Find Academic Supervisors

Bernhard U. SeeberView profile

Professor

• Auditory perception
• Computational models
• Binaural hearing
• +5 more

Prof. Dr. Bernhard U. Seeber is a faculty member at the Technical University of Munich (TUM) in the Department of Electrical Engineering and Information Technology. He specializes in audio information processing, focusing on auditory perception, computational modeling, and hearing device technologies. Academic Rank: Professor Primary Affiliation: TUM Department of Electrical Engineering and Information Technology Research Interests: Auditory scene analysis, cochlear implants, binaural hearing, room acoustics, and signal processing for hearing aids His recent publications demonstrate expertise in auditory nerve modeling, spatial hearing optimization, and audio-visual simulation environments. While no formal awards or student advisement information are available in the provided texts, his work has significant implications for improving hearing technologies and understanding auditory perception mechanisms.

Ian StewartView profile

Assistant Professor

• Audio Mastering
• Music Technology
• Digital Audio Processing
• +7 more

Ian Stewart is an Assistant Professor in the Music Production and Engineering (MP&E) department at Berklee College of Music, where he teaches audio mastering and music technology. He also operates Flotown Mastering, a professional audio mastering studio he has run since 2011, and has collaborated with artists such as KRS-One and Mr. Lif. He co-developed the Basslane Pro plugin with Tone Projects in 2022 and is a recognized thought leader in hybrid learning technologies for audio education. B.S. in Music Engineering Technology, University of Miami (2007) Stewart's research and teaching focus on audio mastering, digital audio fundamentals, loudness standards (LUFS), and the integration of technology in music production. He is a passionate advocate for audio quality and has authored numerous educational articles on topics such as multiband compression, dynamic EQ, AI in mastering, and headphone-based mastering workflows. His work bridges academic instruction with real-world engineering practice, emphasizing accessibility and adaptability in non-ideal acoustic environments. His recent publications reflect a strong trend toward practical, accessible mastering techniques for modern platforms like Spotify and Apple Music, with emphasis on loudness normalization, tonal balance, and workflow efficiency. He explores both technical and aesthetic aspects of mastering, including the use of tools like Ozone, dithering, true peak limiting, and AI-assisted processing. His articles are widely used in audio education and industry training. Commercial Integrator's 40 Under 40 (2021) Stewart has contributed to the development of online mastering academies for the Audio Engineering Society (AES) and has helped pioneer hybrid learning models during the pandemic. While no formal grants are mentioned, his work in educational technology and plugin development indicates active engagement in research and innovation. He has advised on best practices for remote mastering, home studio setups, and collaborative audio workflows. He runs Flotown Mastering, offering services in music mastering, audio restoration, and forensic audio analysis. The studio emphasizes collaboration, customer service, and technical excellence. Stewart also contributes to iZotope’s educational content as an author and is active in promoting audio literacy among producers and engineers.

• Psychophysics
• Acoustics
• Psychoacoustics
• +7 more

Chris Feakes is a Lecturer at De Montfort University, affiliated with the Faculty of Computing, Engineering and Media and based in the Leicester Media School. His work bridges academic research and industrial practice in audio and media technology. His research interests include: Psychophysics and psychoacoustics Acoustics and sensory impairment Assistive and accessibility technology Human-Computer Interaction (HCI) Media and audio production technologies He teaches courses such as Audio Production, AV Production, and supervises final-year projects. His research is supported by affiliation with the Institute of Creative Technologies (IOCT). Notably, his work was featured in the Sound Spheres exhibition at the Cooper-Hewitt Smithsonian Museum, highlighting the creative and inclusive dimensions of his research. Dr Feakes holds a PhD in Human-Computer Interaction and brings industrial experience in music and audio production into his academic role. He has no listed scientific awards or publications in the provided text. He advises students primarily through project supervision, though specific advisees are not named. There is no mention of research grants or labs, but his involvement with IOCT suggests collaborative, interdisciplinary research activity.

Bernd MeyerView profile

Professor

• Collective Behavior
• Artificial Intelligence
• Robotics
• +6 more

Bernd Meyer is a Professor and Assoc. Dean for Sustainability at Monash University's Faculty of Information Technology. His research spans collective behavior in biological systems, swarm robotics, edge computing, and intelligent diagrammatic systems. He has contributed extensively to understanding task allocation in social insects, noise-driven decision-making in collective systems, and the application of machine learning in resource-constrained environments. Key research themes include: Swarm Intelligence and Ant Colony Optimization Machine Learning for Environmental Sound Classification Self-Organized Infrastructure in Social Insects Adaptive Diagramming Tools for Education Recent work focuses on deep learning pipelines for edge devices and developing frameworks for multi-object tracking in biological systems. His contributions bridge computational models with real-world applications in robotics, sustainability, and educational technology.

