Ruohan Gao is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park , with affiliate appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) , Maryland Robotics Center (MRC) , and Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . His research focuses on Computer Vision and Machine Learning , emphasizing Multisensory Machine Intelligence that integrates sight, sound, and touch . He aims to enable machines to perceive, understand, and interact with the world as humans do, with applications in robotic manipulation , audio-visual localization , and differentiable rendering . Article Trends : Span 2018–2025 , centering on audio-visual perception , multisensory datasets , and robotics . Recurring themes include object-centric learning , sound synthesis , and cross-modal consistency . Scientific Awards : Michael H. Granof Award (UT Austin’s Top 1 Doctoral Dissertation, 2021) Best Paper Award Runner-Up (BMVC 2021) Best Paper Award Finalist (CVPR 2019) Highlight Paper (CVPR 2023) He leads the UMD Multisensory Machine Intelligence Lab and collaborates with institutions like Stanford and The University of Texas at Austin . Contact: rhgao@umd.edu .
Professor Anna Marie Munster is a distinguished academic and artist at UNSW Art and Design (College of Fine Arts), where she has held a full-time tenured position since 2001. Her work bridges theoretical scholarship with creative practice, establishing her as a leading figure in digital media art and critical theory. University: University of New South Wales School: College of Fine Arts (UNSW Art and Design) Position: Professor Qualifications: PhD from UNSW (2002), BA (Class 1 Hons with University Medal) from University of Sydney (1984) Professor Munster's research explores the intersections of art, technology, and philosophy, with particular focus on statistical visuality, radical empiricism, the politics and aesthetics of machine learning, and more-than-human perception. Her work integrates process philosophy with contemporary media practices, examining how time, movement, and sonicity shape our experience of digital environments. She approaches these topics through both theoretical scholarship and creative practice, often collaborating with artist Michele Barker on multi-channel audiovisual installations. Her publications reveal a consistent trajectory exploring the relationship between embodiment and digital media, with recent work focusing on AI and machine learning in cultural contexts. Munster's creative output demonstrates sophisticated integration of technical innovation with philosophical inquiry, particularly in how her installations challenge conventional perceptions of time and space. ARC, UNSW Student Council Award for Excellence in Postgraduate Supervision (2017) Dean's Award for Excellence in Postgraduate Supervision (2015) Highly commended for Materializing New Media at Prix Ars Electronica (2008) Winner of National Digital Art Awards 'The Harries' (2006) University Press of New England Publishing Award (2005) Professor Munster has completed over 20 PhD and Masters supervisions at UNSW and been involved in another 10 secondary supervisions. She currently supervises five PhD candidates working on diverse topics including coding materialities, critical bio-textiles, schizophrenia and XR technologies, sound and resonance, and affective drawing. Her ARC-funded research projects demonstrate sustained commitment to interdisciplinary work, particularly at the intersection of art, science, and technology. Her creative practice with Michele Barker has resulted in numerous commissioned installations exploring perception, embodiment, and the relationship between humans and technology.
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.
Essa Yacoub is a Professor in the Department of Radiology at the University of Minnesota, affiliated with the PhD Program in Medical Physics and the Center for Magnetic Resonance Research. His work focuses on advancing MRI and fMRI technologies, particularly at ultrahigh magnetic fields (e.g., 10.5 T), to achieve unprecedented spatial and temporal resolution in brain imaging. He leads projects in RF coil design, noise reduction algorithms, and developmental neuroimaging. Roles: Professor, Medical Physics Program Faculty Affiliations: Center for Magnetic Resonance Research, Department of Radiology Research emphasizes high-resolution fMRI applications, including layer-specific brain mapping, pediatric neurodevelopment studies (e.g., Baby Connectome Project), and translational tools like BIBSNet for infant brain segmentation. His innovations bridge hardware engineering (RF coils) and software (denoising pipelines) to tackle challenges in mesoscopic-scale imaging. Key contributions include optimizing imaging protocols at 7T/10.5T, developing NORDIC denoising for submillimeter data, and advancing understanding of brain networks in aging and neurological disorders. His work is foundational for large-scale initiatives like the Human Connectome Project and non-human primate neuroimaging collaborations. Grants and collaborations focus on translational imaging technologies, while educational contributions include training through the Medical Physics PhD Program. Ongoing efforts aim to refine ultra-high field MRI applications for clinical and basic neuroscience research.
