جست‌وجوی استاد و پژوهشگر

Stefan Goetzeمشاهده پروفایل

استاد مهمان

• Machine Learning
• Signal Processing
• Speech Recognition
• +۳ مورد دیگر

Stefan Goetze is a Visiting Professor in the Department of Computer Science at the University of Sheffield, affiliated with the Speech and Hearing (SpandH) research group. He previously held a Senior Lecturer position from 2020 to 2025. He earned his Dipl.-Ing. (2004) and Dr.-Ing. (2013) in Electrical/Communication Engineering from the University of Bremen, Germany. Prior to his academic role at Sheffield, he led departments at the Fraunhofer-Institute for Digital Media Technology IDMT in Oldenburg, Germany, including the "Audio System Technology for Audiology and Assistive Systems" and "Automatic Speech Recognition" groups. His research focuses on machine learning, signal processing, and assistive technologies, particularly in speech enhancement, dereverberation, and hearing aid systems. He supervises multiple PhD students and has contributed to grants such as the "Participatory co-design of a platform for collecting atypical speech data" (2022). His work spans acoustic event detection, audiovisual processing, and medical applications of speech technology. Key research interests include robust speech recognition in reverberant environments, noise reduction, and the application of deep learning to audio challenges. Notable publications address speech separation models, metric-driven enhancement, and active learning for sound classification. Collaborations include projects on assistive living technologies and healthcare communication systems.

Simon Coltonمشاهده پروفایل

استاد

• Computational Creativity
• Artificial Intelligence
• Game Design
• +۴ مورد دیگر

Simon Colton is a Professor of Computational Creativity, Games, and Artificial Intelligence at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on AI-driven creativity in domains like music, game design, and visual arts, emphasizing generative techniques and philosophical aspects of creative systems. He leads projects in automated composition, procedural content generation, and interdisciplinary AI applications. Education: Extensive background in AI and computational creativity, with a focus on generative systems. Research Interests: Computational Creativity, Generative AI, Music Generation, Game Design Automation, and Neuro-Symbolic Systems. His work explores how AI can autonomously create artistic content, including music, games, and visual art, with notable contributions to frameworks like ANGELINA for game design and The Painting Fool for automated art. Recent projects include AI-driven music composition and tools for creative support (e.g., Gamika, Danesh). Key grants include the £3M EPSRC-funded IGGI2 Centre for Doctoral Training, focusing on Intelligent Games and Game Intelligence. His lab, the Centre for Multimodal AI, advances research in cross-modal AI creativity.

• Artificial Intelligence
• Games
• Future of AI and Humanity
• +۵ مورد دیگر

Professor Peter Cowling is a faculty member at Queen Mary University of London, holding the position of Professor of Artificial Intelligence within the School of Electronic Engineering and Computer Science. He is affiliated with the Centre for Multimodal AI, focusing on research areas including AI in games, esports analytics, Monte Carlo Tree Search (MCTS), and applications in real-world problems like maintenance scheduling and business model analysis. His work spans theoretical advancements in AI algorithms and practical implementations in gaming, healthcare, and education. Notable grants include leadership in the IGGI CDT Phase 2 (£3M EPSRC grant), the Digital Creativity Hub, and contributions to game intelligence research. His research emphasizes human-AI interaction, ethical AI narratives, and AI-driven solutions for complex systems. Professor Cowling’s publications highlight contributions to MCTS optimization, esports prediction models, and AI applications in procedural content generation. His interdisciplinary approach bridges computer science, psychology, and economics, addressing societal challenges through technological innovation.