• Computational Creativity
• Machine Learning in Music
• Neural Networks
• +4 more

Kaliakatsos Papakostas Maximos is an Associate Professor in AI in Music at the Department of Music Technology and Acoustics, Hellenic Mediterranean University, where he also directs the MSc program in Sound and Music Technologies. He collaborates with the Athena Research and Innovation Centre and the Cognitive and Computational Musicology Group at Aristotle University of Thessaloniki. His research focuses on computational creativity, neural networks in music understanding, evolutionary algorithms for harmony and rhythm generation, and interactive music systems. He develops applications like Genius Jam Tracks and contributes to open-source projects such as the CHAMELEON harmonisation system. His work bridges music theory, AI, and technology, with applications in mobile music tools and cognitive modeling. Research Interests : Computational Creativity through Machine Learning and Conceptual Blending Neural Networks in Music Generation and Analysis Evolutionary Computation for Musical Structures Human-Computer Interactive Systems for Improvisation Cognitive Models of Musical Perception Labs & Collaborations : Collaborating Researcher at Athena Research Centre Partnership with the Cognitive and Computational Musicology Group (Aristotle University) Development of open-source tools like CHAMELEON and GCT_py Grants & Projects : Not explicitly listed in provided texts, but his GitHub activity suggests ongoing research in AI music technologies.

Bernd ZimmermannView profile

Lecturer

• Audio Engineering
• Sound Design
• Music Technology
• +3 more

Bernd Zimmermann has been a Lecturer in Audio at the University of Art and Design Offenbach since 2004, where he also serves as Technical Manager in Film/Video. His expertise bridges audio engineering, film technology, and digital signal processing. Born 1964 in Offenbach am Main Trained as radio and television technician Over 35 years of professional experience in audio/visual technology and music Research & Teaching Focus: AudioLab, film technology, sound design, and multimedia systems. His work integrates technical innovation with artistic practice. Professional Background: Prior experience in CAD systems development and extensive stage/studio music production. Based in Offenbach's Main building, Room 12a/12b.

Johan VerhoevenView profile

Professor

• Speech Production
• Phonetics
• Phonology
• +3 more

Johan Verhoeven is a Professor in Experimental Phonetics at the Department of Language and Communication Science , City St George's, University of London. He has been affiliated with the university since 2006 and specializes in speech production , acoustic phonetics , and the phonetic description of motor speech disorders , particularly focusing on Foreign Accent Syndrome , Dysarthria , and Apraxia of Speech . Over his career, he has held academic positions at the University of Edinburgh, University of Oxford, Aarhus University, Hasselt University, and the University of Antwerp. Qualifications : BA, MA, AHSO, MSc, PhD in Germanic Languages and Linguistics, Neurolinguistics, and Phonetics. Research Areas : Speech Production, Phonetics, Phonology, Motor Speech Disorders, Neurolinguistics, and Acoustic Analysis. Collaborators : Allen Hirson, Peter Mariën, and Husen Owaida (PhD student). His recent publications (2025–2019) span topics like accent modulation in neurological conditions , intrinsic vowel pitch , articulatory asymmetry , and hearing impairment effects on speech . These works intersect with disciplines such as phonetics, neurology, psychiatry, and computational linguistics. Awards include a Fellowship at the Flemish Royal Academy of Belgium of Sciences and the Arts (2011–present) and membership in the International Phonetic Association . Teaching modules include Acoustic Phonetics, Biomedical Sciences, and Speech Acoustics and Perception.

Tokunbo OgunfunmiView profile

Professor

• Deep Learning
• Artificial Neural Networks
• Adaptive Signal Processing
• +7 more

Tokunbo Ogunfunmi is a Professor of Electrical and Computer Engineering and Director of the Information Processing and Machine Learning (IPML) Research Lab at Santa Clara University's School of Engineering. He served as Associate Dean for Research and Faculty Development from 2010-2014 and as Associate Dean for Mission, Culture and Inclusion from 2021-2024. Dr. Ogunfunmi holds a Ph.D. and M.S. from Stanford University (1990, 1984) and a B.S. from the University of Ife, Nigeria (1980). His research interests span deep learning, artificial neural networks, adaptive/nonlinear signal processing, digital signal processing, and multimedia signal processing with VLSI/DSP/FPGA implementations. Dr. Ogunfunmi has published 4 books and over 200 refereed journal and conference papers in these areas. His recent publications demonstrate strong focus on deep learning applications, hardware acceleration for neural networks, quaternion-based adaptive algorithms, and efficient video/speech processing techniques. Dr. Ogunfunmi has received numerous awards including the SCU Presidential Special Recognition Award (2020), IEEE Meritorious Service Awards (2021, 2013), Carnegie Foundation Visiting Professorships (2019, 2015), and multiple Best Paper Awards. He has served in significant editorial roles including Senior Associate Editor for IEEE Signal Processing Letters and Associate Editor for several IEEE Transactions. As an active member of the professional community, he is a Senior Member of IEEE, Member of Sigma Xi, and Member of AAAS. His industrial experience includes consulting for major companies such as Broadcom, AMD, NEC, AT&T Bell Labs, and NIKON Precision Research & Development. Dr. Ogunfunmi is also a registered professional engineer in California.

