جست‌وجوی استاد و پژوهشگر

Renata Dividinoمشاهده پروفایل

استادیار

• Graph Knowledge Representation
• Machine Learning
• Web Science
• +۵ مورد دیگر

Renata Dividino is an Assistant Professor in the Department of Computer Science at Brock University, Canada. She holds a BSc from the University of Campinas (Brazil), an MSc from Universität des Saarlandes (Germany), and a PhD from Universität Koblenz – Landau (Germany). Her research focuses on graph knowledge representation, machine learning, and their applications in web science, semantic web foundations, and provenance systems. She has worked at institutions like DFKI, Fraunhofer IGD, and the Big Data Analytics Lab at Dalhousie University, bridging academic and industrial sectors. Her industry experience includes roles as an AI Scientist and Director of Data Science in the maritime sector, where she developed patented technologies for AI-driven maritime operations and risk assessment systems. Key research contributions include improving AI system reliability via provenance analysis and advancing knowledge graph applications. Education: BSc in Computer Science, University of Campinas MSc in Computer Science, Universität des Saarlandes PhD in Computer Science, Universität Koblenz – Landau Research Interests: Provenance systems, semantic web foundations, knowledge graphs, graph-based AI, maritime AI applications, and federated learning. Her work emphasizes practical applications in complex networks, web-scale data, and social networks. Awards: No specific awards mentioned, but her contributions include patented technologies and peer-reviewed publications on provenance-driven AI transparency. Advising & Grants: Secured industry grants for R&D projects in maritime AI and data science. Her work on vessel risk assessment and infectious disease prediction demonstrates applied research impact.

• Digital Signal Processing
• Audio Technology
• Acoustics
• +۵ مورد دیگر

Prof. Dr.-Ing. André Jakob is a faculty member at Berlin University of Technology , affiliated with the Department VII - Electrical Engineering - Mechatronics - Optometry. His academic role spans teaching and research in digital signal processing, audio technology, and acoustics. Digital Signal Processing Audio Technology Acoustics Active Noise Control His research focuses on active noise control , simulation of moving sound sources , and audio signal processing , with applications in robotics, building acoustics, and medical devices. Publications include advancements in anti-noise window systems , sound source localization , and acoustic measurement techniques . His recent work explores real-time auralization for educational robotics and nonlinear acoustic modeling with neural networks. The 15 most recent articles demonstrate a consistent focus on acoustic simulation , active control systems , and sound propagation modeling , with conference contributions at DAGA, NAG-DAGA, and international acoustics events. Topics range from dental drill noise reduction to active sound design in musical instruments , reflecting interdisciplinary applications. He supervises numerous Master's and Bachelor's theses in areas like real-time signal processing, deep learning for sound recognition, and virtual acoustics. His lab at TU Berlin explores multi-loudspeaker systems , acoustic beamforming , and active noise cancellation for both industrial and consumer applications.

Haydar Aygunمشاهده پروفایل

دانشیار

• Acoustic Materials
• Metamaterials
• Biomedical Acoustics
• +۷ مورد دیگر

Dr. Haydar Aygun serves as Associate Professor in Acoustics and Building Services at the Bioscience and Bioengineering Research Centre, Department of Civil and Building Services Engineering, University of Salford. He holds multiple leadership roles including Course Director for the MSc Environmental and Architectural Acoustics, Diploma in Acoustics and Noise Control, and The Acoustics Apprenticeship programs. With a PhD in Acoustics and Vibration from the University of Hull (2006), his academic foundation includes a MSc in Advanced Materials, Processes and Manufacturing (2002) and a BSc in Mechanical Engineering. Dr. Aygun's research spans multiple critical domains of acoustics: Development and characterization of novel acoustic materials and metamaterials for noise control Duct acoustics and sound propagation through complex media Biomedical applications including bone analysis and respiratory sound detection Vibration control of porous materials using advanced computational methods Environmental noise assessment and building acoustics solutions His publication record shows consistent scholarly output with 46 research items from 2006-2025, demonstrating particular productivity in recent years (7 publications in 2023). His work shows increasing integration of computational methods with experimental validation, expanding applications in medical diagnostics and sustainable building technologies. Dr. Aygun actively contributes to the academic community through multiple service roles: Editorial Board Member for Journal of Applied Acoustics EPSRC Grant Reviewer Reviewer for Journal of Applied Acoustics and Journal of the Acoustical Society of America Deputy leader of Biomedical Acoustics Special Interest Group within UK Acoustics Network As an educator, Dr. Aygun has supervised numerous PhD students and teaches across multiple levels from undergraduate Building Services Engineering to specialized postgraduate Acoustics programs. His consultancy work bridges academic expertise with practical industry applications in noise assessment, building acoustics, and event sound management.