• Biometrics
• Computer Vision
• Medical Imaging
• +۳ مورد دیگر

Jian He is a Teaching Professor at the School of Mathematics , Monash University, Australia. His research focuses on advancing cross-spectral biometric systems, medical image processing, generative adversarial networks (GANs), and robust machine learning. He has contributed significantly to improving face recognition technologies under challenging conditions such as thermal-to-visible spectrum translation and adversarial attacks. His work includes developing frameworks for medical imaging segmentation, enhancing security in biometric systems, and exploring novel applications in audio-visual synchronization and video generation. Key research themes include: Cross-spectral face recognition and thermal imaging analysis Medical image segmentation and 3D reconstruction Generative models for text-to-image synthesis and video generation Adversarial attacks and defense mechanisms in AI systems His publications demonstrate expertise in integrating deep learning with biometric security, healthcare AI, and computer vision applications. Notable contributions include surveys on cross-spectral recognition and innovations in latent diffusion models for high-quality video synthesis. No scientific awards or grants are explicitly listed in the provided texts. He currently has no recorded advisees, though his research areas suggest potential collaborations in interdisciplinary teams focused on AI and medical imaging.

Harry Nguyenمشاهده پروفایل

مدرس

• Reliable AI
• Multimodal Machine Learning
• Health Informatics

Harry Nguyen (Hoang D. Nguyen) is a Lecturer and Programme Director for the MSc in Computing Science at University College Cork. Affiliated with the SFI Research Centre for Data Analytics, he leads research in reliable AI systems for healthcare and sustainability. Research Focus: Develops robust ML models integrating graph networks and multimodal learning, with applications in medical diagnosis (e.g., COVID-19 detection via cough analysis) and environmental monitoring. Directs the Reliable Machine Intelligence research group. Advising: Mentors 15+ graduate students on projects spanning federated learning, sound-based diagnostics, and conversational AI for diabetes care. Secured funding from Science Foundation Ireland for CRT-AI initiatives.

• Deep Learning
• Speech Processing
• Speech Enhancement
• +۲ مورد دیگر

Sabato Marco Siniscalchi is a Full Professor at the University of Palermo and an Adjunct Professor at NTNU. He holds a doctorate in computer engineering from the University of Palermo and has held positions at Georgia Tech, Apple Inc., and Kore University. His research focuses on deep learning applications in speech processing and health-related speech analysis. He previously served as Associate Editor for IEEE/ACM Transactions on Audio, Speech and Language Processing and is an elected member of the IEEE SLT Committee. His expertise spans neural networks, acoustic modeling, and speech recognition systems.

• Probability
• Machine Learning
• Digital Signal Processing
• +۵ مورد دیگر

Jacob Mashburn is an Instructional Assistant Professor in the Department of Mathematics at Texas A&M University, part of the College of Arts & Sciences. He holds a PhD (2016-2022) and BA (2012-2016) in Mathematics from Texas A&M, with a minor in Economics. His research focuses on Probability Theory, Quantum Mechanics applications in Fock Spaces, and operator algebras. He has published two academic papers in collaboration with Dr. Michael Anshelevich. Education details include a dissertation titled "Distributions and von Neumann Algebras over Fock Spaces with Depth-Two Action" under Prof. Anshelevich. He has extensive teaching experience, instructing over 700 students in Python/MATLAB programming with a 94% pass rate. His skills span Python, MATLAB, TensorFlow, and audio signal processing. Research interests include machine learning applications to audio synthesis (e.g., FM synthesizer prediction with Notable projects include a data visualization challenge (2nd place) analyzing sustainable energy models and a data science competition award for best external data use. His LinkedIn and GitHub profiles highlight collaborative work across academia and industry.

Robert Roweمشاهده پروفایل

استاد

• Music Technology
• Interactive Music Systems
• Algorithmic Composition
• +۳ مورد دیگر

