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Mark Liberman is a Trustee Professor at the University of Pennsylvania , holding appointments in the Department of Linguistics and Department of Computer and Information Science . He serves as Director of the Linguistic Data Consortium and Faculty Director of Ware College House . His career spans linguistics, speech technology, and computational methods. Education: Harvard University (1965-1969), MIT (M.S. 1972, Ph.D. 1975) Professional Experience: AT&T Bell Laboratories (1975-1990), University of Pennsylvania (1990-present) His research interests include: Corpus-based Phonetics : Analyzing speech patterns via large-scale datasets. Clinical Applications : Developing speech biomarkers for neurodegenerative diseases. Tonal Phonology : Studying lexical tone and intonation in languages like Yoruba and Mandarin. Formal Annotation Models : Creating frameworks for linguistic data standardization. Recent publications highlight automated speech analysis, cross-linguistic prosody, and digital biomarkers for conditions like ALS-FTD and Alzheimer’s. His collaborations span computational linguistics , neurology , and cognitive science . Scientific awards include the IEEE James L. Flanagan Award (2017), Antonio Zampolli Prize (2010), and fellowships from the AAAS and Linguistic Society of America . He advises PhD students May Chan and Jonathan Him Nok Lee and contributes to editorial boards for journals like Cognition and Annual Review of Linguistics . His work bridges speech science , language technology , and neurocognitive research .

• Corpus-based Phonetics
• Speech and Language Technology
• Clinical Applications of Linguistics
• +6 more

Jianjing Kuang is an Associate Professor of Linguistics at the University of Pennsylvania, affiliated with the School of Arts and Sciences. As Director of the Penn Phonetics Laboratory, they lead research in phonetics, laboratory phonology, and tonal language studies. Kuang holds a Ph.D. from UCLA (2013) and specializes in the interplay between production and perception in speech, particularly focusing on tonal systems, prosody, and cross-linguistic fieldwork. Their work integrates behavioral experiments, corpus studies, and computational modeling to explore phonological contrasts, voice quality, and sound change. Key affiliations include MindCORE and the Center for East Asian Studies. Research interests include multidimensional cues in tone processing, glottal articulations, mapping production-perception relationships, and prosodic sentence processing. Kuang has conducted fieldwork on languages such as Yi, Q’anjob’al, and Mandarin. Their studies address topics like tonal splitting in Yi, cue-changing in Korean stops, and prosodic patterns in Mayan languages. Recent work explores voice quality’s role in pitch perception and prosodic boundary detection. Awards and grants are not explicitly listed, but Kuang’s extensive publications (over 50 peer-reviewed papers) reflect sustained academic impact. They advise graduate students in phonetics and phonology, with active collaborations in computational linguistics and speech technology. The Penn Phonetics Laboratory, under Kuang’s direction, emphasizes interdisciplinary research bridging experimental and theoretical phonetics.

• Phonetics
• Tonal Languages
• Speech Production and Perception
• +4 more

Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.

