جست‌وجوی استاد و پژوهشگر

• Robotics
• Robot Learning
• Machine Learning
• +۷ مورد دیگر

Lerrel Pinto is an Assistant Professor of Computer Science at the Courant Institute of Mathematical Sciences at New York University (NYU), where he leads the General-purpose Robotics and AI Lab (GRAIL) as part of the CILVR research group. His work bridges the gap between theoretical machine learning and practical robotics applications, with a focus on enabling robots to generalize and adapt in real-world environments. Dr. Pinto received his undergraduate degree from IIT Guwahati, followed by a PhD from the Robotics Institute at Carnegie Mellon University (CMU). He then completed a postdoctoral fellowship at the University of California, Berkeley before joining NYU as faculty. His research program centers on robot learning and decision making, with several key thrusts that demonstrate his innovative approach to robotics. Pinto's work emphasizes large-scale learning techniques that leverage both extensive data and sophisticated model architectures. A significant portion of his research focuses on representation learning for sensory data, particularly developing methods that enable robots to make sense of visual, tactile, and auditory inputs. His lab has made notable contributions to reinforcement learning algorithms that allow robots to adapt to new scenarios with minimal retraining. Pinto also champions open-source robotics , developing affordable robot platforms that democratize access to robotics research. Analysis of Pinto's recent publications reveals a strong trend toward multimodal perception in robotics, integrating visual, tactile, and auditory information to create more robust robot systems. His work increasingly focuses on zero-shot and few-shot learning capabilities, enabling robots to handle novel situations without extensive retraining. There's also a clear progression toward general-purpose robotics , moving away from task-specific solutions toward more flexible systems that can handle diverse real-world challenges. Dr. Pinto's scientific contributions have been recognized with several prestigious awards: Sloan Research Fellowship (2025) NSF CAREER Award (2024) RAL Early Career Award (2024) Best Student Paper Award at ICRA (2016) Outstanding Paper Award at MFM-EAI workshop at ICML (2024) Best Paper Award at NGSM workshop at ICML (2024) Best Student Paper Award at RSS (2023) As an advisor, Pinto has mentored numerous students who have gone on to impactful careers in both academia and industry. His former PhD student Denis Yarats co-founded Perplexity.AI, while Mahi Shafiullah became a postdoc at UC Berkeley and Meta AI. Many of his Masters students have pursued PhDs at top institutions like CMU, MIT, and Stanford, or joined leading robotics companies including 1X, Fauna Robotics, and NVIDIA. Pinto's lab has secured significant research funding, including the NSF CAREER award and likely other grants supporting his robotics research program. The General-purpose Robotics and AI Lab (GRAIL) that Pinto leads brings together a diverse team of researchers working on cutting-edge robotics challenges. The lab maintains strong collaborations with industry partners and other academic institutions, facilitating technology transfer and real-world impact. GRAIL's research spans multiple robotics platforms and focuses on developing algorithms that enable robots to learn from diverse experiences and generalize across environments.

Olga Sorkine-Hornungمشاهده پروفایل

استاد

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +۵ مورد دیگر

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

Xavier Serraمشاهده پروفایل

استاد

• Audio Signal Processing
• Sound and Music Computing
• Music Information Retrieval
• +۵ مورد دیگر

Xavier Serra is a Full Professor at the Department of Engineering at Universitat Pompeu Fabra (UPF), Barcelona. He is the founder and director of the Music Technology Group (MTG), and leads the UPF-BMAT Chair on AI and Music. He also coordinates the Master in Sound and Music Computing and serves as President of the Phonos Foundation. His research focuses on audio signal processing, sound and music computing, and computational musicology, emphasizing open science and open innovation. Education: BSc in Biology, University of Barcelona (1981) Master in Music, Florida State University (1983) PhD in Computer Music, Stanford University (1989) Research Interests: Audio Signal Processing Data-Driven and Knowledge-Driven Methodologies Music Information Retrieval Cultural Music Analysis (e.g., Carnatic/Turkish/Andalusian Music) Music Education Technology Notable Projects: CompMusic (ERC Advanced Grant, 2010-2017): Multicultural computational music analysis Open datasets: Freesound, Saraga, FSD50K Technologies: Reactable, Vocaloid, Essentia API Recent Trends in Articles: Focus on AI-driven audio processing (neural fingerprints, generative models), cross-cultural music analysis, and explainable music difficulty estimation. Awards: ERC Advanced Grant (2010) for CompMusic Project. Labs/Teams: Director of MTG, Phonos Foundation, and UPF-BMAT Chair. Active in open-source projects and international collaborations.

