جست‌وجوی استاد و پژوهشگر

Rémi de Fleurianمشاهده پروفایل

پژوهشگر

• Music Cognition
• Empirical Aesthetics
• Data Science
• +۴ مورد دیگر

Rémi de Fleurian is a Data Scientist at Spotify, focusing on recommendations and user engagement. He concurrently holds a Visiting Researcher position at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Music Cognition Lab and Cognitive Science Research Group. He earned a PhD in Cognitive Science from Queen Mary (2020), an MSc in Music, Mind, and Brain from Goldsmiths, and a BSc in Osteopathic Studies from the British School of Osteopathy. His research explores musical expectation, aesthetic responses, and chills in music, employing behavioral, physiological, and computational methods. Notable work includes studies on valence dynamics and corpus-based analyses of chills. He also contributes to open science through data sharing and reproducible workflows. Recent articles focus on computational models of musical pleasure and rhythm complexity. His work has been featured in Quartz, Big Think, Inc., and IFLScience. Teaching includes guest lectures on research methods, music perception, and R programming at Queen Mary, alongside workshops on data visualization (ggplot2) and statistical tools.

• Art and Design Education
• Human-Computer Interaction
• Music Technology
• +۳ مورد دیگر

Andrea Guidi is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. His research focuses on intersections of creativity, technology, and education, particularly in art and music domains. He explores embodiment in digital musical instruments and the integration of AI into creative pedagogies. Key research areas include music technology, human-computer interaction, and educational applications of AI. Notable work includes projects like Magpick (an augmented guitar pick) and KOBI 3.0, a knowledge ecosystem for creativity research. His recent publications address AI-driven creativity education and embodied interaction in musical instruments. Guidi’s work emphasizes practical innovations alongside theoretical exploration, bridging technical systems with artistic practice. His articles highlight trends in interdisciplinary creativity research, digital instrument design, and the role of technology in shaping creative workflows and educational methodologies.

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

Shubhr Singh is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science, based in the Peter Landin building (Room CS 403). His research focuses on artificial intelligence, machine learning, audio signal processing, and their applications in bioacoustics and financial technology. He has contributed to advancements in graph neural networks (GNNs) for audio identification and classification, few-shot learning for bioacoustic event detection, and AI-driven innovations in banking systems. His work spans interdisciplinary areas such as perceptual music similarity metrics, intelligent audio control systems, and agricultural studies on crop growth optimization. Singh collaborates on international challenges like the DCASE series, exploring cutting-edge methods in sound event detection and domain adaptation. Key research trends in his publications include leveraging GNNs for complex audio analysis tasks, addressing data scarcity via few-shot learning, and integrating AI into real-world applications like neobanks and agricultural sustainability. His articles reflect a blend of theoretical advancements and practical implementations across diverse domains.

Carlos Vianna Lordeloمشاهده پروفایل

استاد مهمان

• Audio Signal Processing
• Machine Learning
• Music Information Retrieval
• +۳ مورد دیگر

Carlos Vianna Lordelo is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on Audio Signal Processing and Machine Learning, particularly in music-related applications such as source separation, instrument recognition, and deep learning architectures. He contributes actively to music information retrieval through projects like harmonic-percussive source separation and dataset development for folk music analysis. Key research interests include developing algorithms for music audio processing, adversarial domain adaptation, and CNN-based models. His work integrates theoretical advancements with practical applications in audio engineering and multimedia systems. Lordelo's GitHub repository showcases contributions to instrument prediction, source separation techniques, and open-source tools for music research. Notable projects include the 'source-separation-AHS' repository and collaborations on datasets like 'Tap & Fiddle'.

