course leader for MSc Big Data Technologies and leads postgraduate programs, having secured £1.23 million in funding from the Office for Students to develop inclusive AI and Data Science courses for non-STEM and far-STEM graduates. His research spans Artificial Intelligence, Big Data Analytics, Blockchain, and Optimization, with focus on Industry 5.0 applications. Current work addresses federated learning heterogeneity, smart contract security, healthcare fraud detection, and ethical AI implementation. He develops cloud-based platforms for large-scale data processing and computational intelligence solutions for real-world industry challenges. Recent publications demonstrate strong trends in federated learning techniques, blockchain vulnerability mitigation, and big data applications across healthcare, finance, and social media. His work consistently bridges academic research with industry needs through partnerships with Multiverse and Cambridge Spark, emphasizing practical solutions for credit risk assessment, satellite telemetry, and personalized marketing. Scientific recognition includes: UEL Vice-Chancellor & President Impact & Innovation Award for Industry 4.0 readiness (2023) Fellow of the Higher Education Academy (FHEA) Dr Karami actively supervises UG/PG/PhD students while leading curriculum innovation through externally funded projects. His Chainlink Bootcamp initiative connects academia with industry practitioners, and he serves as external examiner and conference program chair. Significant grant achievements include developing diversity-focused STEM pathways that enhance graduate employability through industry-aligned training in AI and Data Science.

Saeed Sharifمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Innovative Tele-Health
• Medical Technology
• +۹ مورد دیگر

Dr Saeed Sharif is an Associate Professor in the Computer Science department at the School of Architecture, Computing and Engineering , University of East London . He leads the Intelligent Technologies Research Group and serves as Course Leader for MSc Computer Science programs. With a PhD in artificial intelligence from Brunel University London , his work bridges Artificial Intelligence , Medical Technology , and Smart Infrastructure . His research interests span Medical Image Analysis , Intelligent Diagnosis Systems , Big Data Analysis , and IoT Security . Collaborations with NHS Trusts and global institutions focus on improving healthcare systems through machine learning and biomedical signal processing. 2018-2022: £268,000 KTP project with Innovate UK Multiple Research Internship Schemes at UEL (2017-2021) European Research Centre collaboration (2015) As a technical committee member for conferences like IEEE CIT and a journal Guest Editor , he drives academic discourse. He has supervised numerous PhD students and served on examination panels, while maintaining 53+ publications in venues including IEEE Access , Applied Soft Computing , and Computer Methods and Programs in Biomedicine .

• Machine Learning
• Field-Programmable Gate Arrays (FPGA)
• Network Traffic Classification
• +۵ مورد دیگر

Prof. Dr. Oğuzhan ERDEM is a faculty member at Trakya University Faculty of Engineering , Department of Electrical and Electronics Engineering since 2023. He earned his PhD in Electrical and Electronics Engineering from Middle East Technical University in 2011 and served at various academic roles across 22 years, including Department Head since 2021. PhD: Middle East Technical University (2011) Visiting Scholar: University of Southern California (2010-2011) His research focuses on machine learning applications in biomedical diagnostics and FPGA-based network processing . Recent publications explore Parkinson's disease detection through keystroke analysis, cough sound diagnostics, and hardware-accelerated network traffic classification. Key article trends show: 2023-2025: Deep learning for medical diagnostics 2016-2020: Hardware-optimized network processing 2010-2015: FPGA-based IP lookup architectures Awards: Middle East Technical University 2011 Doctoral Thesis of the Year He has supervised 6 graduate theses and led 2 major projects including Early Detection of Parkinson's Disease Using Multimodal Signals (2021). Patent co-inventor in systolic array architectures.

Thierry Artièresمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۶ مورد دیگر

Thierry Artières is a University Professor at Aix-Marseille University, primarily affiliated with École Centrale Marseille (ECM), where he holds multiple leadership positions including Head of the Computer Science teaching unit, Head of the IAAA course of the Computer Science Master's degree, and Head of the IAM course of the 3rd year Computer Science option. He is a key member of the QARMA (Machine Learning) research team within the LIS (Laboratoire d'Informatique et Systèmes) and collaborates with several research institutes including the READ laboratory, Institut de Neurosciences de la Timone (INT), and the ILCB (Institute of Language, Communication and the Brain). His research interests span Machine Learning, Deep Learning, and Artificial Intelligence with applications to neuroscience, medical imaging, and computational biology. His work focuses on understanding brain representations of voice and sound, optimizing MRI acquisition through deep learning, and developing novel machine learning techniques for multi-label classification and generative modeling. He has supervised numerous PhD students including Loris Berthelot, Hamed Benazha, Malek Senoussi, Swetali Nimje, and Charly Lamothe. His recent publications reveal a strong focus on applying deep learning to neuroscience problems, particularly in understanding how the brain processes sound and voice. His work combines theoretical machine learning advances with practical applications in medical imaging and cognitive neuroscience, often through collaborations between computer science and neuroscience laboratories. His research demonstrates a consistent trend toward interdisciplinary work that bridges AI with biological and medical domains. Member of QARMA Machine Learning team at LIS Collaborator with Institut de Neurosciences de la Timone Involved with ILCB Institute (Institute of Language, Communication and the Brain) Supervisor of multiple PhD students and Master's interns Regularly posts about internship opportunities in Machine Learning and AI Professor Artières actively mentors students through PhD positions, Master's internships, and engineering student projects. He has secured funding for multiple research projects, including ANR-funded collaborations with neuroscience institutes. His lab regularly offers 5-6 month internships on cutting-edge topics in machine learning, and he has facilitated numerous research opportunities for students interested in AI and data science careers. He also contributes to understanding the French job market for AI and data science professionals.