Robert Rowe is a Professor of Music Technology at New York University's Steinhardt School of Culture, Education, and Human Development, and an Affiliated Faculty member at NYU Abu Dhabi. He is a core member of the Music & Audio Research Laboratory (MARL), specializing in interactive music systems, algorithmic composition, and music cognition. His work bridges music technology, computer science, and artistic performance. Bachelor of Music in Music History & Theory (University of Wisconsin, 1976) Master of Arts in Composition (University of Iowa, 1978) Ph.D. in Music and Cognition (MIT Media Lab, 1991) Rowe's research focuses on interactive systems, real-time music processing, and the intersection of music with cognitive science and artificial intelligence. His compositions have been performed globally and are featured on multiple record labels. Key contributions include the books Interactive Music Systems (1993) and Machine Musicianship (2001), which explore computational approaches to music creation and performance. His recent work emphasizes distributed performance systems, emotion recognition in music, and the application of machine learning to interactive composition. Notable projects include Melting the Darkness (2014) and Arcturus (2006), which showcase real-time interaction between performers and computational systems. Awards : First Prize in the Bourges International Electroacoustic Music Competition (1990) Grants : Multiple grants from NYU and external institutions for research in music technology and cognition. Rowe has led initiatives in NYU’s Steinhardt School, including serving as Associate Dean of Research & Doctoral Affairs, and collaborates internationally with institutions like IRCAM (Paris) and the Sweelinck Conservatory (Amsterdam). His lab, MARL, develops cutting-edge tools for music analysis, generation, and performance.

Simon Leglaiveمشاهده پروفایل

استادیار

• Audio Signal Processing
• Machine Learning
• Speech Enhancement
• +۷ مورد دیگر

Simon Leglaive is a tenured Assistant Professor at CentraleSupélec in Rennes, France, and a researcher in the AIMAC team at the IETR laboratory (CNRS UMR 6164). He holds a PhD in Audio Signal Processing from Télécom Paris (2017), and previously served as a postdoctoral researcher at Inria Grenoble Rhône-Alpes. His academic affiliations include CentraleSupélec, Télécom Paris, and CNRS, with research collaborations across institutions including Inria, GIPSA-lab, and Sorbonne University. His research focuses on the intersection of signal processing and machine learning, particularly for audio and speech applications. Key areas include probabilistic generative models, deep learning, low-dimensional representation learning, and weakly or semi-supervised learning. His work enables advanced multimodal machine perception tasks such as speech enhancement, audiovisual speech analysis, and human mesh recovery. He teaches machine learning, deep learning, and audio signal processing at CentraleSupélec. The 15 most recent publications highlight a strong trend in deep generative modeling, especially using variational autoencoders (VAEs) and masked autoencoders, for tasks like speech enhancement, source separation, emotion recognition, and human pose recovery. There is a clear emphasis on disentangled and interpretable latent representations, multimodal fusion, and unsupervised or weakly supervised methods. Applications span from speech technology to computer vision, reflecting a cross-disciplinary approach centered on fundamental representation learning. Coordinator and Principal Investigator, DEGREASE project (ANR-23-CE23-0009) Partner, DEESSE project (ANR-24-CE23-2229) Elected member, IEEE AASP Technical Committee (2023–2025) Organizing committee, CHiME-7 UDASE challenge (2023) Conference area chair, ICASSP (2024, 2025), WASPAA (2023, 2025) Simon Leglaive advises multiple PhD students and has served on the IETR laboratory council since 2022. His academic service includes extensive reviewing for top journals and conferences in signal processing and machine learning. His research is supported by competitive grants from the French National Research Agency (ANR). He is actively involved in collaborative research through the AIMAC team at IETR and partnerships with Inria and industry labs.

Nicola Dibbenمشاهده پروفایل

استاد

• Music Psychology
• Popular Music Studies
• Digital Media and Music
• +۳ مورد دیگر

Nicola Dibben is a Professor of Music and Faculty Director of Research and Innovation at the School of Languages, Arts and Societies, University of Sheffield. Her work bridges music cognition, popular music studies, and music digitalisation, with a focus on AI music generation, environmentalism, and social justice. Her research explores how music engagement shapes identity, emotions, and environmental values, including collaborations with Björk on the Biophilia album-app and projects like MIMA (Machine Intelligence for Music and Audio). She investigates ethical AI tools for musicians and the psychological impacts of synthetic media. Key Research Areas: Music and AI (assistive technologies, voice conversion) Environmentalism in music (Iceland, Colombia collaborations) Embodied cognition (facial surgery effects, prosociality studies) Digital transformations (VR, tablet-based music) Scientific Awards: Honorary Doctorate, University of Oslo (2022) Fellow of the Higher Education Academy (FHEA) She supervises doctoral candidates in music cognition, transgender singer experiences, and music's role in environmentalism. Her work includes Datasounds (AHRC-funded) and MiMA (Machine Intelligence for Music and Audio).