Craig Macdonaldمشاهده پروفایل

استاد

• Information Retrieval
• Web Information Retrieval
• Enterprise Information Retrieval
• +۴ مورد دیگر

Craig Macdonald is a Professor of Information Retrieval in the School of Computing Science at the University of Glasgow. His research focuses on information retrieval across diverse domains, including web, enterprise systems, social media, and smart cities. He has led projects like the SMART FP7 initiative, developing a search engine integrating sensor and social media data. Macdonald is a core developer of the Terrier IR platform and has contributed to TREC tracks such as Blog, Microblog, and Web. His PhD thesis addressed expertise search in enterprise environments, leading to practical systems like the SICSA expert search tool. He has organized conferences including CIKM 2011 and contributed to BCS IRSG and ACM SIGIR. His work spans theoretical advancements and applied systems, emphasizing reproducibility and open-source tools like PyTerrier. Education: BSc (1st Class Honours) in Computing Science (2004), PhD in Information Retrieval (completed). Research Interests: Expert search, blog/web/enterprise IR, sensor data integration, and recommendation systems. Notable projects include the SMART search engine for smart cities and contributions to TREC's Web track since 2014. He has also pioneered methods in query expansion, diversification, and sensitivity review systems. Publications (selected): Over 150+ papers in top venues like SIGIR, CIKM, and ECIR, focusing on retrieval models, recommendation systems, and evaluation frameworks. His work on RAG pipelines, pruning techniques, and contextual recommendation has driven practical advancements in AI-driven IR.

Xinyi Chenمشاهده پروفایل

پژوهشگر ارشد

• Astrophysics
• Cosmology
• Dark Energy
• +۶ مورد دیگر

Xinyi Chen is a Postdoctoral Fellow at the Perimeter Institute for Theoretical Physics. Her research focuses on large-scale structure cosmology, dark energy, and primordial non-Gaussianity, leveraging machine learning and computational tools to analyze galaxy surveys like DESI. She holds a Ph.D. from Yale University (2024), where she developed novel methods for initial condition reconstruction and optimal statistical analysis. Her work bridges astrophysics and interdisciplinary applications, including medical imaging and gravitational lensing studies of Hubble tension. Education: Ph.D. in Physics (2024), Yale University; B.S. in Physics, University of Michigan. Research Highlights: Hybrid reconstruction algorithms, BAO analysis pipelines, DESI collaboration, and alternative PNG constraint methods. Awards: 2021 NASA FINSST Award. She explores applications of machine learning in both astrophysics and medicine, as a Franke Interdisciplinary Fellow. Active in teaching and outreach, she also contributes to Yale's cosmology seminar organization and engages in creative writing and classical music.

Alba Curryمشاهده پروفایل

مدرس

• Early Chinese philosophy
• Ancient Greek philosophy
• ethics
• +۴ مورد دیگر

Dr Alba Curry is a Lecturer in Philosophy at the University of Leeds, affiliated with the School of Philosophy, Religion and History of Science. Her expertise spans early Chinese and Greek philosophy, ethics, emotions, feminist philosophy, and AI ethics. She holds a PhD in Comparative Literature from the University of California, Riverside, alongside degrees from the University of Glasgow and Fudan University. Dr Curry’s research bridges ancient philosophical traditions and contemporary issues, particularly exploring anger’s societal role and AI ethics. She collaborates with computer scientists and neuroscientists on AI-related projects, advocating for interdisciplinary dialogue. Her work emphasizes critical engagement with universalist philosophical impulses and contextual feminist critiques. Education: BA/MA Philosophy/Arts & Media Informatics, University of Glasgow MA Chinese Philosophy, Fudan University, Shanghai PhD Comparative Literature, University of California, Riverside Research interests focus on moral psychology, gendered emotions, and AI ethics. She explores how ancient philosophies like Daoism and Stoicism inform modern ethical dilemmas, particularly through analyses of pop culture (e.g., Taylor Swift) and technology (e.g., large language models). Her recent work critiques gender bias in AI emotion detection systems and advocates for ethical AI design. Professional affiliations include the Emotion and Society Lab and the Centre for Aesthetic, Moral and Political Philosophy. She advises students on ancient China/Greece, emotion philosophies, and feminist/AI ethics. Her interdisciplinary projects reflect her commitment to global philosophical dialogue and practical societal impact. Labs/Teams: Active in the Emotion and Society Lab, collaborating on AI ethics and emotion analysis projects.

