جست‌وجوی استاد و پژوهشگر

Leila Wehbeمشاهده پروفایل

دانشیار

• Machine Learning
• Cognitive Neuroscience
• Neuroscience
• +۴ مورد دیگر

Leila Wehbe is an Associate Professor in the Machine Learning Department and Neuroscience Institute at Carnegie Mellon University (CMU), with affiliations in Psychology and Computational Biology. She leads a research group focused on understanding high-level brain representations of language and vision using machine learning techniques. Her work combines neuroimaging (fMRI/MEG) with computational models to investigate how the brain processes meaning and visual stimuli. Education : PhD in Machine Learning from CMU, advised by Tom Mitchell BE in Electrical and Computer Engineering from the American University of Beirut Postdoc at UC Berkeley's Helen Wills Neuroscience Institute with Jack Gallant Research Interests : Her research bridges cognitive neuroscience and AI, focusing on: Decoding language and visual processing from brain activity Developing machine learning models aligned with brain representations Investigating semantic composition in language Exploring visual cortex selectivity for objects/food Improving neural decoding with advanced methods (e.g., transformers, generative models) Awards & Recognition : NSF CAREER Award (2022) NIH R21/R01 Awards Human Frontier Science Program Award Google Faculty Research Award Grants & Labs : Leads the Wehbe Lab, part of brAIn at CMU. Active in grant programs including NSF and NIH, focusing on language-brain alignment and visual cortex studies. Co-organized workshops at ICLR and CVPR on brain-inspired AI.

Avniel Singh Ghumanمشاهده پروفایل

دانشیار

• visual cognition
• neural dynamics
• face recognition
• +۶ مورد دیگر

Dr. Avniel Singh Ghuman is an Associate Professor in the Department of Neurological Surgery at the University of Pittsburgh School of Medicine. He serves as Director of the Cognitive Neurodynamics Lab and plays a key role in advancing MEG (Magnetoencephalography) Research at the university. His work bridges clinical neurosurgery with fundamental neuroscience research to understand visual perception mechanisms. Dr. Ghuman's educational background includes: BA in Math and Physics from The Johns Hopkins University (1998) PhD in Biophysics from Harvard University (2007) Postdoctoral training at the National Institute of Mental Health Dr. Ghuman's research focuses on how the brain transforms visual input into meaningful perception of objects, faces, words, and social images in real-world contexts. His laboratory employs both invasive (intracranial EEG) and non-invasive (MEG) techniques to examine the spatiotemporal dynamics of neural activity during visual processing. The lab integrates multivariate machine learning methods, network analysis, and direct neural stimulation to investigate information processing at both local brain regions and distributed network levels. Recent work has pioneered methods for studying brain activity during authentic social interactions and natural behavior. His publication record demonstrates significant contributions to understanding real-world face perception, neural dynamics during natural behavior, and the application of advanced analytical techniques to brain imaging data. His research has revealed how brain network dynamics form a 'punctuated equilibrium' of stable states with transitory bursts between them, coinciding with behavioral shifts in everyday activities. Dr. Ghuman has received notable recognition for his work: Young Investigator Award from NARSAD (2012) Award for Innovative New Scientists from the National Institute of Mental Health (2015) His research has been featured in prominent media outlets including MIT Technology Review, The Wall Street Journal, and Carnegie Mellon University publications. As Director of the Cognitive Neurodynamics Lab, Dr. Ghuman leads a research program that has advanced our understanding of how the brain processes visual information in real-world environments, with implications for both fundamental neuroscience and clinical applications.

Daragh Byrneمشاهده پروفایل

عضو هیئت علمی

• Human-Computer Interaction
• Internet of Things (IoT)
• Tangible Interaction Design
• +۴ مورد دیگر

Daragh Byrne is an Associate Teaching Professor at the Carnegie Mellon University School of Architecture , with courtesy appointments in the School of Design and Human-Computer Interaction Institute (HCII) . Previously an Assistant Research Professor at Arizona State University’s School of Arts, Media and Engineering, he manages the NSF-funded XSEAD project and leads MakeSchools , a catalog of making practices in higher education. PhD in Digital Media from Dublin City University (2011) M.Res. in Design and Evaluation of Advanced Interactive Systems from Lancaster University B.Sc. in Computer Applications from Dublin City University His research explores experiential media systems through Internet of Things and tangible interaction design , focusing on how computational tools can capture human experience and enable multidisciplinary collaboration. Key projects include Sentient Concrete (thermochromic architectural surfaces) and Spooky Technology (speculative design around invisible technologies). He has developed CMU’s Designing for the Internet of Things course since 2016, creating hands-on curricula for connected product design. Recent publications examine creative physical computing education , AI-driven documentation systems , and XR-enabled skill training . Awards include multiple CMU research grants and the CHI 2018 Best Paper Award . He actively advises PhD and Masters students in Computational Design, emphasizing human-centered design and speculative technology research .

