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Nils PetersView profile

Assistant Professor

• Audio Signal Processing
• Spatial Audio
• Machine Learning
• +5 more

Dr. Nils Peters is an Assistant Professor in Electronic & Electrical Engineering at Trinity College Dublin (TCD). He specializes in audio signal processing, spatial audio, and machine learning applications in acoustics. Previously, he held roles at Qualcomm and the International Audio Laboratories Erlangen (Fraunhofer IIS/FAU). His research spans psychoacoustics, XR technologies, and accessibility in audio systems. Education: PhD in Music Technology, McGill University (Canada) Postdoctoral work at UC Berkeley MSc in Electrical and Audio Engineering, Graz University of Technology (Austria) Research Interests: Dr. Peters focuses on bridging audio engineering with human perception. Key areas include spatial audio rendering, emotion preservation in anonymized speech, and acoustic localization systems. His work integrates machine learning for audio signal analysis and IoT applications in sound environments. Recognition: 2023 Best Paper Award at IS2 (Internet of Sounds) 2023 Teaching Excellence Award (FAU) Multiple Qualcomm Qualstar Diamond Awards (2015-2020) Advising & Leadership: Serves as AES Co-Chair for Spatial Audio and steering board member of IEEE's Internet of Sounds Initiative. Advises on audio engineering projects and lab courses at TCD. Labs & Teams: Collaborates with Qualcomm, Fraunhofer IIS, and UC Berkeley teams. Active in interdisciplinary projects involving 3D audio, XR, and accessibility technologies.

Sanjeev P. KhudanpurView profile

Associate Professor

• Speech Recognition
• Machine Translation
• Natural Language Processing
• +3 more

Sanjeev P. Khudanpur is an Associate Professor at Johns Hopkins University (JHU), holding dual appointments in the Department of Electrical and Computer Engineering and the Department of Computer Science (secondary). He is affiliated with the Center for Language and Speech Processing (CLSP) and the Human Language Technology Center of Excellence. His research focuses on applying information-theoretic methods to human language technologies, including automatic speech recognition, machine translation, and natural language processing. He has contributed to foundational work in pronunciation modeling, statistical language models, and cross-lingual adaptation techniques. Dr. Khudanpur has authored over 50 peer-reviewed publications and edited volumes, such as Mathematical Foundations of Speech and Language Processing . He received his B.Tech. from IIT Bombay (1988) and his Ph.D. from the University of Maryland, College Park (1997). Since joining JHU in 1996, he has held roles including Associate Research Scientist at CLSP and Assistant Professor in ECE/CS before becoming Associate Professor in 2008. His teaching includes courses on information theory, random signal analysis, and speech/text information extraction. Dr. Khudanpur’s awards include recognition of two of his papers among the most impactful in ICASSP’s 50-year history. He advises graduate students in areas like multilingual speech systems and adversarial ML defenses, and collaborates extensively through CLSP workshops and industry partnerships. Current research emphasizes robust ASR in complex acoustic environments, end-to-end systems, and low-resource language technologies.

Eunsol ChoiView profile

Assistant Professor

• Natural Language Processing (NLP)
• Machine Learning
• Question Answering (QA)
• +6 more

Eunsol Choi is an Assistant Professor in Computer Science at New York University's Courant Institute and the Data Science program. Previously, she held an Assistant Professor position at the University of Texas at Austin (2020–2023) and was a researcher at Google AI (New York City). She earned her Ph.D. in Computer Science from the University of Washington, advised by Luke Zettlemoyer and Yejin Choi. Her research focuses on advancing NLP and machine learning, particularly in areas like continual learning, long-form question answering (QA), and spoken language processing. Key projects include developing benchmarks like PropMEND, SituatedQA, and AmbigDocs, as well as exploring model robustness and human-LM interaction. Her work emphasizes practical applications, such as improving QA systems, reducing model hallucinations, and scaling style-prompted TTS datasets. Research Interests : Continual Learning & Knowledge Editing Long-form QA and Multimodal Reasoning Human-Machine Interaction in NLP Spoken Language and Speech-to-Speech Translation Benchmark Design for NLP Evaluation Her lab's research has been supported by grants from Google, Open Philanthropy, Cisco, Sony, Home Depot, Apple, and the NSF. Notable awards include the Outstanding Paper Award at EMNLP 2021 for SituatedQA. Key Contributions : Proposed frameworks for knowledge propagation in LLMs. Designed datasets addressing ambiguity in QA (e.g., AmbigDocs). Advanced TTS systems with rich style annotations (e.g., ParaSpeechCaps). Lab & Students : Her research group (nicknamed by students as the (E)xplaining and (U)nderstanding (N)ature and (S)tructure/Synthesis (O)f (L)anguage lab) has produced over 20 Ph.D. and undergraduate researchers, many advancing to roles at top tech companies and academia.