Scott Dexterمشاهده پروفایل

مدرس

• Computer Science Education
• Ethics in Computing
• Free and Open Source Software (FOSS)
• +۴ مورد دیگر

Scott Dexter is a Lecturer in the Department of Computer Science and Engineering (CSE) at the University of Michigan, Ann Arbor, where he joined in 2024. Previously, he held academic roles at Brooklyn College and the Graduate Center of the City University of New York, as well as Alma College. His professional experience includes leadership roles as Director of General Education and Director of the Center for Teaching and Learning at Brooklyn College. Dr. Dexter earned his PhD in Computer Science from the University of Michigan in 1998. His teaching philosophy emphasizes inclusive pedagogy, incorporating strategies like Team-Based Learning (TBL), specifications grading, and principles from Grading for Equity and Inclusive Teaching . At Michigan, he has taught EECS 376 (Foundations of Computer Science) and EECS 203 (Discrete Mathematics), with upcoming courses in professional development and special topics. His teaching portfolio spans computer science fundamentals, ethics, and digital humanities. Research interests include ethics in computing, free and open-source software (FOSS), software aesthetics, and innovative teaching methodologies. Notable work includes studies on FOSS's role in creativity, ethics education for graduate students, and the application of learning theories in mathematics education. He has published extensively on pedagogy, including analyses of student feedback in linear algebra curricula and the design of collaborative learning tools. Outside academia, Dexter studies Persian music (kamancha and radif) and serves on the board of the CodeLab Foundation, a nonprofit advancing programming education. His interdisciplinary scholarship bridges technical fields with humanities, exemplified by his 2025 article exploring trans futurity in mid-century literature.

Eamonn Bellمشاهده پروفایل

استادیار

• Digital Humanities
• Music Technology History
• Computational Musicology
• +۳ مورد دیگر

Eamonn Bell is an Assistant Professor in the Department of Computer Science at Durham University, specializing in the digital humanities and the history of technology in music production. His research examines the intersection of computational methods with musicology, including early uses of digital computers in musical analysis and the cultural impact of formats like the CD. Previously, he was a postdoctoral Research Fellow at Trinity College Dublin (TCD), funded by the Irish Research Council (2019–2021). Bell holds a PhD in Music Theory from Columbia University (2019), where he explored computational approaches to musical scores under Joseph Dubiel, and a B.A. (Mod.) in Music and Mathematics from TCD (2013). His work spans topics such as the socio-technical history of CD audio formats, algorithmic creativity in early AI research, and the preservation of obsolete digital media. He teaches across computer science curricula, emphasizing interdisciplinary methods in digital humanities. Bell’s research has been supported by UK Research and Innovation (UKRI) and smaller institutional grants, and he contributes to projects like DRI initiatives for UK-based arts researchers. Key awards include the Government of Ireland Postdoctoral Fellowship. His teaching experience includes courses on digital music critique and music theory fundamentals at Columbia, alongside workshops in data visualization and music technology. Bell’s lab and team collaborations focus on emerging technologies in musicology, such as AI’s role in cultural analysis and the challenges of preserving born-digital artifacts.

Aidan Hoggمشاهده پروفایل

استادیار

• Spatial Acoustics
• Immersive Audio
• Statistical Signal Processing
• +۳ مورد دیگر