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

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

Weiran Wangمشاهده پروفایل

استادیار

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

Weiran Wang is an Assistant Professor in the Department of Computer Science at the University of Iowa. Previously, he worked as a Staff Research Scientist at Google (2021-2024), Senior Research Scientist at Salesforce Research (2019-2020), and Amazon Alexa (2017-2019). He completed his PhD in 2013 at UC Merced under Miguel A. Carreira-Perpinan and postdoctoral research at Toyota Technological Institute at Chicago (2014-2017) with Karen Livescu and Nathan Srebro. PhD: EECS Department, UC Merced (2013) MS: Computer Science, Chinese Academy of Sciences (2008) BS: Computer Science, Huazhong University of Science and Technology (2005) His research focuses on machine learning algorithms for speech processing , multi-view representation learning , and optimization . Key contributions include advancements in end-to-end speech recognition, stochastic canonical correlation analysis, and deep variational methods for multi-modal data. Notable work includes improving WER metrics for telephony speech and developing GPU/TPU implementations for ASR biasing. The 15 most recent publications span 2024-2018, emphasizing speech recognition (2024 NAACL/Interspeech), multi-view learning (2022 ICLR), self-training (2020 Interspeech), and acoustic modeling (2018). Trends include deep learning, attention mechanisms, and distributed optimization techniques. No scientific awards are explicitly mentioned in the provided texts. Current teaching includes CS4980: Deep Learning (Spring 2025) and CS4420: Artificial Intelligence (Fall 2024).

• Cybersecurity
• Privacy
• Industrial Networks
• +۷ مورد دیگر

Martin Henze is a tenure-track Assistant Professor at RWTH Aachen University's Department of Computer Science, where he leads the Security and Privacy in Industrial Cooperation (SPICe) research group. Additionally, he co-leads the Secure Production & Energy Networks research group at the Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE in Bonn, Germany. His work bridges academic research with practical industrial security applications, focusing on critical infrastructure protection. Dr. Henze's research interests center on technical security and privacy aspects of industrial networks and data sharing, with special emphasis on energy and production sectors. His work spans industrial intrusion detection, 5G security for industrial applications, IoT security in constrained environments, and blockchain security. He develops practical security solutions that balance protection needs with the resource constraints and operational requirements of industrial systems, particularly focusing on making security both effective and comprehensible for operators. His recent publications demonstrate a strong focus on industrial security challenges, with particular emphasis on intrusion detection systems that maintain operator control, TLS optimization for resource-constrained industrial IoT, 5G security for production systems, and novel approaches to securing legacy industrial protocols. His work consistently addresses the tension between security requirements and operational constraints in industrial settings. Nachwuchsförderpreis Verbraucherforschung NRW Borchers-Plakette ICT Young Researcher Award Dr. Henze actively contributes to the academic community through service on numerous prestigious program committees including ACM CCS, IEEE S&P, NDSS, and USENIX Security. His teaching portfolio includes graduate courses on Industrial Data Security, Industrial Network Security, and specialized seminars on 5G/6G Security and IoT Security. His research is highly collaborative, frequently involving partnerships across institutions and with industry to address real-world security challenges in critical infrastructure. He heads the SPICe research group at RWTH Aachen, which focuses on developing practical security and privacy solutions for industrial cooperation scenarios. The group's work emphasizes creating security mechanisms that are not only technically sound but also comprehensible and usable by industrial operators, recognizing that the human element is critical in maintaining security in complex industrial environments.

• Bio-inspired Sensors
• MEMS
• Neuromorphic Engineering
• +۳ مورد دیگر

Claudia Lenk serves as Full Professor at Ulm University since 2024, leading the Biomedical Sensor Systems and Microsystems research group. Her work focuses on developing bio-inspired acoustic sensors to enhance human and machine hearing capabilities, particularly for speech processing in noisy environments through MEMS-based adaptive technologies. Education: Technical Physics, TU Ilmenau PhD in Biophysics (specializing in numerical/chemical modeling of atrial fibrillation mechanisms) Postdoctoral research developing MEMS-based artificial hair cells for hearing enhancement Her research integrates bio-inspired engineering with acoustic sensor design to create noise-robust systems that mimic biological hearing mechanisms. Key innovations include tunable MEMS resonators, neuromorphic auditory processing, and integrated signal pre-processing for hearing aids and robotics. This interdisciplinary approach bridges microsystems engineering, neuroscience, and auditory perception to address limitations in current speech processing technologies. Analysis of her 15 most recent publications (2020-2025) reveals a consistent trajectory toward adaptive neuromorphic acoustic systems. Dominant themes include resonance frequency control, dynamic range expansion through nonlinear dynamics, and bio-inspired feature extraction for low signal-to-noise ratio environments. These advancements target practical implementations in energy-constrained devices like hearing aids and autonomous systems. Scientific Awards: No awards documented in source materials Advising and Grants: Source materials contain no information regarding student supervision, research grants, or collaborative funding initiatives. Labs and Teams: She directs Ulm University's Biomedical Sensor Systems and Microsystems group, which develops cutting-edge sensor technologies for hearing applications, robotics, and speech processing systems through MEMS fabrication and neuromorphic computing approaches.