• Auditory Perception
• Neural Oscillations
• Auditory-Motor Coupling
• +5 more

Dr. Etienne Gaudrain is a permanent Researcher at the Lyon Neuroscience Research Center (CRNL) , Université Lyon 1, France, and a Visiting Researcher at the University Medical Center Groningen (UMCG), Netherlands. His work focuses on auditory perception, particularly in cochlear implant users, and he has co-supervised multiple PhD and Master’s students across institutions. Current affiliation: Université Lyon 1, Faculty of Medical Sciences Secondary affiliation: University Medical Center Groningen (UMCG) Education: PhD in Acoustics, Université Lyon 1 (2008) Habilitation à Diriger des Recherches (HDR), Université Lyon 1 (2021) M.Sc. in Acoustics, École Centrale de Lyon / Université Lyon 1 (2004) M.Sc. in Engineering, École Centrale de Lyon (2004) Research interests span auditory scene analysis, speech perception, cochlear implants, pitch and vocal tract length perception, and perceptual integration of speech features. He has contributed to understanding how cochlear implant users segregate concurrent speech and perceive vocal characteristics, developing novel methodologies like Zebra-speech to study sequential aspects of auditory processing. His work has involved collaborations with institutions in France, UK, Germany, Switzerland, and the Netherlands. He has served as an ad-hoc reviewer for journals such as The Journal of the Acoustical Society of America and organized multiple international workshops on speech-in-noise research. Supervision and Grants: Supervised 5 PhD students (2012–2025), including Floris Rotteveel (dual PhD 2022–2025) and Moïra-Phoebé Huet (2016–2019) Coordinated or contributed to grants totaling over €2.5 million, including an NWO VICI grant (€1.5M, 2017) and an ERA-NET CoSySpeech grant (€844k, 2021) Labs and Teams: Auditory Cognition and Psychoacoustics team, CRNL, Lyon Speech Perception group, UMCG, Groningen Collaborations with MRC Cognition and Brain Sciences Unit (Cambridge, UK), CNBH (Cambridge), and international researchers

• Auditory Scene Analysis
• Speech Perception
• Cochlear Implants
• +5 more

Bistra Andreeva is a full Professor of Phonetics and Phonology at Saarland University, Department of Language Science and Technology. She is also a principal investigator of the DFG-funded project “Judeo-Spanish in Bulgaria: a contact language between archaism and innovation” (2022-2025) and leads or co-leads several other externally funded projects on prosody, information structure, and language contact. She teaches and has taught at Saarland University, Sofia University “St. Kliment Ohridski”, and other European universities. Education & Academic Background Habilitation (kumulative Habilitationsschrift): “Contrastive Prosody: Bulgarian vs. German”, Saarland University, 2016. PhD (Dr. phil.): “Zur Phonetik und Phonologie der Intonation der Sofioter-Varietät des Bulgarischen”, Saarland University, 2007. Research Interests Andreeva’s research focuses on the phonetics and phonology of intonation and rhythm, cross-language and individual differences in the production and perception of syllabic prominence, the relation between intonation and information structure, minority languages (especially Judeo-Spanish in Bulgaria), and the interaction between information density and prosodic structure. Her methodological expertise includes experimental phonetics, corpus-based studies, and acoustic analysis. Major Projects SFB 1102 C1: “Information Density and the Predictability of Phonetic Structure” (DFG, 2014-2026) JuSpa: “Judeo-Spanish in Bulgaria: a contact language between archaism and innovation” (DFG, 2022-2025) Prosodic aspects of Bulgarian compared to other languages with lexical stress (Bulgarian National Science Fund, 2019-2024) Prosodic manifestations of focus in French and English from a Bulgarian point of view (DAAD & Sofia University, 2021-2023) Scientific Awards & Recognition While no major named prizes are listed, Andreeva’s work has been continuously supported by competitive grants from the German Research Foundation (DFG), DAAD, and Bulgarian National Science Fund. Her publications appear in top-tier journals and conference proceedings (Journal of the Acoustical Society of America, Interspeech, Speech Prosody, International Congress of Phonetic Sciences). Teaching & Supervision She offers a broad spectrum of courses including Experimental Phonetics, Prosody, Phonetics & Phonology of Slavic Languages, Introduction to Instrumental Phonetics, and seminars on language contact. She has supervised numerous BA, MA, and PhD theses and regularly teaches at the PhD School of Sofia University. Labs & Teams She is affiliated with the Phonetics Laboratory at Saarland University and collaborates closely with the Institute for Bulgarian Language (Bulgarian Academy of Sciences), working in interdisciplinary teams involving phoneticians, computational linguists, and field linguists.