• Signal Processing
• Acoustic Sensor Networks
• Machine Learning
• +۳ مورد دیگر

Roberto Gil Pita is a Professor at the University of Alcalá (Spain), affiliated with the Department of Signal Theory and Communications. He leads the AES3 research group focusing on acoustic and electromagnetic smart sensor networks and signal processing applications. His doctoral work (2006) centered on radar target classification using statistical and AI methods under the supervision of Dr. Manuel Rosa Zurera. His research spans signal processing, machine learning, and their applications in aerospace, biomedical systems, and smart cities. Key areas include UAV detection, emotion recognition from speech, and acoustic localization using microphone arrays. His academic background includes a doctorate from the University of Alcalá and extensive contributions to wireless acoustic sensor networks, hearing aid signal processing, and bioimpedance spectroscopy. He has developed energy-efficient algorithms for real-time audio analysis, acoustic violence detection systems, and robust methods for speech enhancement in noisy environments. His work bridges theoretical signal processing with practical engineering solutions for defense, healthcare, and urban monitoring. Research interests extend to aeroelastic flutter analysis in aviation, wearable biomedical sensors for stress assessment, and data-driven approaches for sound environment classification. He has pioneered the use of deep learning in flutter testing and acoustic event classification, contributing to datasets like REALISED for benchmarking machine learning models. Notable projects include acoustic localization of drones using microphone arrays, real-time emotion detection systems, and collaborative research in smart healthcare technologies. His work emphasizes computational efficiency and energy conservation, particularly for embedded systems and battery-operated devices.

• Acoustic Signal Processing
• Speech Enhancement
• Microphone Array Processing
• +۶ مورد دیگر

Dr Alastair Moore is a Researcher in the Dyson School of Design Engineering at Imperial College London, affiliated with the Speech and Audio Processing Laboratory. He holds a PhD from Imperial College and has industrial experience in consumer electronics. His research focuses on microphone array signal processing, speech enhancement in reverberant environments, and applications in augmented reality, hearing aids, and robot audition. Education: MEng in Electronic Engineering with Music Technology Systems (1st class) from University of York (2005); PhD in Binaural Hearing funded by France Telecom R&D (2012). Research Interests: He specializes in speech enhancement algorithms for wearable devices, binaural hearing systems, room acoustics modeling, and acoustic SLAM. His work includes developing virtual reality tools for hearing aid evaluation and room identification techniques for forensic applications. Funding: His projects are supported by EPSRC, EU FP7, Google, Reality Labs Research, and the UK Home Office. Notable grants include EPSRC EP/M026698/1: Environment-aware speech enhancement EPSRC EP/S035842/1: Binaural hearing aids with VR evaluation EU FP7/2007-2013: Robot audition using spherical microphone arrays Google: Speech dereverberation for VoIP UK Home Office: Audio forensics via room geometry inference Labs: Active member of the Speech and Audio Processing group led by Prof. Mike Brookes and Prof. Patrick A. Naylor.

