جست‌وجوی استاد و پژوهشگر

Xavier Serraمشاهده پروفایل

استاد

• Audio Signal Processing
• Sound and Music Computing
• Music Information Retrieval
• +۵ مورد دیگر

Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.

Karthik Menonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Bioengineering
• Computational Modeling
• +۵ مورد دیگر

Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.

Nicolas Davidenkoمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Perception
• Face Recognition
• +۵ مورد دیگر

Nicolas Davidenko is an Associate Professor in the Department of Psychology at the University of California, Santa Cruz (UCSC). He leads the High Level Perception Lab, focusing on behavioral and computational studies of human perception, particularly face recognition, spatial orientation, and visual ambiguity. His work emphasizes 'top-down' processes like attention and expectations. Davidenko holds a Ph.D. in Psychology from Stanford University (2006), an M.S. in Statistics from Stanford (2004), and an A.B. in Mathematics from Harvard (1998). His research explores how humans perceive and interpret complex visual information, including studies on illusions, virtual reality, and misophonia. He has developed parametric models of faces to study memory encoding and drawing accuracy. Notable achievements include a Top-10 Finalist placement in the 2015 Best Illusion of the Year Contest for his 'Mind-controlled motion' research. He teaches courses such as PSYC 121 (Perception), PSYC 139K (Face Recognition), and advanced cognitive research seminars. Davidenko also runs the CSASS Matlab Workshops, training researchers in statistical tools. His lab includes graduate students and postdocs, with alumni like Jennifer Day (Ph.D. ’19) and Pat Samermit (Ph.D. ’18). Recent projects include investigations into vection in VR environments, cross-sensory modulation of aversive sounds, and time perception in virtual reality. His work bridges cognitive psychology, neuroscience, and computational modeling, contributing to understanding how perception shapes human interaction with the environment.

Angelika Braunمشاهده پروفایل

استاد

• Sociophonetics
• Forensic Phonetics
• Contrastive Phonetics
• +۶ مورد دیگر

Prof. Dr. Angelika Braun is a full Professor of Phonetics at the University of Trier since October 2009, with a career spanning forensic phonetics, sociophonetics, and cross-cultural speech analysis. She previously held roles at the Bundeskriminalamt (Wiesbaden/Düsseldorf) and Philipps-Universität Marburg, where she habilitated in Phonetics and Speech Processing (2000). Her work bridges academic research with forensic practice. Research Focus: Her Sociophonetics (language and emotions, gender-specific speech) Forensic Phonetics (speaker identification, voice analysis) Contrastive and Hawaiian Phonetics Speech prosody and toxin effects (smoking, alcohol) Intercultural dubbing studies Academic Contributions: Over 15 recent articles explore voice quality, emotional speech, forensic age estimation, and cross-cultural dubbing effects. Key conferences include Interspeech, International Congress of Phonetic Sciences, and ISCA. Her work appears in journals like Forensic Linguistics and The Phonetician . Scientific Honors: Fellow of the American Academy of Forensic Sciences (AAFS) Founder Member and former Chairperson of the International Association for Forensic Phonetics (IAFP) Life-Member of the International Phonetic Association (IPA) Leadership roles in ISPhS and GAL Practical Impact: Developed the Almeida-Braun Transcription System for dialect analysis and contributed to forensic audio enhancement protocols (e.g., Rodney King case). Serves as reviewer for Language and Speech , Forensic Linguistics , and JIPA . Collaborates on longitudinal studies of vocal aging and speaker identification.

Yannis Stylianouمشاهده پروفایل

استاد

• Speech Signal Processing
• Audio Signal Processing
• Biomedical Signal Processing
• +۳ مورد دیگر

Yannis Stylianou is Professor of Speech Processing at University of Crete and Senior Research Scientist at Apple. Former positions include AT&T Labs Research, Bell-Labs, and Toshiba Cambridge Research Lab. IEEE Fellow with PhD from ENST-Paris and over 200 publications. Research spans: Adaptive speech/audio modeling Neural speech synthesis/enhancement Biomedical signal processing Awards include: IEEE Fellowship French Ministry Research Fellowship ENST Graduate Scholarship Recent work focuses on neural TTS architectures, intelligibility enhancement, and multimodal synthesis. Organizes annual International Summer School on Speech Processing.