Brian Roberts is a Professor at Aston University's School of Psychology within the College of Health and Life Sciences. His primary research focuses on auditory perception, particularly auditory scene analysis and speech perception. He holds a PhD in Experimental Psychology (Auditory Perception) from the University of Cambridge (1989) and has held academic positions at the University of Birmingham (1993-2001) and Aston University since 2005. His work explores perceptual grouping mechanisms, cochlear implant listening, and the neural bases of auditory phenomena. Affiliations: Cognition & Neuroscience Research Group (CNRG), Centre for Vision and Hearing Research Employment History: Reader at University of Birmingham (2001), Senior Lecturer (1999), Lecturer (1993) Research interests include auditory stream segregation, formant analysis, and the effects of stimulus properties on perceptual organization. He has collaborated internationally, including visiting appointments at Macquarie University (2019) and the University of British Columbia (2011). Notable achievements include being elected a Fellow of the Acoustical Society of America (2009). Recent publications investigate factors influencing stream segregation (2024), asymmetric timbre effects (2023), and lexical bias in consonant identification (2022). Current projects explore sudden changes in auditory stimuli and their impact on perceptual grouping. Grants & Funding: Extensive research grants supporting auditory perception studies (details not explicitly listed) Supervised Work: 4 documented supervisees, though names not provided Labs/Teams: Active in the Auditory Perception Laboratory at Aston University, focusing on psychophysical and computational modeling approaches.
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.
Prof. Bernhard U. Seeber is an Extraordinary Professor at the Technical University of Munich (TUM), leading the Chair of Audio Signal Processing within the TUM School of Computation, Information and Technology. His work bridges auditory neuroscience and engineering, focusing on improving hearing aids, cochlear implants, and virtual acoustic systems. He holds affiliations with the Bernstein Center for Computational Neuroscience, Munich Institute of Biomedical Engineering, and others. Education: Studied and earned his PhD (2003) in Electrical Engineering and Information Technology at TUM. Postdoctoral research included time at UC Berkeley and the MRC Institute of Hearing Research (UK), where he pioneered studies on binaural hearing and cochlear implant optimization. Research Interests: Combines experimental and theoretical approaches to explore auditory scene analysis, binaural unmasking, and spatial hearing. Key areas include signal coding for cochlear implants, virtual acoustics, and non-destructive acoustic monitoring. His work emphasizes interdisciplinary collaboration with industry and academia. Awards: Lothar Cremer Award (2010), Emmy Noether Fellowship (2007), and recognition from the German Acoustical Society. Teaching: Offers courses on audio communication, computational neuroscience, and technical acoustics. Projects: Leads initiatives like HAPPAA and Auralization, advancing sound field synthesis and hearing aid algorithms. Current Roles: Head of Chair of Audio Signal Processing, Board Member of DEGA, and spokesperson for the ITG Technical Committee on Hearing Acoustics.