Paul GelusoView profile

Assistant Professor

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

Paul Geluso is an Assistant Professor and Director of the Music Technology Program at New York University's Steinhardt School of Culture, Education, and Human Development. His work bridges theoretical, practical, and artistic approaches to sound recording and reproduction. He has extensive industry experience as a producer, recording engineer, and mastering engineer on hundreds of commercially released recordings, including Grammy and Latin Grammy-nominated projects. His research focuses on immersive audio technologies for multi-channel systems, recently co-editing a seminal text on binaural and spatial audio. Geluso holds a BSc in Electrical Engineering from New Jersey Institute of Technology (1988) and a Master of Music in Music Technology from NYU (2000). Education: Bachelor of Science in Electrical Engineering, New Jersey Institute of Technology, 1988 Master of Music in Music Technology, New York University, 2000 Research Interests: Geluso explores innovative methods for capturing, mixing, and processing immersive audio. His work emphasizes practical applications of spatial audio in film, music production, and artistic installations. Collaborations with contemporary artists have led to exhibitions at institutions like The New Museum, Cooper-Hewitt School of Design, and the London SPACE gallery. Awards & Grants: New York State Council on the Arts Individual Artist Grant Meet the Composer Grant Professional Experience: Geluso has taught at Bard College and the Peabody Institute, and served as Chief Sound Engineer at Harvestworks Digital Media Arts for over 20 years. His sound art installations have been showcased at major festivals and venues including the New York Electronic Arts Festival and NIME.

Caroline BergvallView profile

Professor

• Multilingual Studies
• Interdisciplinary Arts
• Performance Studies
• +3 more

Caroline Bergvall is a Global Professorial Fellow at Queen Mary University of London’s School of English and Drama. She holds a PhD from Dartington College of Arts/University of Plymouth (2000). As a poet and interdisciplinary artist, she focuses on multilingual projects, transhistoric approaches, and site-specific performance. Her research explores modes of listening, voice processing, and creative engagement through workshops and public performances. Key works include the trilogy Meddle English (2005), Drift (2011), and Alisoun Sings (2019). Awards include the Cholmondeley Award (2017) and the Bernard Heidsieck-Centre Pompidou Paris Prize (2017). Her current research project emphasizes interdisciplinary dialogue and concludes in 2025 with a public performance event. Bergvall founded the Solitary to Solidary Arts Lab to support artists and writers. She has exhibited globally, including at the Henry Moore Institute and the British Library. Research interests span sound art, vocal poetics, and translocal trajectories. Her work often addresses language violence, migratory narratives, and cross-cultural textualities. Public engagement includes mentoring programs and collaborations with musicians and institutions worldwide.

Aidan HoggView profile

Lecturer

• Spatial Acoustics
• Immersive Audio
• Deep Learning
• +3 more

Aidan Hogg is a Lecturer in Computer Science at Queen Mary University of London (QMUL), affiliated with the Centre for Digital Music (C4DM) and the School of Electronic Engineering and Computer Science. He holds a PhD and is an Honorary Research Associate at Imperial College London. His research focuses on deep learning applications for spatial acoustics, immersive audio, and statistical signal processing in audio systems. Key interests include HRTF upsampling, speaker diarization, and binaural audio technologies. He holds qualifications including PhD, DIC, MEng, ACGI, and AFHEA. His work bridges theoretical advancements with practical applications in virtual and augmented reality audio, with notable contributions to the SONICOM HRTF dataset and GAN-based spatial audio rendering techniques. He teaches advanced control systems (ECS654U) and leads the Virtual, Immersive, Augmented and Binaural Audio Lab (VIABAL). Recent grants include £65,621 from L-ACOUSTICS UK LIMITED for 'Online Speech Enhancement in Low Direct-to-Reverberant-Ratio Scenarios' (2024–2025). His research outputs span over 15 peer-reviewed articles between 2019–2025, emphasizing machine learning for audio signal processing and multimodal AI integration. Aidan collaborates widely, hosting international institutions such as the Central Conservatory of Music, China, and presents at major conferences like IEEE WASPAA and ICASSP. His lab focuses on advancing immersive audio technologies through interdisciplinary approaches.