• Computational Medicine
• Speech and Language Processing
• Health Assessment Systems
• +۴ مورد دیگر

Mary Pietrowicz is a Teaching Assistant Professor in the Department of Biomedical and Translational Sciences at the University of Illinois and a Senior Research Scientist at the Illinois Applied Research Institute. Her interdisciplinary research integrates computational methods with health sciences, focusing on acoustic and behavioral signal analysis for conditions like ALS, schizophrenia, and depression. She earned a Ph.D. in Computer Science from the University of Illinois, an M.S. in Computer Science from Florida Atlantic University, and a B.S. in Electrical Engineering from Purdue University. Her research explores computational modeling of human expression through speech, movement, and creativity to develop scalable health assessment tools. Key areas include: Machine learning for disease state detection via vocal and kinetic biomarkers Deployable telehealth systems using ubiquitous devices (phones, sensors) Cross-disciplinary applications in neurology, psychiatry, and addiction science Cultural computing through interactive art and sonic installations Her publications reveal a consistent focus on acoustic signal processing applied to neurodegenerative/psychiatric disorders, with innovations in laughter analysis, verbal fluency testing, and real-time health monitoring. Trends include machine learning refinement for clinical diagnostics and multimodal data fusion. She contributes to patented technologies in sensor security, geolocation systems, and speech characterization. At the Illinois Applied Research Institute, she develops deployable health-assessment frameworks and collaborates on interdisciplinary projects bridging engineering, medicine, and digital art.

Chenyou Fanمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Natural Language Understanding
• +۳ مورد دیگر

Chenyou Fan is an Associate Professor at the School of Artificial Intelligence , South China Normal University . He holds a Ph.D. in Computer Science from Indiana University and a B.S. in Computer Science from Nanjing University . His research focuses on: Computer Vision Machine Learning Natural Language Understanding Modeling unstructured data (images, texts) across distributed devices Agent-based AI systems Recent publications span conferences like IJCAI , ICASSP , and journals like TPAMI , emphasizing multimodal attention models and heterogeneous learning frameworks. Notably, he received the 2023 Guangdong Youth Science and Technology Award from the Computer Academy of Guangdong. He actively contributes to open-source projects and has a vibrant GitHub presence with 91 recent contributions across repositories like HME-VideoQA (implementing a heterogeneous memory-enhanced multimodal attention model) and SCNU_DL_Class_2024 .

KRASEN KIROV ANGELOVمشاهده پروفایل

استاد

• Wireless Communication Systems
• 5G Networks
• Internet of Things (IoT)
• +۷ مورد دیگر

Krassen Kirov Angelov is a full professor at the Department of Communication Equipment and Technologies within the Faculty of Electrical Engineering and Electronics at Technical University - Gabrovo . He has led and participated in 15 research projects, focusing on wireless communication platforms, IoT systems, and optical network optimization. Head, Department of Communication Equipment and Technologies Project Manager for 5G/IoT research (2022) Co-author of 3 textbooks on communication circuits Research Interests include: 5G network design and optimization IoT communication protocols Optical network modeling Secure data transmission Satellite broadcasting systems Smart infrastructure development His recent publications (2021-2025) demonstrate expertise in: Chaotic encryption techniques Neural network applications LoRaWAN coverage planning DVB-S2 signal transmission Passive optical network modeling Radio frequency optimization PhD Students : Plamen Tashev (Radio Channel Monitoring) Mikhail Angelakis (Wireless Resource Management) Victor Krustev (Security Identification) Ivan Zlatev (Network Resource Allocation) Evelina Vaseva (Beekeeping Monitoring)

• Music Information Retrieval
• Digital Musicology
• Computational Music Similarity
• +۳ مورد دیگر

Daniel Wolff is a researcher affiliated with City University London , specializing in computational music similarity modeling and big data applications in musicology. His work bridges machine learning, audio signal processing, and music theory to analyze large-scale music collections. Doctoral thesis (2014): Spot the Odd Song Out: Similarity Model Adaptation and Analysis using Relative Human Ratings Key research focus: Adaptive similarity metrics, chord progression mining, and dataset automation Active in conferences like ISMIR, ACM Digital Libraries, and Interdisciplinary Musicology workshops Research Trends: His publications emphasize: Big data infrastructure for musicological research Machine learning techniques for audio feature extraction Interactive visualization of harmonic patterns User-driven similarity modeling via comparative ratings Technical Contributions: Developed uncertainty sampling methods for dataset curation, parallel computing approaches for chord analysis, and reproducible frameworks for music similarity evaluation.