• Phonetics and phonology of intonation and rhythm
• Cross-language and individual differences in syllabic prominence
• Intonation and information structure
• +3 more

Dr. Andrew Hines is a Researcher at the School of Computer Science, University College Dublin, specializing in machine learning applications for signal processing in speech, audio, and video domains. His work focuses on Quality of Experience (QoE) modeling, speech quality assessment, and immersive media analysis. He has held leadership roles in European COST Actions like Qualinet and CryptoAction, and previously worked in industry as a Director of Engineering. University: University College Dublin Role: Director of Research, Innovation and Impact Key Collaborations: IEEE (Senior Member), Audio Engineering Society (Ireland) Research interests center on machine learning for QoE optimization, audio-visual integration, and healthcare applications like heart sound classification and stroke rehabilitation. His recent publications explore self-supervised learning, neural speech codecs, and contextual factors in speech/audio quality assessment. Scientific contributions include awards like IEEE Senior Membership, and his work spans both academic research and industrial engineering in finance and aviation sectors. He leads the QxLab research team at UCD and develops open-source platforms such as WARP-Q and AQP for quality metrics.

• Machine Learning
• Signal Processing
• Speech Processing
• +3 more

Song Ma is a Professor of Finance and Entrepreneurship at Yale School of Management and a Faculty Research Fellow at the National Bureau of Economic Research (NBER). He is also an affiliated faculty member at Yale Law School Center for the Study of Corporate Law and Yale SOM Program on Entrepreneurship, having joined Yale SOM Faculty in 2016. His educational background includes: PhD in Finance from Duke University's Fuqua School of Business (2016) BA in Economics from Zhejiang University (2010) Professor Ma's research primarily focuses on innovation economics, entrepreneurship, financial economics, AI, and big data. His work extends to corporate strategy, industrial organization, antitrust, labor, and business law. He has made significant contributions to understanding how innovation interacts with financial markets, corporate strategy, and competition policy, particularly through his influential 'Killer Acquisitions' paper which has been cited in Congressional antitrust reports and lawsuits against major tech companies. His recent publications demonstrate an interdisciplinary approach combining finance, economics, and data science methodologies. Many papers examine the intersection of innovation and corporate finance, with increasing incorporation of AI and big data techniques as seen in his video analysis research. His work shows evolution from traditional finance topics toward more policy-relevant research with real-world impact on antitrust regulation and innovation policy. Professor Ma has received numerous prestigious awards: 2023 Best Paper Award, China International Conference in Finance 2022 Best Paper on Competition Economics, Association of Competition Economics 2022 Jerry S. Cohen Award for Antitrust Scholarship 2021 GARP Best Paper in Risk Management Award 40 Under 40 Best Business School Professors by Poets & Quants (2021) Robert F. Lanzillotti Prize for Antitrust Economics (2020) Jensen Prize for Best Paper on Corporate Finance (2019) In teaching, Professor Ma delivers popular courses including 'Entrepreneurial Finance,' 'Venture Capital and Private Equity,' and 'Finance and the Society.' He co-organizes WEFI (Workshop on Entrepreneurial Finance and Innovation), a bi-weekly virtual research forum. His research has been referenced by major regulatory bodies worldwide including the FTC, EU Competition Commission, and UK Competition and Markets Authority, and featured in leading media outlets like Wall Street Journal and New York Times. Professor Ma actively incorporates new data science technologies into his empirical economic research, focusing on unstructured data analysis and machine learning applications.

• Innovation Economics
• Entrepreneurship
• Financial Economics
• +7 more

Bryan Pardo is a Professor of Computer Science at Northwestern University and head of the Interactive Audio Lab. He co-directs the Northwestern Center for Human Computer Interaction + Design and chairs the Computer Science Diversity Committee. He teaches courses in Deep Learning, Machine Learning, Generative Modeling, and Digital Music Instrument Design. PhD in Computer Science and Engineering, University of Michigan MMus in Jazz and Improvisation, University of Michigan MS in Computer Science, Ohio State University BMus in Jazz Composition, Ohio State University His research focuses on machine understanding and manipulation of sound, particularly in music and speech domains. Key areas include Machine Learning (e.g., automated gradient clipping), Signal Processing (e.g., Multi-scale Common-fate Transform), and Human Computer Interaction. Applications involve inclusive audio interfaces, audio search engines, source separation, natural language-controlled audio effects, privacy-preserving adversarial attacks on voice recognition, and music co-creation tools. Recent publications highlight advancements in neural watermarking (MaskMark), masked acoustic modeling (VampNet), and real-time adversarial privacy systems for speech. His lab's work has been applied in Adobe's AI-powered audio editor and Lexie B2 hearing aids. Scientific Awards: $1.8 million NSF Future of Work award $440K NSF grant for accessible music programming $200K Toyota grant $100K Sony grant TorchCrepe pitch tracker: 20 million+ downloads Bryan Pardo advises PhD student Max Morrison and collaborates with researchers like Patrick O'Reilly, Zeyu Jin, and Prem Seetharaman. His lab develops technologies for blind and visually impaired audio creators, including HaptEQ and Eyes-free tools.