Eugene Chanمشاهده پروفایل

دانشیار

• Consumer Psychology
• Political Ideology and Consumer Behavior
• Marketing Communication
• +۷ مورد دیگر

Eugene Chan is an Associate Professor in the Department of Marketing Management at the Ted Rogers School of Management, Toronto Metropolitan University. He holds a PhD in Marketing from the University of Toronto, a MA in Social Psychology from the University of Chicago, and an AB in Honors Psychology from the University of Michigan, along with an ARCT (Hons) in Piano Performance from the Royal Conservatory of Music, reflecting a multidisciplinary background. His research lies at the intersection of consumer psychology, political ideology, health communication, and sustainability. He employs experimental and survey methods grounded in social science theories to understand how individuals make judgments and decisions in marketplaces and society. Key themes include the influence of political ideology on consumer behavior, strategies for effective marketing and health communication, and promoting environmentally sustainable choices. His work often explores how emotional, moral, and cognitive factors shape consumer decisions. His recent publications span top journals such as Journal of Consumer Psychology , Global Environmental Change , Computers in Human Behavior , and Personality and Social Psychology Bulletin . These studies reveal consistent focus on behavioral interventions, ideological divides, health compliance, and environmental sustainability. Trends indicate a strong emphasis on moral psychology, emotional triggers, and real-world applications in public policy and marketing strategy. Scientific Awards: Emerging Researcher Award, Australian and New Zealand Marketing Academy (2018) Dean’s Award for Excellence in Research by an Early Career Researcher, Monash Business School (2018) He has secured research funding from diverse sources including the National Natural Science Foundation of China, ACR Transformative Consumer Research Grant, IHS Hayek Fund, and the Indiana Commission for Higher Education, supporting projects on fake news mitigation, brand revitalization, moral foundations, and pandemic behavior. He has taught at institutions worldwide, including the University of Toronto, Monash University, and Purdue University, demonstrating international academic engagement. Eugene Chan serves in editorial roles for several journals, including as Associate Editor for Australasian Marketing Journal and International Journal of Consumer Studies , and as Special Issue Editor for Frontiers in Psychology . He also co-authored the 2nd Asia-Pacific edition of the textbook Consumer Behavior , contributing to marketing education. He advises students in consumer behavior and marketing research, though specific advisees are not listed.

• Intelligent Systems and Control
• Cognitive Robotics
• Nano-robotics
• +۹ مورد دیگر

Laxmidhar Behera is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur, specializing in Intelligent Systems and Control. With over two decades of academic experience at IIT Kanpur and international research experience at institutions including Fraunhofer Institute of Autonomous Intelligent Systems in Germany, ETH Zurich, and University of Ulster, he has established himself as a leading researcher in cognitive robotics and intelligent control systems. Dr. Behera's research spans multiple cutting-edge domains including Cognitive Robotics, Nano-robotics, Vision based Control, Soft Computing, Information Retrieval in music and language, Semantic Information Processing, Physics of Complex Systems, Cyber Physical Systems, Formation Control of UAVs, Brain-Computer Interface (BCI), and Sanskrit Computational Linguistics. His interdisciplinary approach bridges traditional control theory with modern computational intelligence techniques, creating innovative solutions for complex real-world problems. His extensive publication record in top-tier journals like IEEE Transactions demonstrates his leadership in areas such as brain-computer interfaces, visual servoing, multi-robot systems, and music information retrieval. Notably, his work on quantum neural networks for EEG filtering and multisatellite formation control has received significant attention in the research community. UKIERI Standard Research Award 2008 Best Paper at International Conf. on Intelligent Sensors and Information Processing (ICISIP-2004) Best Paper at WoSco,02, Int. Conf. High-Performance Computing (HiPC, 2002) AICTE career award for young teacher (1997) Senior Member IEEE Multiple IEEE top accessed articles (2009-2010) As an Associate Editor for Autosoft Journal and Technical Committee Member for Intelligent Control at IEEE Control System Society, Dr. Behera actively contributes to the academic community. His laboratory in the Western Lab - 212A of the Department of Electrical Engineering serves as a hub for research in intelligent systems, where he mentors students and collaborates with researchers worldwide on cutting-edge projects in robotics, control systems, and computational intelligence.