Marchegiani Maria Letiziaمشاهده پروفایل

استادیار

• Signal Processing
• Machine Learning
• Robotics
• +۹ مورد دیگر

Maria Letizia Marchegiani is an Assistant Professor at the University of Parma , affiliated with the Department of Engineering and Architecture . Previously, she held academic positions at Aalborg University (2019) and Oxford Robotics Institute (2014-2018). Education: PhD in Computer Science and Engineering from Sapienza - University of Rome , MSc/BEng in Computer Engineering Her research interests span signal processing , machine learning , and their applications to robotics , autonomous systems , intelligent transportation , and intelligent healthcare . She explores intersections between auditory perception , cognitive modeling , and energy-efficient wearable systems . Recent publications demonstrate expertise in acoustic event localization , ML-SDWSN architectures , thermal camera integration for vehicles, and privacy-preserving wearable systems . Her work addresses challenges in network reliability , urban soundscapes , and human-robot collaboration .

Morwaread Farboodمشاهده پروفایل

دانشیار

• Music Perception and Cognition
• Computational Modeling of Music
• Algorithmic Composition
• +۳ مورد دیگر

Morwaread Farbood is an Associate Professor and Associate Director of Music Technology at New York University's Steinhardt School of Culture, Education, and Human Development. She holds dual affiliations with the NYU Music and Audio Research Lab (MARL) and the Max Planck/NYU Center for Language, Music, and Emotion (CLaME). Her research bridges music cognition, computational modeling, and algorithmic composition, with a focus on real-time auditory perception phenomena like tonality and musical tension. She co-founded the Northeast Music Cognition Group and was a 2017-2018 Radcliffe Fellow in Computer Science at Harvard University. Her professional achievements include the Pro Musicis International Award and First Prize at the Prague International Harpsichord Competition. Farbood actively contributes to music accessibility through innovations in audio description systems for live theater, using reference recordings and real-time synchronization techniques. She also maintains an active performing career as a harpsichordist, with performances at venues like Carnegie Hall and major international festivals. Research interests span computational models of musical structure, cross-cultural melodic learning, and neurocognitive underpinnings of musical perception. Her work integrates methods from music theory, cognitive psychology, and computer science to advance understanding of auditory perception's temporal dynamics. Recent projects include developing automated audio description systems for theatrical performances and exploring asymmetrical scale properties' impact on musical learning. She has authored over 50 peer-reviewed articles, patents, and creative works, reflecting her dual expertise in academic research and artistic practice.

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.

Josh Reissمشاهده پروفایل

استاد

• Audio Engineering
• Signal Processing
• Music Production
• +۴ مورد دیگر

Josh Reiss is a Professor of Audio Engineering at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science . He holds additional roles including President-Elect and Fellow of the Audio Engineering Society (AES), and Visiting Professor at Birmingham City University. His research focuses on audio signal processing, procedural audio, and intelligent music production. He earned degrees including BSc in Physics, BSc in Mathematics, and a PhD. Research & Awards : Reiss has published over 200 papers, authored books like Intelligent Music Production , and received awards such as the AES Board of Governors Award (2009, 2010) and Best JAES Paper 2016. His work spans sound synthesis, dynamic range compression, and live audio systems. Teaching & Industry : Teaches modules like Artificial Intelligence and Sound Design. Co-founded startups LandR (AI mixing), Tonz, and Nemisindo. Leads the Centre for Digital Music at QMUL, advancing research in audio technology. Labs & Teams : Active in the Centre for Digital Music, collaborating on projects like the Open Multitrack Testbed and semantic audio evaluation tools.

• Parallel Programming
• Dependently Typed Languages
• Concurrent Systems
• +۳ مورد دیگر

Dr Adam Barwell is a Lecturer and Researcher at the School of Computer Science, University of St Andrews. His research focuses on parallel programming, dependently typed languages, and formal methods in concurrent systems. He advises PhD student Leonid Nosovitskiy and has contributed to projects involving session types, distributed systems, and type-safe refactoring techniques. His educational background includes a PhD (not explicitly detailed here), and he actively engages in academic activities such as organizing events like Doors Open @ Computer Science 2025. Key research themes include concurrency control, functional programming paradigms, and improving code security through dependent types. Recent publications emphasize advancements in parallel language design, fault-tolerant protocols, and type-driven development. Collaborations span international conferences and institutions, reflecting his contributions to both theoretical and applied computer science domains.