Gérard Cholletمشاهده پروفایل

استاد

• Speech Processing
• Biometrics
• Audiovisual Systems
• +۳ مورد دیگر

Dr. Gérard Chollet is a Professor at Telecom SudParis, associated with the SAMOVAR laboratory. His research focuses on speech processing, biometrics, privacy-preserving AI, and assistive robotics. He leads projects like the EMPATHIC virtual coach for aging populations and the Roberta IRONSIDE robot assistant. Key contributions include audiovisual identity verification, low-resource ASR systems, and privacy-conscious machine learning frameworks. His work spans over 20 years with publications in top journals and conferences, emphasizing healthcare technology and multimodal human-machine interaction. Education: Not explicitly stated in text, but expertise implies advanced degrees in electrical engineering or computer science. Research Interests: Speech recognition, audiovisual biometrics, privacy in AI, assistive robotics, and end-to-end machine learning. Recent work emphasizes ethical AI, healthcare applications, and secure cloud computing. Grants & Collaborations: Involved in EU projects (e.g., EMPATHIC, IRONSIDE). Collaborates with industry partners (e.g., SpeechMorphing Inc., University of Surrey) and global institutions (e.g., University of Tsukuba, MIT). Lab/Team: SAMOVAR lab at Telecom SudParis, focusing on media and networks research. Active in cross-disciplinary teams for health tech and AI ethics.

Dijana Petrovskaمشاهده پروفایل

مدرس

• Biometrics
• Speech Processing
• Computer Vision
• +۳ مورد دیگر

Dijana Petrovska is a Lecturer at Telecom SudParis, part of the Université Paris-Saclay. Her work focuses on biometric systems, medical diagnostics, and privacy-preserving technologies. She has contributed to projects like the EMPATHIC Virtual Coach, aimed at improving elderly health through AI-driven virtual assistants. Her research interests include facial recognition, speech processing, and cryptographic key regeneration using biometrics. Notably, she has pioneered methodologies for early-stage Parkinson's disease detection via facial action units and voice analysis. She also explores secure authentication systems combining biometrics with cryptography, emphasizing privacy preservation. Publications span over two decades, with recent work emphasizing cross-disciplinary applications of AI in healthcare and security. While no specific academic awards are listed, her impactful contributions to the field of biometric and medical AI are evident through her prolific publication record and active project leadership. Her involvement in collaborative projects like SpeechXRays and EMPATHIC demonstrates a commitment to bridging technical innovation with real-world societal needs. Though no student advisees are listed, her work likely impacts both academic and industry research ecosystems.

Suresh Raja Neethirajanمشاهده پروفایل

دانشیار

• Digital Agriculture
• Artificial Intelligence
• Big Data Analytics
• +۱۱ مورد دیگر

Dr. Suresh Raja Neethirajan is an Associate Professor and Dalhousie University Research Chair in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. His research program focuses on digital livestock farming, integrating artificial intelligence, big data analytics, and advanced sensor technologies to improve animal health, welfare, and sustainable agricultural practices. His research interests span Digital Agriculture , Artificial Intelligence , Big Data Analytics , Animal-Computer Interaction , and Sensors & Bio-Instrumentation . He leads the Mooanalytica research lab, which develops innovative tools such as facial recognition systems for emotion detection in cows and pigs, rapid diagnostic sensors for avian influenza and food allergens, and wearable devices for non-invasive physiological monitoring. The recent publications reflect a strong trend in developing AI-driven, sensor-based solutions for precision livestock farming. These include emotion recognition in pigs, sex detection in chicken eggs, and circadian rhythm modeling in dairy cows, all leveraging computer vision, machine learning, and multimodal data fusion to advance animal welfare and farm efficiency. Dalhousie University Research Chair Dr. Neethirajan supervises BSc, MSc, and PhD students and is actively involved in teaching and guest lectures on sensor technologies. His lab has access to state-of-the-art research facilities in the Netherlands and collaborates across veterinary, agricultural, and biomedical domains. He has previously held academic and research positions at Wageningen University, the University of Guelph, Oak Ridge National Laboratory, and the National Agricultural Research Organization in Japan. His team has developed numerous industrial-ready solutions, including on-farm detection tests for ketosis and coronavirus, and is focused on transforming laboratory innovations into practical applications for the agri-food industry. Future work continues to emphasize resilience sensing, data management systems, and non-contact vital sign technologies.