• Machine Learning
• Generative Modeling
• Signal Processing
• +7 more

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.

• Cognitive Psychology
• Perception
• Face Recognition
• +5 more

Paul Boersma is a Professor of Phonetic Sciences at the University of Amsterdam within the Faculty of Humanities. His research explores how phonetic, phonological, and morphological phenomena emerge through computational modeling using artificial neural networks and Optimality Theory, with a focus on multi-level constraint interactions and distributional learning. University of Amsterdam Faculty of Humanities Phonetic Sciences Key research areas include: Computational Modeling : Simulations of phonological category emergence from phonetic data Optimality Theory : Gradual Learning Algorithm applications BiPhon Framework : Parallel bidirectional phonology/phonetics models Statistical Learning : Cross-situational and distributional learning mechanisms Recent publications emphasize: 2025: Inclusive speech recognition systems using Whisper model 2025: F0 ratio analysis for creaky voice diagnostics 2024: Prosodic clitics in child speech and checked tones in Shanghai Chinese 2023: Distributional learning in developmental language disorder contexts 2022: Substance-free phonological features and ghost segment phenomena He has also contributed extensively to the Praat software for phonetic analysis, with continuous updates since 1993.

• Phonetics
• Phonology
• Computational Linguistics
• +7 more

Josh McDermott is a Professor in the Department of Brain and Cognitive Sciences at MIT and an Associate Investigator at the McGovern Institute. He holds roles as Associate Department Head and Principal Investigator of the Laboratory for Computational Audition. His work bridges psychology, neuroscience, and engineering to study auditory perception, with a focus on sound interpretation, hearing impairment treatments, and machine hearing systems. Education includes a B.A. from Harvard (summa cum laude), an MPhil from University College London, and a PhD from MIT. Postdoctoral training included NYU and the University of Minnesota. Research interests encompass computational principles of sound perception, natural sound statistics, music cognition, and machine hearing. Key areas include sound localization, auditory scene analysis, and the role of generative models in perception. Recent publications highlight advancements in auditory neural networks, cross-cultural music perception, and noise schema processing. Awards include the Troland Research Award, BCS Excellence in Advising, and NSF CAREER Award. Advising includes over 20 graduate students and postdocs, with notable contributions to auditory neuroscience and machine learning. Major grants support projects on auditory models and sensory systems. The lab develops tools like cochleagram generation and headphone screening software. The Laboratory for Computational Audition operates at MIT, focusing on biological and computational approaches to hearing. Collaborations span engineering, psychology, and neuroscience to advance understanding of auditory processing.

• Auditory Neuroscience
• Psychology
• Machine Learning
• +5 more

Erik W. Willis is an Associate Professor in the Department of Spanish and Portuguese at Indiana University Bloomington, where he also serves as Director of Graduate Studies and Director of the Hispanic Linguistics Program. His work focuses on Spanish phonetics, phonology, and dialectal variation, particularly in Caribbean and Mexican Spanish varieties. He employs acoustic analysis and laboratory methods to study rhotic contrasts, intonational patterns, and sociolinguistic phenomena. Ph.D., University of Illinois at Urbana-Champaign (2003) M.A., Brigham Young University (1997) B.A., Brigham Young University (1994) His research spans multiple subfields including: Acoustic characterization of Spanish rhotics Intonational system analysis using Sp_ToBI Second language phonology acquisition Web-based language assessment tools Vowel system variations in Spanish dialects Contextual phonetic realizations in Caribbean Spanish Recent publications demonstrate systematic variation in coda liquids, tap-trill contrasts, and intonational patterns across Dominican, Puerto Rican, and Mexican Spanish. His work combines empirical acoustic analysis with sociolinguistic frameworks to document both phonetic realizations and their pragmatic functions in spoken language.