Michel Dumontierمشاهده پروفایل

استاد

• FAIR data
• generative artificial intelligence
• machine learning
• +۹ مورد دیگر

Dr. Michel Dumontier is a Distinguished Professor of Data Science at Maastricht University, where he serves as the founder and Director of the Institute of Data Science. He is internationally recognized as a co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles, which have transformed scientific data management globally. His academic background includes: BSc in Biochemistry from the University of Manitoba (1999) PhD in Bioinformatics from the University of Toronto (2004) Assistant/Associate Professor at Carleton University (2005-2013) Associate Professor at Stanford University (2013-2016) Distinguished Professor at Maastricht University (2017-present) Dr. Dumontier's research focuses on unlocking data potential for scientific discovery, with expertise in knowledge graphs for drug discovery and personalized medicine. His work spans FAIR data principles, generative AI, machine learning, semantic technologies, ontology, and data integration. His recent publications reveal a strong trend toward applying generative AI to healthcare data, with increasing focus on synthetic health data generation, privacy-preserving techniques, and knowledge graph applications in drug repurposing. His work bridges computer science, biomedical informatics, and clinical applications across multiple medical domains. Dr. Dumontier has secured significant research funding as a principal investigator: NWO (Dutch Research Council) Horizon Europe MCSA NIH/NCATS ARPA-H He coordinates the AIDAVA and REALM projects, leads the NCATS Biomedical Data Translator, and directs the GENIUS AI lab. As editor-in-chief of the journal Data Science, he shapes discourse in the field. Dr. Dumontier maintains active industry connections through: Minderheidsaandeelhouder at Data2Discovery Inc Scientific advisor and minority shareholder at OntoForce NV Scientific advisor, board member, and minority shareholder at Comunicare Editor-in-chief of Data Science Journal at Sage Publishing

Simone Baumann-Pickeringمشاهده پروفایل

استاد

• Acoustic Ecology
• Marine Mammal Biology
• Deep-Sea Biology
• +۵ مورد دیگر

Simone Baumann-Pickering is a Professor of Biological Oceanography at the Marine Physical Laboratory, part of the Scripps Institution of Oceanography at UC San Diego. Her research focuses on acoustic ecology, marine mammal behavior, and conservation, with an emphasis on using passive acoustics to study species distribution, habitat utilization, and ecosystem dynamics. She leads the Scripps Acoustic Ecology Laboratory , which develops innovative methods for long-term acoustic monitoring and species classification. Education: PhD in Biology, Eberhard-Karls-Universität Tübingen, Germany M.S. in Biology, Eberhard-Karls-Universität Tübingen, Germany B.S. in Biology, Eberhard-Karls-Universität Tübingen, Germany B.S. in Media Engineering, Stuttgart Media University, Germany Research Interests: Acoustic ecology, adaptive strategies in changing environments, conservation management, habitat modeling, and species discrimination through sound. Her work bridges technology and ecology, leveraging machine learning and hydrophone arrays to address conservation challenges. Recent Research Trends: Her articles emphasize the integration of acoustic data with environmental variables (e.g., oceanographic conditions, climate fluctuations) to understand species behavior and distribution. Key themes include sperm whale demographics, beaked whale habitat utilization, and the impacts of vessel noise on marine ecosystems. Grants & Projects: She leads initiatives like SanctSound , which builds data systems for marine management decisions. Her work often involves collaboration with federal agencies and conservation organizations to inform policy. Labs & Teams: The Scripps Acoustic Ecology Laboratory develops tools such as Where’s Whaledo to advance array-based localization of marine animal vocalizations, enhancing conservation and research capabilities.

Gaël Richardمشاهده پروفایل

استاد

• Machine Learning
• Audio Signal Processing
• Artificial Intelligence
• +۳ مورد دیگر

Gaël Richard is a Professor at Télécom Paris specializing in machine learning and audio signal processing. He leads the Hi! Paris center, focusing on AI and data science applications. His research emphasizes hybrid interpretable AI for sound analysis, including projects like Hi-Audio funded by a €2.5M ERC Advanced Grant (2022). Key areas include machine listening, music source separation, and speech processing. Applications span autonomous vehicle acoustics and music technology. Notable contributions include neural audio compression (QINCODEC), diffusion models for music synthesis (Diff-TONE), and source separation techniques (Inverse Drum Machine). Research & Awards Recipient of the 2022 ERC Advanced Grant for the Hi-Audio project exploring hybrid AI models that integrate domain knowledge with neural networks. This approach reduces data requirements and enhances model interpretability. Active in audio-visual scene analysis and weakly-supervised learning systems. Affiliations & Labs Executive Director of Hi! Paris, a multidisciplinary lab advancing AI and data science for societal impact. Collaborates on projects like the HI-AUDIO online platform for distributed music data collection and the MAD-EEG EEG dataset for auditory attention decoding.