Katherine M. KinnairdView profile

Associate Professor

• Machine Learning
• Mathematics
• Cultural Analytics
• +8 more

Katherine M. Kinnaird is an Associate Professor of Computer Science and Statistical & Data Sciences at Smith College, where she chairs the Department of Computer Science. Her research focuses on developing domain-neutral methodologies for representing high-dimensional sequential data in low-dimensional spaces, with applications in music information retrieval, biology, literature, and health care. Education Ph.D. in Mathematics, Dartmouth College (2014) A.M. in Mathematics, Dartmouth College (2010) A.B. in Mathematics, Wellesley College (2008) Her work spans computational approaches to music structure analysis, text analysis, and data science education. Peer-reviewed publications include ISMIR 2016, ISMIR 2017, ISMIR 2019, and TISMIR 2021 papers, emphasizing multi-scale representations, similarity thresholds, and reproducibility in computational music research. Scientific Awards NSF GK-12 Fellowship (2013-2014) Dartmouth Graduate Teaching Award (2013) Dartmouth Graduate Community Service Award (2011) Project NExT Fellow (MAA Red '15 Cohort) Kinnaird founded the Data Science TRAIn Lab to train undergraduates in STEM research and has co-organized initiatives like the Women in Machine Learning (WiML) Workshop (2013) and Women in Music Information Retrieval (WiMIR, 2018). Her teaching philosophy emphasizes active learning and collaborative environments.

• Biomedical Signal Processing
• Sleep Disorders
• Spinal Cord Injury
• +3 more

Yolanda Castillo Escario is a researcher at the Institute for Bioengineering of Catalonia , specializing in the Biomedical Signal Processing and Interpretation group. Her work focuses on leveraging smartphone sensors and machine learning to assess sleep disorders, trunk function in spinal cord injury patients, and respiratory diagnostics through cough acoustics. Key research areas: Sleep-disordered breathing, Spinal cord injury rehabilitation, Smartphone-based health monitoring Recent publications highlight her contributions to deep learning for apnea detection , accelerometry for sleep position analysis , and acoustic profiling of respiratory diseases . Her interdisciplinary approach bridges biomedical engineering, neuroscience, and mobile health technologies. As part of IBEC's collaborative initiatives, she works with hospitals and industry partners to translate research into practical applications. Her projects include clinical trials for spinal cord injury rehabilitation and epidemiological studies on respiratory disease patterns.

Caroline TraubeView profile

Professor

• Digital Music
• Music Information Retrieval
• Performance Studies
• +9 more

Caroline Traube is a Professor at the Faculty of Music, Université de Montréal. She leads the Laboratoire de recherche sur le geste musicien (LRGM) and collaborates with interdisciplinary research groups including OICRM, CIRMMT, BRAMS, and LFO. Her work bridges musicology, acoustics, and digital music through empirical and creative approaches. Ph.D. in Music Technology, McGill University (2004) Engineer Degree in Electrical Engineering, Stanford University (2000) Civil Engineer (Telecommunications), Faculté Polytechnique de Mons (1996) Her research focuses on instrumental timbre , performance studies , and musician well-being , integrating acoustics , biomécanics , and digital music methodologies. Current projects include: 2025-2031: Music, Humanities, and Sciences in France (SSHRC-funded) 2025-2030: Analytical Approaches to Orchestration (FRQSC) 2024-2026: PianOptim - Optimal Piano Gestures (FRQSC) She supervises doctoral and master's students in topics ranging from virtual orchestration to Buddhist mantra performance . Her recent publications address digital performance tools and timbre analysis in piano, flute, and video game music contexts.

• Machine Learning
• Deep Learning
• Pattern Recognition
• +6 more

Angelina Njegus is a Full Professor at Singidunum University , where she has worked since 2005. She holds a Ph.D. in Business Studies and has extensive experience in both academia and industry, including consulting for IBM. Her research focuses on machine learning algorithms , deep learning , and Big Data Analytics , with applications in pattern recognition , tourism technology , and blockchain systems . Bachelor's: Faculty of Organizational Sciences, Information Systems (1994) Master's: Faculty of Organizational Sciences, Industrial Engineering (1999) Doctoral: Faculty of Business Studies (2003) Her recent publications (2023-2025) demonstrate expertise in metaheuristic optimization for machine learning, audio-visual emotion recognition , and cryptocurrency applications in tourism . She pioneered the Virtual University platform (MTVU) and contributes to agile methodologies in software development. Collaborations include projects with researchers from IEEE, Springer, and international institutions. Research interests span AI ethics in human resources , IoT integration in tourism , and security risks in cloud computing . Her work appears in journals like Complex & Intelligent Systems and conferences including ICPR and FG 2017 . She has authored books on software design patterns and information systems in tourism .