Aidan Hogg is an Assistant Professor/Lecturer in Computer Science at Queen Mary University of London (QMUL) and an Honorary Research Associate at Imperial College London. His primary affiliation is with the Centre for Digital Music (C4DM) and the School of Electronic Engineering and Computer Science at QMUL. He co-leads the Virtual, Immersive, Augmented and Binaural Audio Lab (VIABAL) . Education: PhD and MEng from Imperial College London, specializing in engineering and computer science disciplines. His research focuses on deep learning for spatial acoustics , immersive audio , and statistical signal processing with applications to speech and audio systems. Key areas include HRTF upsampling , speaker diarization , and acoustic localization . Grants: Secured funding such as the Online Speech Enhancement in Scenarios with Low Direct-to-Reverberant-Ratio grant (£65,621 from L-ACOUSTICS UK LIMITED, 2024–2025). His work bridges academia and industry, particularly in audio engineering and AI-driven audio solutions. Labs/Teams: Active in the Centre for Multimodal AI and collaborates with institutions like the Central Conservatory of Music, China. His research outputs include datasets like the SONICOM HRTF Dataset and contributions to conferences such as DAFX, ICASSP, and WASPAA.

Vincent Lostanlenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Audio Content Analysis
• Computer Music
• +۱۱ مورد دیگر

Vincent Lostanlen is a Researcher (chargé de recherche) at CNRS, affiliated with the LS2N laboratory located at Centrale Nantes engineering school. He also serves as a visiting scholar at New York University. His research focuses on developing artificial intelligence for audio analysis, emphasizing machine listening systems, bioacoustic monitoring, and computer music. Key projects include the BirdVox platform for avian migration monitoring and the Kymatio Python library for wavelet scattering transforms. Education: Ph.D. in Mathematics from École Normale Supérieure (2017), supervised by Stéphane Mallat; MSc in Acoustics & Music Informatics from Université Pierre-et-Marie-Curie (2013); Dipl. Ing. in Signal Processing from Télécom ParisTech (2013). Research interests span audio representation learning, environmental sensor networks, and generative models for sound. Notable contributions include the SONYC-UST urban sound dataset and the PCEN signal frontend for robust acoustic sensing. Scientific awards include the 2019 AFIM Young Researcher Award, 2016 SFA Best Poster Prize, and 2015 IEEE MLSP Best Paper Award. He advises graduate students across computer science and music technology domains, with former advisees working in academia and industry. Current activities include developing green AI for audio processing and collaborating on bioacoustic sensor networks for biodiversity monitoring. His work bridges signal processing theory with practical applications in music, ecology, and biomedical engineering.

Fabian C. Mossمشاهده پروفایل

استادیار

• Computational Musicology
• Music Theory
• Digital Humanities
• +۴ مورد دیگر

Fabian C. Moss is an Assistant Professor for Digital Music Philology and Music Theory at Julius-Maximilians-Universität Würzburg (JMU), Germany. His research bridges humanities and computational methods, focusing on interdisciplinary approaches to music structure, including musicology, mathematics, data science, and digital humanities. He leads projects such as DigiMusTh (building a digital collection of historical music theory texts) and Digital Choro (exploring Brazil’s musical heritage). He is also part of the Zentrum für Philologie und Digitalität (ZPD) and the Graduate School Humanities (GSH) at JMU. Before joining JMU, Moss held positions as a Research Fellow in Cultural Analytics at the University of Amsterdam and as a postdoctoral and doctoral researcher at École Polytechnique Fédérale de Lausanne (EPFL). He has conducted research visits at MIT and Escola Superior de Música de Catalunya (ESMUC). His work emphasizes computational modeling, corpus studies, and historical music analysis, with a focus on tonal evolution, harmonic progressions, and digital tools for musicology. Moss teaches courses in computational musicology, music theory, and digital tools. He serves on advisory boards for journals like Computational Humanities Research and Analitica , and actively contributes to conferences and workshops. His research outputs include datasets, software tools (e.g., MonodiKit, midiVERTO), and interdisciplinary analyses of musical corpora.