Nima Mesgaraniمشاهده پروفایل

دانشیار

• Acoustic Signal Processing
• Computational Neuroscience
• Brain-Machine Interface
• +۳ مورد دیگر

Nima Mesgarani is an Associate Professor of Electrical Engineering at Columbia Engineering, Columbia University, affiliated with the Sense, Collect and Move Data Committee. His research bridges engineering and neuroscience through reverse-engineering neural signal processing mechanisms, leading to advancements in brain-machine interfaces, neural prosthetics, and speech processing algorithms. He received his PhD in Electrical Engineering from the University of Maryland and completed postdoctoral training at Johns Hopkins University's Center for Language and Speech Processing and UC San Francisco's Neurosurgery Department. Research Focus Professor Mesgarani's lab integrates computational neuroscience and engineering to study acoustic signal processing. Key areas include: Neural decoding of speech and auditory attention in multi-talker environments Development of brain-controlled hearing technologies Novel speech separation and synthesis algorithms inspired by cortical processing Cross-modal learning between auditory and visual systems Applications of large language models in neural signal interpretation Publication Trends Analysis of his 15 most recent articles (2025) reveals dominant themes: neural decoding techniques using intracranial EEG, brain-inspired speech separation models (e.g., Mamba architectures), applications of large language models in auditory neuroscience, cross-modal distillation methods, and clinical translation of audio processing algorithms. A strong emphasis emerges on real-time brain-computer interfaces and noise-robust speech processing. Laboratory and Collaborations Mesgarani directs an interdisciplinary lab developing neurotechnology for hearing restoration. His team collaborates with neurosurgery departments and speech processing centers, focusing on translating theoretical models into clinical brain-machine interfaces. The lab's work has yielded patents for brain-informed speech separation systems and attention-decoding frameworks.

Dhruv Jainمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Audio AI
• Accessibility
• +۵ مورد دیگر

Dhruv Jain is an Assistant Professor in the Computer Science and Engineering Department at the University of Michigan , with affiliations in the School of Information and Michigan Medicine . He leads the Soundability Lab , focusing on transforming hearing into a programmable interface through human-centered AI systems. PhD from University of Washington MS from MIT Media Lab Formerly worked at Microsoft Research, Google, and Apple Research Interests intersect Human-Computer Interaction (HCI) , Audio AI , Accessibility , and Hearing Health . His work develops systems like SoundWatch (smartwatch-based sound awareness) and HomeSound (IoT sound visualization) to expand auditory access for DHH individuals and other domains. Recent Publications (2023–2025) span top venues like CHI , ASSETS , and ICMI , with trends including generative AI for real-time captioning , adaptive soundscapes , and clinical communication tools . SIGCHI Outstanding Dissertation Award (2023) William Chan Memorial Dissertation Award (2023) Best Poster Award at ASSETS 2024 Google Academic Research Award (2024) NIH Grant ($450k, 2023) William Demant Foundation Grant ($740k, 2025) Teaching includes Accessible Computing (undergraduate) and Advanced Accessibility (graduate), alongside global DIY workshops in five countries. The Soundability Lab collaborates with neuroscientists, clinicians, and Deaf scholars to create deployable systems like clinical communication tools now at Michigan Medicine and features integrated into Apple devices.

Takako Fujiokaمشاهده پروفایل

دانشیار

• Auditory Perception
• Neural Oscillations
• Auditory-Motor Coupling
• +۵ مورد دیگر

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.