Simon HadfieldView profile

Associate Professor

• Computer Vision
• Robotics
• Machine Learning
• +3 more

Dr Simon Hadfield is an Associate Professor (Reader) in Robot Vision and Autonomous Systems at the University of Surrey, affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on advancing computer vision and machine learning techniques for real-world applications, particularly in robotics. He holds a PhD and certifications such as FHEA (Fellow of the Higher Education Academy) and AUS (Advanced University Skills). Research Interests: - Effective exploitation of novel visual sensors - Practical application of computer vision to robotics - Efficient dynamic reconstruction techniques - Real-time robotics applications - Sensor fusion and calibration systems Recent projects include the NIMROD initiative exploring analogue visual sensors and contributions to multimillion-pound research efforts in sustainable packaging and agricultural robotics. His work emphasizes bridging the gap between lab-based techniques and real-world deployment, with publications in top-tier conferences and journals. Notable achievements include developing paradigms for low-complexity 3D reconstruction and proposing methods for depth estimation and localization. His research has been funded by grants such as a £1m award for robotic applications in farming. Collaborations and lab affiliations include CVSSP, where he leads projects at the intersection of vision, robotics, and autonomous systems. Future work continues to focus on scalable vision techniques and ethical AI practices in representation learning.

• Media Psychology
• Neurocommunication
• Cognitive Processes in Communication
• +3 more

Emma Rodero Antón is a Full Professor of Media Psychology and Neurocommunication at the Department of Communication, Pompeu Fabra University (UPF). She directs the Media Psychology Lab and holds a Ph.D. in Psychology and Communication. Her research focuses on cognitive processes, attention, memory, and nonverbal communication, utilizing psychophysiological methods to analyze voice, sound, and auditory engagement. Education: Ph.D. in Psychology and Communication Doctorate in Health Sciences Master’s in Arts and Humanities (Communication) Graduate Degree in Communication Research Interests: Her work explores how auditory elements (e.g., prosody, gestures, spatial audio) influence communication effectiveness, with applications in healthcare (e.g., Alzheimer’s caregiving), crisis communication, and virtual reality interventions for public speaking anxiety. She employs EEG, physiological measurements, and experimental designs to study listener engagement and cognitive-emotional responses. Publications Overview: Recent articles address topics like CSR skepticism during crises, neurophysiological responses to audio formats (podcasts, radio), and strategies to improve communication with Alzheimer’s patients. Her work bridges neuroscience and communication theory, emphasizing practical applications in media design and healthcare. Awards & Grants: No specific awards listed, but her extensive publication record reflects sustained academic impact. Active in grant-funded projects related to media psychology and neurocommunication. Advising & Labs: Director of the Media Psychology Lab Advisor on projects involving VR therapy, audio storytelling, and caregiver-patient communication Future Work: Expanding research into immersive audio technologies, cross-cultural communication dynamics, and psychophysiological tools for communication effectiveness assessment.

Søren BechView profile

Professor

• Acoustics
• Audio Engineering
• Sound Zones
• +4 more

Søren Bech is a Professor at the Department of Electronic Systems within The Technical Faculty of IT and Design at Aalborg University. His academic qualifications include a PhD from the Technical University of Denmark (1988) and an MSc in Electrical Engineering (1982). His primary research focuses on audio quality, acoustics, and sound zone technologies, with emphasis on perceptual audio evaluation, spatial audio systems, and cortical auditory attention decoding. He leads projects such as ISOBEL (Interactive Sound Zones) and SOUNDS (Service-Oriented, Ubiquitous, Network-Driven Sound), addressing challenges in dynamic audio environments and networked audio systems. Key research areas include automotive audio systems, headphone technology, and the integration of EEG-based attention decoding for personalized sound experiences. He has received recognition, including the Best Student Paper award in 2024. Collaborations span academia and industry, with involvement in organizations like the Danish Sound Cluster and Danmarks Forsknings- og Innovationspolitiske Råd. His work bridges theoretical acoustics with practical applications in consumer electronics and immersive media.