Kanad Mandkeمشاهده پروفایل

پژوهشگر

• Developmental Psychology
• Cognitive Neuroscience
• Speech Processing
• +۳ مورد دیگر

Dr. Kanad Mandke is a Researcher in the Department of Psychology at the University of Cambridge. His work focuses on developmental neuroscience, particularly investigating the neural mechanisms underlying speech processing and language acquisition in children with and without developmental disorders like dyslexia. His research employs advanced neuroimaging techniques including EEG, MEG, and computational modeling to explore how auditory and multisensory information is encoded in the brain. Key research areas include: Rhythmic auditory processing and its role in literacy development Neural correlates of speech perception deficits in dyslexia Development of binaural hearing capabilities Machine learning applications in decoding neural speech representations His publications demonstrate a strong focus on understanding atypical brain responses to speech stimuli in pediatric populations. Recent studies have explored beta-band EEG oscillations, low-frequency cortical encoding, and temporal fine structure sensitivity in dyslexia. Collaborative work includes multilayer network analysis of brain connectivity in musicians and infants' early language acquisition processes. Dr. Mandke's research also addresses translational aspects such as identifying neurophysiological markers for developmental disorders and advancing our understanding of cognitive reserve mechanisms through informational complexity analysis.

Sabrina Caldwellمشاهده پروفایل

مدرس ارشد

• Human centred computing
• human-AI teaming
• digital image authentication
• +۶ مورد دیگر

Sabrina Caldwell serves as a Senior Lecturer and Researcher at The Australian National University's School of Computing. As Co-chair of JPEG Trust within the ISO/IEC JTC 1/SC 29/WG 1 working group, she has led the development of international standard ISO/IEC 21617-1 for media trustworthiness assessment, scheduled for publication in October 2024. Her work bridges academic research with global standardization efforts in digital media security. Her research interests focus on two interconnected domains: digital image authentication (particularly distinguishing authentic photographs from AI-manipulated content) and physiological signal processing using neural networks. Within human-AI teaming, she investigates deception detection through multimodal analysis of nonverbal behavior and develops frameworks for assessing the credibility of visual media in the age of generative AI. Her fingerprint reveals strong concentrations in neural networks (100%), physiological signal processing (85%), and affective computing (30%). Analysis of her 29 research outputs shows increasing emphasis on multimodal deception detection and media trustworthiness standards since 2020. Her work demonstrates consistent collaboration with international researchers like Temmermans, Bhowmik, and Kyburz, with notable projects including MEC23: Advanced Biometrics and C2 Sociotechnical Experimentation. The publications reveal a strategic shift toward practical standardization frameworks after 2022, coinciding with her JPEG Trust leadership role. Caldwell holds active research supervision registration and leads projects including the 2023-2026 Australian government travel grant for international standards participation and the 2024 Human-machine aesthetics seed grant. Her projects consistently address real-world applications of computing for social good, particularly in media trust and human-centered biometrics.

• Human-Computer Interaction
• Persuasive Technology
• Information Security
• +۲ مورد دیگر

Dr. Jim Marquardson is an Assistant Professor in the Information Assurance & Cyber Defense department at Northern Michigan University's College of Business . With a Ph.D. in Management Information Systems from the University of Arizona, his research focuses on lie detection with technology, human-computer interaction, persuasive technology, information security, data analytics, and educational methodologies. Ph.D. in Management Information Systems (University of Arizona) Teaching credentials in cybersecurity, network simulation, and virtual labs Industry experience at Zions Bank and Exxon Mobil Research Interests revolve around integrating hands-on learning into cybersecurity education, including Amazon Web Services Academy implementations, GitHub-based tutorials , and Security Operations Center (SOC) frameworks . His work bridges academic theory with practical applications in information assurance and network design. Publications highlight trends in cybersecurity pedagogy , cloud computing education , risk management , and persistent skill gaps in entry-level cybersecurity roles. Articles from 2017-2025 demonstrate sustained focus on improving educational outcomes through simulation , AI integration , and collaborative learning techniques . Teaching Philosophy emphasizes practical experience , with students actively building computer networks , creating virtual machines , and developing security software . His background in corporate IT informs real-world applications of theoretical concepts. Mountain Biking Baking College Football Ethical Hacking Programming Music