Magdalena Fuentesمشاهده پروفایل

استادیار

• Machine Listening
• Human-Centered Machine Learning
• Multimodal Representation Learning
• +۳ مورد دیگر

Magdalena Fuentes is an Assistant Professor of Music Technology and Integrated Design & Media at New York University (NYU), affiliated with the Music and Audio Research Lab (MARL) and the Integrated Design & Media (IDM) programs. She holds a Ph.D. from Université Paris Saclay (France) and a B.Eng. in Electrical Engineering from Universidad de la República (Uruguay). Her research focuses on Machine Listening, Human-Centered Machine Learning, and Multimodal Representation Learning, with applications to Music Information Retrieval and Environmental Sound Analysis. Previously, she served as a Postdoctoral Faculty Fellow at NYU’s MARL and Center for Urban Science and Progress (CUSP). Her work bridges technical innovation with cultural and societal contexts, particularly in underrepresented music traditions like Brazilian percussion and Candomblé rituals. She has developed open-source tools such as Soundata to ensure reproducible audio dataset usage. Her research outputs span audio-visual synchronization (e.g., SONIQUE), environmental sound monitoring (e.g., SONYC projects), and rhythm analysis frameworks (e.g., Carat toolbox). These contributions highlight her dual focus on advancing machine learning techniques while maintaining human-centric and culturally sensitive applications.

Laurence R. Harrisمشاهده پروفایل

استاد

• Perception
• Neuroscience
• Multisensory Integration
• +۷ مورد دیگر

Laurence R. Harris is a Full Professor in the Department of Physics and Astronomy at York University, Faculty of Science. He is actively engaged in research on human perception during movement and is eligible to supervise graduate students in the Biology Graduate Program, reflecting interdisciplinary work at the intersection of physics, neuroscience, and biology. His research focuses on understanding how the brain integrates information from multiple senses—such as vision, vestibular input, touch, and hearing—to represent body position, orientation, and motion in space. Using virtual reality, electrical stimulation, underwater immersion, and sensory manipulation tools, his lab investigates perceptual mechanisms underlying self-motion, spatial orientation, and event timing. His work has strong implications for both fundamental neuroscience and practical applications in virtual environments and aging populations. The recent publications (2020–2022) span high-impact journals like PLoS ONE , Scientific Reports , and Royal Society Open Science , demonstrating sustained contributions to the fields of perception and multisensory integration. Themes include body representation, age-related sensory decline, visual size and distance estimation, and motion perception during self-motion. These studies employ psychophysical methods and controlled sensory environments to isolate and analyze perceptual mechanisms. Dr. Harris has not had scientific awards explicitly mentioned in the provided text. He supervises graduate students through the Biology Graduate Program and conducts research involving grants and laboratory-based experimental work, though specific grant details are not listed. His lab uses advanced equipment for sensory stimulation and motion tracking, including VR systems, loudspeakers, vibrators, and motion platforms. His laboratory focuses on experimental perception research, utilizing controlled sensory environments to manipulate and measure perceptual responses. The team investigates how altered sensory input affects body image, spatial orientation, and motion perception across the lifespan.