Minghui ZhengView profile

Associate Professor

• Human-robot collaboration
• Robotic motion planning
• Task allocation and sequence planning
• +5 more

Minghui Zheng is an Associate Professor in Mechanical Engineering at Texas A&M University's College of Engineering. His research focuses on human-robot collaboration, robotic motion planning, and learning-based control systems for heterogeneous robots. He leads the Control and Robotics (CtrlRobot) Lab, which develops solutions for disassembly, recycling, and remanufacturing through advanced robotics and AI. Dr. Zheng holds a Ph.D. from UC Berkeley (2017), an M.S. from Beihang University (2011), and a B.S. from Beihang University (2008). His work integrates machine learning with robotics to address challenges in collaborative environments, including task allocation, real-time human motion prediction, and ergonomic improvements in e-waste handling. Key research directions include: Knowledge-informed motion planning for manipulators Uncertainty-aware human behavior modeling Cable-driven robotic hand design Connected vehicle systems for collaborative estimation He has received prestigious awards such as the NSF CAREER Award (2021) and the Exceptional Scholar: Young Investigator Award (2023). His lab develops platforms like VR Co-Lab for disassembly training and tools like SIMPNet for spatial-informed motion planning. Publications span robotics journals and conferences, emphasizing interdisciplinary solutions at the intersection of AI, control theory, and mechanical engineering. Current projects explore multi-task learning, diffusion models for motion prediction, and energy-efficient robotic systems.

Joshua PeeplesView profile

Assistant Professor

• Machine Learning
• Deep Learning
• Texture Analysis
• +3 more

Dr. Joshua Peeples is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University's College of Engineering. His research focuses on developing novel machine learning and computer vision methods for texture analysis, pattern recognition, and image processing, with applications ranging from biomedical imaging to agricultural automation and underwater acoustics. His work centers on histogram-based deep learning architectures for texture characterization and segmentation, with recent extensions to multi-modal data analysis and explainable AI. The Advanced Vision and Learning Lab he leads pioneers data-driven solutions for real-world challenges across diverse domains. Awards: NSF Graduate Research Fellowship McKnight Doctoral Fellowship ACES Faculty Fellowship Edward Alexandar Bouchet Honor Society His publications demonstrate strong emphasis on developing specialized neural network layers for texture analysis, with applications across sonar classification, plant phenotyping, and biomedical segmentation. Research consistently integrates theoretical machine learning advances with practical applications in engineering domains.

Laresa KosloffView profile

Senior Lecturer

• Visual Arts
• Film
• Performance Art
• +6 more

Dr. Laresa Kosloff is a Senior Lecturer at RMIT University's School of Art, located on the City Campus in Melbourne, Australia. She has been with RMIT since 2012 and is open to supervising Masters and PhD students in areas such as the performing body in art, humor, conceptual art, and video art. Her research interests focus on the body's agency within everyday contexts, employing strategies like slapstick physicality and narrative overlays. She has exhibited widely, including at the Institute Modern Art (Brisbane), Monash Prato Centre (Italy), and Artspace Sydney. Her work explores themes of humor, tension between cultural values, and individual agency. Awards and Recognition: Nillumbik Contemporary Art Prize (2023) Incinerator Art for Social Change Award (2021) Guirguis New Art Prize Winner (2019) Finalist, 67th Blake Art Prize (2022) Advising and Collaborations: Laresa has supervised numerous research projects, including Bogeybabies – an exorcism of the virtual damaged child figure (2024) and Theres no going back: chaos and destruction as compositional strategies in performance and video (2023). She collaborates with institutions like Buxton Contemporary and Sutton Gallery. Labs and Teams: While no specific lab is mentioned, her participatory artworks often involve collaborative sculptural practices and posthuman ethics, as seen in projects like Life-Extending Breadcrumbs (2021).