Etienne Gaudrainمشاهده پروفایل

پژوهشگر

• Auditory Scene Analysis
• Speech Perception
• Cochlear Implants
• +۵ مورد دیگر

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

Suresh Krishnaمشاهده پروفایل

دانشیار

• Neurophysiology
• Computational Neuroscience
• Vision Science
• +۵ مورد دیگر

Suresh Krishna is an Associate Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on the neurophysiological and computational basis of sensory processing, attention, and eye movements, with applications to brain-machine interfaces and human health. He works with human subjects, non-human primates, and open datasets using in-vivo electrophysiology, eye-tracking, and computational modeling. Research interests include visual attention mechanisms, saccadic eye movement control, neural coding of motion perception, and the interplay between attention and decision-making. His work bridges basic neuroscience with translational applications such as improving neural prosthetics and understanding perceptual disorders. Recent work highlights how neural remapping processes during saccades underlie spatial perception, and how attention modulates neural activity patterns in visual cortex. The lab's publications reveal critical insights into the temporal dynamics of attentional shifts and their neural substrates, particularly in areas MT and MST. Dr. Krishna's team also investigates auditory temporal processing in the inferior colliculus, exploring correlations between neuronal responses to sound modulation. Their findings contribute to understanding how sensory systems encode temporal information across modalities. Research is conducted in the M2B3 Lab (http://m2b3.lab.mcgill.ca), which integrates experimental and computational approaches to study brain mechanisms underlying perception and action. No specific awards are listed, but ongoing work involves major contributions to primate neurophysiology and translational neuroscience.

Chuang Ganمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Computer Vision
• +۷ مورد دیگر

Chuang Gan is an Assistant Professor at the University of Massachusetts Amherst, affiliated with the College of Information and Computer Sciences and the Department of Computer Science. His work focuses on advancing artificial intelligence, robotics, computer vision, and embodied agents through interdisciplinary research combining neural networks, physical simulations, and multimodal learning. Research interests include generative models, reinforcement learning, vision-language integration, and scalable autonomous systems. He explores topics like world modeling for robots, adaptive policy learning, and physics-driven AI. His projects often involve creating systems that learn from visual, auditory, and tactile inputs to perform complex tasks such as object manipulation, navigation, and decision-making in dynamic environments. Recent research trends emphasize embodied AI systems capable of long-horizon planning, compositional reasoning, and efficient learning from limited data. His work bridges theory and practice, with applications in robotics, simulation platforms, and multimodal generation. Key contributions include frameworks for 3D scene understanding, adaptive world models, and novel training paradigms for large language models. His research has been applied to robotics platforms like RoboDreamer and UBSoft, focusing on unbounded soft environments. Collaborations involve designing benchmarks for physical scene understanding (e.g., Physion++), and creating tools like DiffTactile for tactile simulation. His work often integrates principles from differential geometry, PDE dynamics, and game theory. Chuang Gan’s research group develops open-source tools and benchmarks, such as the SoftZoo robot co-design platform and the SOK-Bench situated reasoning benchmark. His team emphasizes scalable alignment methods beyond human supervision and explores ethical AI through principles like symmetry-enhanced training.

Andrew Yonelinasمشاهده پروفایل

استاد

• Human Memory
• Cognitive Neuroscience
• Memory Processes
• +۵ مورد دیگر

Andrew Yonelinas is a Professor in the Department of Psychology at the University of California, Davis, where he directs the Human Memory Lab. He holds additional leadership roles as Associate Director of the Center for Mind and Brain and is an affiliated faculty member with the UC Davis Center for Neuroscience. His research bridges cognitive psychology and neuroscience to investigate fundamental memory mechanisms and their neural substrates. His educational background includes a Ph.D. in Experimental Psychology from McMaster University (1995) and a B.S. in Cognitive Science from the University of Toronto (1990). These foundational studies established his expertise in experimental methodologies and cognitive theory. Yonelinas specializes in dual-process models of memory, distinguishing between recollection (detailed contextual retrieval) and familiarity (vague recognition). His lab employs process dissociation, remember/know procedures, and ROC modeling alongside neuroimaging (fMRI, ERP) and clinical studies with amnesic and Alzheimer's patients. Recent work expands into auditory working memory, multisensory integration, and the impact of mental illness on cognitive processes, revealing hippocampal roles across memory systems. His research consistently addresses how memory fails in clinical conditions while developing unified theoretical frameworks. Analysis of his 2024-2025 publications shows a strong focus on memory mechanisms across sensory modalities, with increasing emphasis on clinical applications. Key trends include hippocampal contributions to visual/auditory working memory, EEG-based biomarkers for mental illness, and the interplay between schema knowledge and memory distortion in aging populations. His work demonstrates methodological innovation through model-based EEG phenotyping and multisite clinical collaborations. His scientific recognition includes: American Psychological Society’s Shahin Hashtroudi Memorial Award University of California Chancellor’s Fellow Award European Brain and Behavior Society International Lecture Award Yonelinas actively shapes his field through editorial roles at top journals including Proceedings of the National Academy of Sciences and Journal of Experimental Psychology, while serving as a grant reviewer for NIH, NSF, and international funding bodies. His Human Memory Lab trains next-generation researchers in memory theory and methodology, with recent projects examining stress effects on memory precision and neural mechanisms of action slips. The Human Memory Lab operates within UC Davis's neuroscience ecosystem, collaborating closely with the Center for Mind and Brain on projects involving clinical populations and neuroimaging. Current initiatives include the CNTRACS Consortium for EEG standardization in mental illness and investigations into how stress modulates memory binding through hippocampal mechanisms.