Ana Serrano is an Associate Professor at Universidad de Zaragoza, Spain, where she is affiliated with the Graphics & Imaging Lab in the EINA (Edificio Ada Byron) school. She earned her PhD at the same institution under the supervision of Prof. Diego Gutierrez and Prof. Belen Masia, and completed a postdoctoral fellowship at the Max-Planck-Institute for Informatics under Prof. Karol Myszkowski. Her research focuses on visual computing , particularly in computational imaging , material appearance perception and editing , and virtual reality . She is especially interested in developing perceptually-driven methods that leverage knowledge of the human perceptual system to enhance user experiences and assist content creation in immersive environments. Her recent publications (2023–2025) span top-tier venues such as SIGGRAPH, CVPR, IEEE TVCG, and Eurographics. These works explore topics like saliency prediction in 3D and 360° video, crossmodal perception in VR, gloss modeling, radiance fields, and perceptual evaluation of immersive content. The research demonstrates a strong integration of machine learning, human perception, and computer graphics to solve real-world problems in visual computing. She has received several prestigious awards, including: Eurographics 2023 Young Researcher Award VGTC VR 2024 Significant New Researcher Award Eurographics 2020 PhD Award Adobe Research Fellowship (honorable mention, 2017) NVIDIA Graduate Fellowship (2018) Ana Serrano actively supervises PhD and Master’s students and has taught courses such as Virtual Reality, Computational Imaging, and Deep Learning applications. She serves as an Associate Editor for Computer Graphics Forum , ACM Transactions on Applied Perception , and Computers and Graphics , and has held leadership roles in major conferences including Eurographics (Tutorials co-chair, 2023), ACM SAP (Program co-chair, 2022), and CEIG (Program co-chair, 2022). Her professional service includes extensive program committee and reviewer roles for SIGGRAPH, IEEE VR, ISMAR, and others. She leads a vibrant research group focused on human perception in virtual environments, with current projects on computational models of attention and perception, integrated with physiological signals. Her lab, the Graphics & Imaging Lab, fosters interdisciplinary collaboration and innovation in visual computing.
Stephen S. Hudson is an Assistant Professor of Music Theory at Occidental College. He holds a B.A. from the University of California, Davis, and a Ph.D. from Northwestern University. Appointed in 2022, he specializes in metal music studies and R&B/Soul harmony. Education: B.A., University of California, Davis; Ph.D., Northwestern University Hudson’s research focuses on embodied experiences of rhythm, timbre, and song form in metal music, as well as harmonic complexity in R&B and Soul. His work has been featured in Music Theory Spectrum , Metal Music Studies , and Current Musicology . Recent publications include articles on Drake’s harmonic loops and Meshuggah’s groove structures. He is authoring two books: Heaviness in Metal Music (Oxford University Press) and a study of Meshuggah’s riffs (Lever Press). As Vice President of the Pacific Southwest chapter of the American Musicological Society, he advocates for inclusive music theory pedagogy that extends beyond classical traditions. His teaching emphasizes tools for songwriters and producers. Hudson maintains an active interest in classical and baroque cello performance, reflecting his engagement with both historical and contemporary music practices.
Larry Heck is a Professor at the Georgia Institute of Technology with joint appointments in the School of Electrical and Computer Engineering and the School of Interactive Computing. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar. His research focuses on machine learning, deep learning, natural language processing, conversational systems, and speech/speaker recognition. He directs the AI Virtual Assistant (AVA) Lab, advancing next-generation AI assistants. Dr. Heck has held leadership roles in industry, including at Microsoft, Google, Samsung, and Viv Labs, and has over 50 U.S. patents. Education: BSEE from Texas Tech University (1986) MSEE and PhD in Electrical Engineering from Georgia Tech (1991) Research Interests: Dr. Heck’s work bridges machine learning and human-centric AI, with emphasis on conversational systems, multimodal interaction, and real-world applications. His AVA Lab develops AI assistants that integrate visual, auditory, and contextual cues for natural interaction. Recent projects include multimodal sensor integration, dialogue systems for caregiving networks, and embodied AI for avatar animation. Awards: IEEE Fellow (2020) Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Advising & Grants: While primarily focused on industry collaboration, Dr. Heck mentors students through Georgia Tech’s interdisciplinary programs. His research is funded by government agencies and corporate partnerships, including the NSA and DARPA. Labs & Teams: The AVA Lab collaborates with academia and industry to create AI systems that understand context, gestures, and environment. Current initiatives include multimodal dialogue datasets (e.g., OKCV, SensorQA) and reinforcement learning frameworks for real-time systems.