• Language Acquisition
• Cognitive Science
• Computational Modeling
• +۷ مورد دیگر

Emmanuel Dupoux is a Professor at École des Hautes Études en Sciences Sociales (EHESS), affiliated with the School of Advanced Studies in Social Sciences and the Laboratory of Cognitive Science and Psycholinguistics (LSCP). His work bridges cognitive science, computational linguistics, and machine learning to study infant language acquisition and social cognition. Co-creator and director of EHESS Cognitive Science Master program Former LSCP laboratory director (1998-2009) Current research focuses on textless speech modeling and ecological audio analysis Research areas include: Modeling early language acquisition mechanisms using Bayesian models and HMM Investigating phonological 'deafness' and critical age plasticity Studying social cognition development through infant-toddler experiments His technical contributions span speech processing toolkits like Shennong, emergent communication frameworks (STOP dataset), and innovative approaches to self-supervised speech modeling. Recent publications demonstrate breakthroughs in: Topography-inspired CNN designs for better memory efficiency Prosody-aware generative spoken language models (pGSLM) Textless emotion conversion systems He actively explores how machine learning can reverse-engineer infant language learning processes from ecological audio data, with applications in neurodegenerative disease diagnostics. Key collaborations include: INRIA's Cognitive Machine Learning (CoML) team Facebook AI Research (FAIR) partnerships VoxPopuli multilingual speech corpus development

Olivia Wilesمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Adversarial Robustness
• +۴ مورد دیگر

Olivia Wiles is a Senior Researcher at DeepMind, focusing on adversarial robustness, distribution shift, and computer vision. She earned her DPhil from the University of Oxford under Andrew Zisserman in the Visual Geometry Group (VGG), following a Computer Science degree at the University of Cambridge. Her work spans view synthesis, self-supervised learning, and robust model design. Education : DPhil (Oxford), Computer Science (Cambridge) Key Collaborators : Georgia Gkioxari, Justin Johnson, Richard Szeliski (FAIR), Andrew Zisserman Current Role : Senior Researcher at DeepMind Her research emphasizes adversarial robustness , 3D reconstruction , and self-supervised learning , with notable contributions to view synthesis (SynSin), image matching (Co-Attention), and robustness under distribution shifts. Publications at CVPR, NeurIPS, and ECCV highlight her work in generative models, physical prediction, and multi-view geometry. Scientific Awards : Best Poster, BMVC 2017 Best Paper, NeurIPS ML Safety Workshop 2022 Outstanding Reviewer, ECCV 2020, ICCV 2020/2021 Olivia contributes to academia via community service, including roles as Area Chair (CVPR, ICCV) and reviewer for top-tier conferences (NeurIPS, SIGGRAPH) and journals (PAMI). Her Google Scholar profile reveals ongoing work in unsupervised physics modeling , GANs , and intuitive physics from visual data.

Timo Gerkmannمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +۳ مورد دیگر

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Dominique Ginhacمشاهده پروفایل

استاد

• Hardware design of smart vision systems
• Real-time image processing applications
• VLSI CMOS imaging technology
• +۵ مورد دیگر