Jens Kreitewolfمشاهده پروفایل

عضو هیئت علمی

• Auditory Cognition
• Speech Perception
• Neuroimaging
• +۷ مورد دیگر

Jens Kreitewolf is a Faculty Lecturer in the Departments of Psychology and Mathematics and Statistics at McGill University. He teaches courses in statistics, research methodology, and psychophysics. His research focuses on auditory cognition, speech comprehension, and the neural mechanisms underlying voice perception. Dr. Kreitewolf holds a Ph.D. (Dr. rer. nat.) from Humboldt University of Berlin and completed postdoctoral fellowships at BRAMS and the University of Lübeck. His work combines experimental psychology, neuroimaging, and psychophysics to explore auditory processing challenges in adverse listening conditions. Key interests include how familiarity with a talker’s voice aids comprehension and the impact of hearing impairment on speech perception. Education: M.Sc. in Psychology (Ruhr University Bochum, 2009); Ph.D. in Psychology (Humboldt University of Berlin, 2014). Research Interests: Auditory scene analysis and speech-in-noise processing Voice recognition and familiarity effects Neural correlates of perceptual decision-making Circadian rhythms and perceptual sensitivity Cognitive neuroscience of auditory attention Publications highlight contributions to understanding: Risk factors for depression symptom progression Self-concept clarity in romantic evaluations Neurobiological mechanisms of working memory vulnerability Vestibular symptoms in migraine patients His interdisciplinary approach bridges psychology, statistics, and neuroscience, with applications to clinical populations and sensory processing disorders.

C. Daniel Melizaمشاهده پروفایل

دانشیار

• Systems Neuroscience
• Behavioral Neuroscience
• Auditory Neuroscience
• +۳ مورد دیگر

C. Daniel Meliza is an Associate Professor in the Department of Psychology at the University of Virginia. His research focuses on the neural mechanisms of auditory learning and perception, primarily using zebra finches as a model system to understand how brains process complex vocal communication. Dr. Meliza's research investigates how neural circuits enable auditory learning and perception in songbirds. His lab studies experience-dependent plasticity, examining how early acoustic environments shape auditory processing. They also investigate how birds form internal models of vocal signals and use them to reconstruct degraded communication in noisy environments. This work has implications for understanding speech perception and communication disorders in humans. His recent publications reveal a strong focus on intrinsic plasticity mechanisms in the auditory cortex, computational modeling of neural systems, and how experience shapes neural coding of vocalizations. The work spans from cellular mechanisms to systems-level processing, with increasing integration of computational approaches to understand neural dynamics. Dr. Meliza has received significant recognition for his research: NIH R01 grant from NIDCD to examine mechanisms of intrinsic plasticity in early auditory learning (2021) NSF CAREER Award to study neural mechanisms of auditory restoration (2020) UVA Presidential Fellowship for Collaborative Neuroscience (2022) Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship (2023) UVA Double Hoo Award (2023) Dr. Meliza has successfully mentored multiple PhD students including Yao Lu, Samantha Moseley, Christof Fehrman, and Margot Bjoring. His lab is well-funded through competitive grants from NIH and NSF, supporting research into the fundamental neural mechanisms underlying auditory learning and perception. The lab employs a multidisciplinary approach combining behavioral experiments, electrophysiology, computational modeling, and molecular techniques. The Meliza Lab at the University of Virginia operates at the intersection of neuroscience, psychology, and computational modeling. The team uses zebra finches to investigate how the brain processes complex vocal communication, with particular focus on how experience shapes neural circuits during development. Current research directions include examining how complex acoustic environments influence auditory perception and neural coding, and how neural circuits implement rapid gain control mechanisms.