Dr Valdas Noreika is a Senior Lecturer in Psychology at the School of Biological and Behavioural Sciences, Queen Mary University of London. He serves as Head of The Centre for Brain and Behaviour and leads the Sleep and Cognition Lab. His work bridges cognitive neuroscience, psychology, and clinical applications, with a focus on understanding consciousness and its disorders. Dr Noreika's educational background includes: BA in Philosophy from Vilnius University, Lithuania MSc in Neurobiology from Vilnius University, Lithuania PhD in Psychology from the University of Turku, Finland, focusing on altered states of consciousness and temporal distortions Dr Noreika's research explores the cognitive and neural mechanisms underlying sleep, dreaming, and consciousness. Using techniques including electroencephalography (EEG), transcranial magnetic stimulation (TMS), and psychophysics, his work investigates both basic mechanisms of consciousness and their applications to neurodevelopmental and mental health conditions. His research spans multiple domains including time processing, inter-brain synchronization across species, and environmental decision-making. A key aspect of his work involves translational research focusing on sleep, subjective experiences, and well-being in conditions such as learning disabilities, ADHD, autism, and depression. Analysis of Dr Noreika's recent publications reveals a strong focus on consciousness studies, neural mechanisms of sleep and dreaming, and applications to neurodevelopmental conditions. His work frequently employs EEG and other neuroimaging techniques to study brain activity during various states of consciousness. Recent trends show increasing emphasis on inter-brain synchronization, particularly in infant-parent interactions and cross-species communication, as well as growing interest in environmental psychology and climate change-related decision making. Dr Noreika has secured significant research funding including: The neural basis of inter-species communication - £155,098 from the Biotechnology and Biological Sciences Research Council (2024-2026) Sleep and circadian interactions with sensory sensitivity in adults with intellectual disabilities - £103,229 from the Baily Thomas Charitable Fund (2023-2025) As a supervisor, Dr Noreika advises multiple PhD students working on diverse topics including Alzheimer's disease diagnosis using information theory, emotion recognition, thermal sensation in Parkinson's disease, cultural differences in cognitive processes, and time processing. His Sleep and Cognition Lab serves as a hub for interdisciplinary research bridging neuroscience, psychology, and clinical applications. Dr Noreika leads the Sleep and Cognition Lab at Queen Mary University of London, which focuses on investigating the neural mechanisms of sleep, dreaming, and consciousness. The lab brings together researchers from diverse backgrounds to study both fundamental aspects of consciousness and their applications to clinical populations. Current projects include investigations of sleep and sensory sensitivity in adults with learning disabilities and human-dog interaction studies.
Mariya Toneva is a tenure-track faculty member at the Max Planck Institute for Software Systems , conducting groundbreaking research at the intersection of Machine Learning , Natural Language Processing , and Neuroscience . She leads the Bridging AI and Neuroscience (BrAIN) group , focusing on computational models that align AI systems with human brain processes. Her work aims to enhance both AI capabilities and neuroscience understanding through this cross-disciplinary approach. Actively recruiting postdocs, PhDs, and research interns in areas like code/text representation, brain-AI alignment, and neuroimaging data analysis Collaborator on NIH-funded projects using fMRI and neuropixel data Research Themes : Her group explores neural mechanisms of language processing, event segmentation in narratives, memory reactivation via music, and effective human-AI collaboration frameworks. Key methods include LLM analysis, cross-modal similarity metrics, and naturalistic task-based fMRI studies. Key Publications (2024-2025): Brain-tuned speech models (INTERSPEECH 2025) Cognitive event boundaries in LLMs (Behavioral Research Methods 2025) Music-induced memory reactivation (biorxiv 2024) LLM-brain alignment reasons (EMNLP 2024) Advising : Mentors PhD candidates Omer Moussa (speech processing), Camila Kolling (representational similarity), and Gabriele Merlin (LLM alignment). Collaborates with institutions like MIT, NYU, and ETH Zurich.