Dominique Ginhac is a Professor of Electrical Engineering at the Laboratory of Electronics, Informatics and Image (Le2i UMR 6306) at the University of Burgundy in Dijon, France. He joined Le2i as an assistant professor in 2000 and was promoted to professor in 2009, also becoming head of the Sensors & Hardware Architectures team. In 2010, he became head of the Electronic department of Le2i, and in 2011, he was appointed Deputy Director of Le2i, overseeing approximately 200 people across three scientific departments: Computer Sciences, Vision, and Electronic. Ginhac holds a Master's Degree in Engineering from Polytech Clermont-Fd (1995) and a PhD in Computer Vision from Blaise Pascal University (1999). In 2008, he received his Habilitation qualification (Accreditation to supervise research) from the University of Burgundy. From 2007 to 2009, he was a visiting professor at Université Libre de Bruxelles in the Consciousness, Cognition & Computation lab, expanding his expertise into cognitive science applications for imaging systems. His research focuses on the development of smart imaging technology where sensing and processing are integrated as closely as possible to achieve low cost, low power, and high processing capabilities. His work spans hardware design of smart vision systems, implementation of real-time image processing applications, VLSI CMOS imaging technology, and embedded image processing on DSP/FPGA platforms. His research activities represent the crossroads between algorithmic and hardware solutions dedicated to real-time embedded image processing applications. An analysis of Ginhac's recent publications reveals a strong focus on smart camera and sensor development, particularly in high dynamic range imaging, single-photon detection, and configurable hardware accelerators. His work shows a clear trajectory from fundamental VLSI image sensor design toward increasingly sophisticated integrated systems combining sensing, processing, and specialized applications in medical imaging and real-time vision. His interdisciplinary approach bridges electronics, computer vision, and application-specific system design. Throughout his career, Ginhac has supervised 8 PhD students and published 20 papers in international peer-reviewed journals, 3 book chapters, and more than 50 conference papers. His research has resulted in several significant projects including VerIDIS (Vertically Integrated Digital Image Sensor), CAPTIvA (Asynchronous Photon-counTing Image sensor Architecture), HDR-ARtiSt (High Dynamic Range Adaptive Real-time Smart camera), and ASTICO (Assistance Textile Cognitive). Ginhac leads the Electronic department at Le2i, which is part of a research laboratory under the supervision of the University of Burgundy and the CNRS. His work involves close collaboration with industry partners and has resulted in practical implementations of smart imaging systems for applications ranging from medical monitoring to automotive vision systems. His current research continues to push the boundaries of integrated vision systems, with particular emphasis on 3D-stacked image sensors and photon-counting technologies.

Ju Fengمشاهده پروفایل

پژوهشگر

• Wind Energy Systems
• Floating Offshore Wind Farms
• Wind Farm Optimization
• +۶ مورد دیگر

Ju Feng is a Senior Researcher at the Department of Wind and Energy Systems, Technical University of Denmark (DTU), specializing in Wind Energy Systems , Wind Farm Optimization , and Control Strategies . He contributes to research in Floating Offshore Wind Farms , Multi-Disciplinary Optimization , and Noise Propagation . His work includes developing simulation tools, wake models, and control frameworks for wind farms in complex environments. Research Interests: Optimization of wind farm design and operation Advanced wake and flow modeling AI-driven wind energy solutions Noise control and acoustic propagation Multi-fidelity/multi-disciplinary modeling Cost-effective wind farm technologies Publication Trends: Ju Feng's recent work (2022-2024) focuses on floating offshore wind farms , noise constraints , and multi-disciplinary integration . Older articles (2012-2017) emphasize control algorithms , wake modeling , and optimization frameworks . Advising: Supervising PhD Student Wang R. (AI-Augmented Wind Farm Design Optimization, 2025-2028) Co-supervising Nyborg C. M. (Noise Control in Wind Farms, 2019-2023) Collaboration Network: Active in international projects like DecoWind, ModFarm, and FarmConners. Collaborates with institutions such as DTU Wind Energy and researchers including Shen W. Z. , Réthoré P.-E. , and Bredmose H. .

• Cognitive Modelling
• Artificial Intelligence
• Decision-Making
• +۳ مورد دیگر

Dr. Leendert van Maanen is an Associate Professor at the Department of Experimental Psychology, Utrecht University, where he studies cognitive mechanisms underlying information processing and decision-making. His research focuses on developing and validating computational cognitive models, with applications in human-machine interaction and cognitive neuroscience data analysis. Research Themes: Cognitive Modeling, Decision-Making, Human-Centered Artificial Intelligence, Applied Data Science Labs: Leads the COBRA (Cognitive and Behavioral Research in Artificial Intelligence) lab His work spans three levels of cognitive integration in AI: model development, human-machine deployment, and neural data analysis. Recent projects include cognitive strategy detection, digital twin user modeling, and neural event detection using multivariate time series. For collaborations and publications, see leendertvanmaanen.com or Utrecht University Research Portal.