Richard M. Sternمشاهده پروفایل

استاد

• Speech Recognition
• Audio Signal Processing
• Music Information Retrieval
• +۳ مورد دیگر

Richard M. Stern is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in the Language Technologies Institute and Department of Computer Science, and serving as an Artist Lecturer in the School of Music since 2007. His interdisciplinary work bridges engineering and music technology through the School of Music's programs. Education: Ph.D. in Electrical Engineering from Massachusetts Institute of Technology (MIT), 1976 Professor Stern's research spans sound, speech, hearing, and music, with core emphases on robust speech processing in variable acoustic environments, music information retrieval, automated accompaniment, and foundational contributions to binaural perception theory. His work integrates psychoacoustic principles with machine learning to address challenges in speech recognition and human-robot interaction. Recent publications (2022-2025) reveal intensified focus on deep learning for speech enhancement in reverberant/noisy conditions, human-robot interaction scenarios, and music tagging—highlighting innovations in beamforming, source separation, and temporal modulation modeling. Awards and Honors: Fellow of the IEEE Fellow of the Acoustical Society of America Fellow of the International Speech Communication Association (ISCA) ISCA Distinguished Lecturer Allen Newell Award for Research Excellence (1992) Lutron Award for Teaching Excellence (2018) Professor Stern has advised numerous graduate students in speech and audio research, though specific names are unlisted in source materials. His grant portfolio includes significant National Science Foundation and industry-funded projects in speech technology, with leadership roles in initiatives like Interspeech 2006. He actively collaborates with CMU's Language Technologies Institute and Music and Technology program. He maintains strong ties to CMU's interdisciplinary ecosystem through the Language Technologies Institute and School of Music's Music and Technology program, contributing to research that merges acoustic engineering with musical applications.

Kenneth S. Henryمشاهده پروفایل

دانشیار

• Auditory Neuroscience
• Cochlear Synaptopathy
• Neural Mechanisms of Hearing
• +۳ مورد دیگر

Dr. Kenneth S. Henry is an Associate Professor at the University of Rochester Medical Center (SMD) with joint appointments in the Departments of Otolaryngology, Biomedical Engineering, and Neuroscience. His research focuses on auditory neuroscience, cochlear synaptopathy, and neural mechanisms of hearing, using behavioral, neurophysiological, and histological approaches. Department of Otolaryngology Department of Biomedical Engineering Department of Neuroscience Research Interests: His lab investigates auditory nerve loss, hidden hearing loss, and neural bases of masked amplitude-modulation perception. Using avian and mammalian models, his work explores how cochlear neurodegeneration impacts behavioral and neural sensitivity to complex sounds in noise. Publications Trends: Recent research examines auditory nerve encoding, envelope processing in hearing loss, cochlear fluid dynamics, and neural mechanisms of speech-in-noise perception using avian models. Lab Members: Current researchers include graduate students Leslie Gonzales (Neuroscience) and Yingxuan Wang (Biomedical Engineering). Alumni include John Wilson (PhD student) and Steph Wong (ENT resident). Contact: Email kenneth_henry@urmc.rochester.edu or call (585) 275-4851 for collaboration/postdoc opportunities.

Marc G. Bermanمشاهده پروفایل

استاد

• Environmental Neuroscience
• Cognitive Psychology
• Urban Neuroscience
• +۷ مورد دیگر

Marc G. Berman is a Professor and Chair of the Department of Psychology at the University of Chicago. His research focuses on understanding how environmental factors interact with human cognition, emotion, and behavior, particularly through the lens of environmental neuroscience. He leads the Environmental Neuroscience Lab (ENL), investigating how natural and urban environments influence brain function, memory, attention, and mental health. Dr. Berman holds a B.S.E. in Industrial and Operations Engineering from the University of Michigan and a Ph.D. in Psychology and Engineering from the same institution. His postdoctoral training was at the Rotman Research Institute in Toronto. Prior to Chicago, he was an Assistant Professor at the University of South Carolina. His research interests include the cognitive and affective benefits of natural environments, brain network efficiency, and the neurobiological underpinnings of self-control and emotion regulation. Recent work explores how urban design elements (e.g., greenspace, street activity) relate to crime rates and mental health outcomes, leveraging big data from social media and geospatial tools. Key contributions include demonstrating that natural environments improve memory and attention by ~20%, and that city characteristics like population diversity and segregation correlate with implicit racial biases. His lab employs fMRI, neuroimaging, computational modeling, and ecological data to quantify brain-environment interactions. Dr. Berman collaborates across disciplines, integrating neuroscience, psychology, urban planning, and data science to inform evidence-based environmental design for public health. Current projects include analyzing social media data to map gang networks and investigating how heat and greenspace influence emotional states in urban populations.