• Spanish Phonetics
• Spanish Phonology
• Laboratory Phonology
• +5 more

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.

• Machine Learning & Artificial Intelligence
• Probabilistic Modelling
• Neuro-Symbolic Modelling
• +7 more

Frank Russo is a Professor in the Department of Psychology at Toronto Metropolitan University, where he holds the NSERC-Sonova Senior Research Chair in Auditory Cognitive Neuroscience. He leads the Science of Music Auditory Research and Technology (SMART) Lab and holds affiliate and adjunct positions at the University Health Network and the University of Toronto, respectively. Research Interests: Dr. Russo's work lies at the intersection of auditory cognitive neuroscience, music psychology, and rehabilitation. His research explores how humans perceive music and speech, particularly under challenging conditions such as hearing loss or non-native accents. He investigates the cognitive and neural mechanisms of listening effort, emotional speech processing, and the social and therapeutic benefits of music, especially through community choirs and digital interventions. Publication Trends: His recent publications emphasize objective measurement of listening effort using functional near-infrared spectroscopy (fNIRS), music-based interventions for Parkinson’s disease and dementia, vocal and emotional responses to singing, and multisensory integration in beat perception. The work is highly translational, bridging basic cognitive neuroscience with clinical and community applications. Scientific Awards and Honors: NSERC-Sonova Senior Research Chair in Auditory Cognitive Neuroscience Fellow of the Canadian Psychological Association Fellow of Massey College Fellow of the Canadian Society for Brain, Behavior and Cognitive Science Past President of the Canadian Acoustical Association Advising and Grants: Dr. Russo actively mentors students and researchers, as evidenced by his co-authorship with numerous junior colleagues. He has secured major funding through NSERC and industry partnerships, enabling the development of impactful technologies such as hearing aid algorithms, sensory substitution systems, and digital therapeutics. His SingWell project fosters collaboration across academic, clinical, and community sectors. Labs and Teams: He directs the SMART Lab at Toronto Metropolitan University, a hub for interdisciplinary research on music, hearing, and cognition. The lab collaborates extensively with KITE Research Institute, Rehabilitation Sciences at the University of Toronto, and various community organizations focused on aging, hearing loss, and neurodegenerative conditions.

• Auditory Cognitive Neuroscience
• Music Psychology
• Music Medicine
• +12 more

Jennifer Beavers, Ph.D. , is an Associate Professor of Music Theory at the University of Texas at San Antonio (UTSA) in the College of Liberal and Fine Arts. Her research intersects early twentieth-century music analysis, timbre/orchestration studies, and music theory pedagogy, with a focus on Maurice Ravel’s compositions and curriculum innovation. Awarded Faculty Mentor Award for Undergraduate Research (2019) Co-founder of UTSA Music Theory Club initiatives Active presenter at Society for Music Theory , American Musicological Society , and College Music Society conferences Research Themes : Analytical approaches to Ravel’s neurological decline, timbre as structural marker, flipped pedagogy, and interdisciplinary collaborations. Her publications span journals like Music Theory Online and Indiana Theory Review , emphasizing timbral transformations and pedagogical innovation. Scientific Awards : Faculty Mentor Award for Undergraduate Research (2019) Teaching Contributions : Courses include Aural Skills , Basic Skills , 20th-Century Analysis , and graduate Research Methods . Her pedagogical research addresses improvisation, pitch-matching, and AP curriculum alignment.

• Early Twentieth-Century Music Analysis
• Music Theory Pedagogy
• Timbre Analysis
• +9 more