Zhengzhong TuView profile

Assistant Professor

• Spatial Intelligence
• 3D Vision
• Multimodal and Generative AI
• +3 more

Zhengzhong Tu is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, College of Engineering. His research focuses on spatial intelligence, 3D vision, multimodal AI, autonomous driving, and trustworthy large language models. He holds a Ph.D. from The University of Texas at Austin (2022) and M.S./B.S. degrees from Fudan University (2018/2016). Education Ph.D., Electrical and Computer Engineering, The University of Texas at Austin – 2022 M.S., Electrical Engineering, Fudan University – 2018 B.S., Electrical Engineering, Fudan University – 2016 Research Interests Tu’s work bridges generative AI, autonomous systems, and multimodal perception. He explores challenges in 3D reconstruction, cross-modal alignment, and safety-critical applications like autonomous driving. His recent projects focus on enhancing model trustworthiness through robust watermarking and constraint optimization, while advancing benchmarks for vision-language models in spatial reasoning. Publications Trends His 2025 articles emphasize generative AI applications in autonomous driving (e.g., crash prediction, sensor fusion), multimodal systems (e.g., vision-language navigation), and video quality enhancement. He also contributes to benchmark development (e.g., NTIRE challenges) and ethical AI practices. Awards No scientific awards explicitly mentioned in the text. Advising & Grants No student advisees or grant information listed. His research is supported by institutional affiliations and collaborative projects. Labs & Teams Not explicitly mentioned in the provided text. His work likely involves Texas A&M’s autonomous systems and AI research groups.

Stephen RoessnerView profile

Assistant Professor

• Psychoacoustics of Distributed Mode Loudspeakers
• Audio Archival Preservation
• Microphone Theory
• +3 more

Stephen Roessner is an Assistant Professor of Electrical and Computer Engineering at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the same institution (2023). His research focuses on audio engineering and acoustics, emphasizing psychoacoustics of distributed mode loudspeakers, audio preservation, microphone theory, and spectral analysis. He has conducted extensive studies on listener preferences for flat-panel loudspeakers and temporal patterns in music. His work integrates signal processing, room acoustics, and human-computer interaction. Education: PhD in Electrical and Computer Engineering, University of Rochester (2023) Key research areas include audio latency masking, real-time signal processing frameworks, and ensemble performance timing analysis. His articles frequently explore loudspeaker design, reverberation effects, and audio preservation strategies. Notably, he has investigated trends in US Billboard music tempo and structural acoustics of glass loudspeakers. His research has been published across multiple venues, including the Proceedings of Meetings on Acoustics and specialized journals in audio engineering. While no specific awards are listed, his contributions reflect sustained innovation in acoustic technologies and music informatics.

Elise PiazzaView profile

Assistant Professor

• Music Cognition
• Auditory Perception
• Communication
• +4 more

Elise Piazza is an Assistant Professor in the Department of Brain and Cognitive Sciences at the University of Rochester, affiliated with the School of Arts & Sciences and Neuroscience. Her research focuses on understanding how communication, auditory perception, and music cognition operate across developmental stages and social interactions. She employs naturalistic paradigms and neuroscience techniques like fNIRS to study neural synchrony between infants and adults, temporal processing in music, and adaptive mechanisms in vocal communication. Her work spans topics such as the influence of tonal context on musical tasks, the role of pitch dynamics in child-directed speech, and neural correlates of learning in infants. Piazza’s methodologies include combining experimental psychology with computational modeling, as seen in her use of language transformer models to analyze creative narratives and quantify musical perceptual similarity. Key research themes include two-person neuroscience, real-world communication dynamics, and the interplay between auditory perception and developmental milestones. Her recent studies highlight how naturalistic temporal structures impact memory and prediction in music, and how rapid adaptation occurs to timbral features in auditory environments. Piazza’s research emphasizes interdisciplinary approaches, bridging cognitive science, neuroscience, and developmental psychology to uncover fundamental principles of human communication and learning. Her lab utilizes cutting-edge neuroimaging tools to explore real-time neural coupling during social interactions, particularly between caregivers and infants.