Barak PearlmutterView profile

Professor

• Adaptive systems
• Automatic differentiation
• Theoretical neurobiology
• +8 more

Professor Barak Pearlmutter is affiliated with Maynooth University in the Faculty of Science & Engineering . His research spans multiple domains including automatic differentiation , neural networks , machine learning , and neuroscience . He has contributed significantly to adaptive systems , brain imaging , and programming language design . Research Interests include: Adaptive systems, automatic differentiation, theoretical neurobiology, neural networks, machine learning, acoustic source separation/localization, neuroscience, brain imaging, programming language design, and computational neuroscience. Publications focus on applying algorithmic differentiation to machine learning, developing neural ODE models for biomedical signals, advancing sparse NMF techniques, and integrating functional programming with numerical methods. Collaborations span institutions like MIT, Oxford, and IEEE societies, with work in brain-computer interfaces , MEG source localization , and neuromodulation for tinnitus treatment. Technical Contributions include the DiffSharp AD library for .NET languages and foundational work on reverse-mode automatic differentiation in functional frameworks. His 2018 Journal of Machine Learning Research survey on AD remains a seminal reference in the field. Application Areas cover biomedical signal processing , optical brain-computer interfaces , cognitive modeling , and neural code optimization . His work intersects computer science, neuroscience, and mathematical computing through sparse decomposition and probabilistic modeling .

Christiane AntweilerView profile

Researcher

• Digital Signal Processing
• Speech and Audio Processing
• Acoustic System Identification
• +6 more

Dr.-Ing. Christiane Antweiler is a Researcher at the Institute of Communication Systems (IKS) , part of RWTH Aachen University 's Faculty of Electrical Engineering and Information Technology. Her work focuses on algorithms for digital speech/audio processing, multi-channel system identification, head-related transfer functions (HRTFs), and acoustic/echo cancellation in medical and teleconferencing applications. Research topics: Medical diagnostics signal processing , time-variant system identification , HRTF measurement , and real-time audio implementation . Current projects: Connected Visual Reality (CoVR) , Acoustic Tube Endoscopy , and Spatial Rendering with quasi-continuous HRTFs. Her 15 most recent publications (2025–2011) span adaptive filtering , acoustic system identification , and 3D audio rendering , with a strong emphasis on perfect sequence excitation and medical applications . She received the Best Paper Award at ICMCIS 2022 for her work on spectrum monitoring. Teaching roles include Information Theory and Source Coding (since 2009) and Digital Speech Processing 1 (2014–2015). Tools used: Matlab , C/C++ , and RT Proc for real-time audio processing.

Massimiliano Di LucaView profile

Associate Professor

• Human Perception
• Multisensory Integration
• Temporal Perception
• +7 more

Massimiliano Di Luca is an Associate Professor at the University of Birmingham, affiliated with both the School of Psychology and the School of Computer Science. His research focuses on understanding the mechanisms of human perception through psychophysical methods, computational modeling, and immersive technologies, particularly in the dynamic and temporal aspects of multisensory integration. He has made significant contributions to the study of virtual reality, haptics, and 3D shape perception. PhD in Cognitive Science (Brown University, 2006) Laurea in Psychology (Università di Trieste, 2000) Di Luca’s research spans human perception, haptics, and computational modeling, emphasizing the temporal dynamics of multisensory processing. His recent work includes the ARME project , Virtual Reality Lab , and collaborations on wearable haptic devices. He has developed models for perceptual recalibration, synchronization in musical ensembles, and tactile feedback optimization in XR systems. His publications highlight trends in virtual reality applications, haptic technology, and multisensory perception, with keywords like 3D shape , augmented reality , Bayesian modeling , and wearable haptics . Notable projects include TEXT: Text Entry for XR Trove and ARME . Scientific Awards: Turing Fellow (2021-2023), Fellow of the InterContinental Academia UBIAS (2021-2023). Di Luca mentors PhD, MSc, and BSc students in psychology and computer science. His grants include funding from EPSRC, BBSRC, Google, and UKRI for projects like ARME , Perception of Materials , and iCURE . He leads the Virtual Reality Lab and has contributed to the InterContinental Academia and Alan Turing Institute .