Eduardo Mercado III is a Professor in the Department of Psychology at the University at Buffalo, College of Arts and Sciences. His research focuses on bioacoustics, cognitive psychology, and marine ecology, particularly the vocal behavior of humpback whales and its implications for understanding human impact on marine ecosystems. He is also known for his work in perceptual learning, autism spectrum disorder, and comparative cognition. Scientific Awards Guggenheim Fellowship Harvard Radcliffe Institute Fellowship Research Trends His recent publications emphasize bioacoustic analysis of humpback whale songs, including their spectral entropy, cyclical variations, and adaptive adjustments to anthropogenic noise. Additional work explores perceptual learning mechanisms in autism, neural network modeling for acoustic classification, and cognitive processes in canines and rodents. Projects Mercado’s “Singers as Sentinels” project combines acoustic analysis of humpback whale songs with public awareness initiatives about ocean noise pollution. The project will produce a book, Why Whales Sing and Dolphins Don’t , and a web-based interface for public engagement.
Jonathan Cannon is an Assistant Professor in the Department of Psychology, Neuroscience & Behaviour at McMaster University's Faculty of Science. His research focuses on timing and rhythm in perception and action, with particular interest in timing-related neural dynamics in the basal ganglia, cerebellum, and supplementary motor area. His work combines mathematical modeling with experimental approaches to understand the neural basis of rhythm perception and production. Dr. Cannon's research interests span timing and rhythm perception , neural dynamics , dynamical systems theory , Bayesian cognition , neural oscillations , and autism research . His approach centers on formulating and simulating neurophysiological and cognitive models, drawing on dynamical systems theory and Bayesian cognitive frameworks. His work incorporates psychophysics, EEG experiments, and collaborations with experimentalists to investigate how the brain processes rhythmic information. Analysis of his recent publications reveals a strong focus on the intersection of rhythm perception, motor control, and autism spectrum disorder. His work demonstrates how beat perception co-opts motor neurophysiology, with particular attention to predictive processes in rhythmic cognition. His research shows reduced precision of motor and perceptual rhythmic timing in autistic adults, while also finding intact sequence learning abilities in certain contexts. Dr. Cannon teaches advanced courses including Machine Learning Methods for Brain Modelling and Neural Data Analysis (PSYCH 734), Computational Models in Neuroscience (NEUROSCI 3MN3), and Neuroscience Seminars. His teaching reflects his interdisciplinary approach that bridges mathematics, neuroscience, and cognitive science. Beyond his academic work, Dr. Cannon is an active musician who performs on violin and guitar, particularly in klezmer and folk music contexts. He has also demonstrated entrepreneurial spirit through founding Flying Leap Games and developing the storytelling game 'Wing It,' which successfully crowdfunded and reached numerous retailers.
Dr. Burkhard Maess is a Research Professor and Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences, leading the Methods and Development Group Brain Networks. His research focuses on auditory and language processing, signal analysis, and dynamic modeling of neuronal networks. He holds a Diploma in Physics (University of Leipzig, 1987) and a PhD in Physics (University of Leipzig, 1990). His career includes postdoctoral positions at the Academy of Sciences of the GDR and the Free University of Berlin before joining the MPI in 1995. Since 2000, he has led research groups on MEG/EEG signal analysis and cortical network dynamics. His work integrates advanced neuroimaging techniques like MEG and EEG to study sensory processing, neural network dynamics, and the effects of aging on auditory attention. Key contributions include developing high-resolution BEM-FMM methods for source localization and analyzing cross-frequency coupling in neuroscience data. His group also explores spinal cord electrophysiology and the neural underpinnings of perceptual decision-making. Dr. Maess’ research spans cognitive neuroscience, biomedical engineering, and computational modeling, with a focus on bridging empirical findings with theoretical frameworks in neuroscience.