Kate Hongمشاهده پروفایل

استادیار

• Sensory Perception
• Neural Circuits
• Cortical Injury Recovery
• +۳ مورد دیگر

Dr. Kate Hong is an Assistant Professor in the Department of Biological Sciences and Neuroscience Institute at Carnegie Mellon University (CMU). She leads the Hong Lab, which investigates neural circuits underlying sensory-guided behavior, focusing on how cortical and subcortical regions mediate perception, decision-making, and recovery from injury. Her work combines electrophysiology, optogenetics, and behavioral assays using mouse models. Education: Ph.D. in Neuroscience, Harvard University Sc.B. in Biology, Brown University Postdoctoral Fellowships at Columbia University and Boston Children's Hospital Research Focus: Dr. Hong's lab explores two core questions: (1) How cortical-subcortical interactions drive sensory-motor decisions, and (2) How neural circuits reorganize after cortical injury. Recent studies include investigating texture discrimination mechanisms and recovery pathways in stroke models. Techniques used include high-speed imaging, optogenetics, and fMRI. Grants & Awards: Collaborative Grant from the RCSA (2023) SFARI Pilot Award to study autism-related tactile deficits Shurl and Kay Curci Foundation Grant (2022) Lab Team: PhD Students: Ben George, Alice Nam, Rawan Fakhreddine Undergraduates: Maggie Monahan (NASA Fellow), Baihe Zhang (SURFiN Scholar) Research Assistants: Morgan Tenney, Alex Lammers Future Directions: Ongoing projects include studying subcortical plasticity post-injury and developing new tools to map thalamocortical circuits in awake mice.

• Data Science
• Business Analytics
• Human-AI Interaction
• +۳ مورد دیگر

Zoey Jiang is an Assistant Professor of Business Technologies at the Tepper School of Business, Carnegie Mellon University since 2020. She holds a PhD from the University of Michigan–Ann Arbor (2020) and dual bachelor’s degrees in BA/BS from Peking University (2014). Her research bridges data science and operations management through innovative applications in AI collaboration, pricing optimization, and healthcare analytics. Education : PhD (University of Michigan–Ann Arbor, 2020), BA/BS (Peking University, 2014) Research Interests focus on: Human-AI collaboration frameworks High-dimensional choice modeling for retail analytics Competitive pricing mechanisms Crowdsourcing contest optimization Healthcare operations through behavioral data Key Publications demonstrate expertise in: Machine learning for substitution patterns in online retail Real-time AI assistance in healthcare Dynamic feedback policies in crowdsourcing Competitive pricing at scale Scientific Recognition : Recipient of Ross T&O Research Productivity Award (2019) Professional Contributions include: INFORMS TIMES Vice President (2025) Editorial reviewing for Management Science , Information Systems Research , and Manufacturing & Service Operations Management Teaching covers data science applications in business through courses like: Data Exploration and Visualization Data Mining & Business Analytics Seminar in Business Tech: Human-AI Interactions

Martial Hebertمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Autonomous Systems
• +۶ مورد دیگر

Martial Hebert is the Dean and University Professor of Robotics at Carnegie Mellon University's School of Computer Science (SCS), leading since August 2019. His career spans decades at CMU's Robotics Institute (RI), where he served as Director (2014-2019) and Professor (1999-present). Broad research interests in computer vision, perception for autonomous systems, and 3D environment modeling. Current PhD advisees include Zhipeng Bao, with numerous past PhD and Master's students listed. Editor-in-Chief of the International Journal of Computer Vision and member of IEEE Robotics and Automation Society. His research focuses on computer vision and robotics , emphasizing 3D data interpretation, object recognition, and machine learning applications. Recent articles highlight advancements in 3D vision , diffusion models , and disaster response robotics , reflecting a trajectory from foundational algorithms to applied autonomous systems. Notably, he pioneered the first master's program in computer vision in the U.S. Hebert's leadership in academic and research spheres includes directing the RI and securing an operating budget peak during his tenure. His work bridges perception, intelligence, and autonomous systems, with applications in disaster scenarios , LiDAR point cloud detection , and video forecasting .

Benoit Hudsonمشاهده پروفایل

استادیار

• Computational Geometry
• Scientific Computing
• Mesh Refinement
• +۷ مورد دیگر

Benoit Hudson is an Assistant Professor at the Toyota Technological Institute at Chicago, specializing in computational geometry and algorithm design with theoretical guarantees. His work bridges practical implementation and theoretical analysis in mesh refinement, with applications to scientific computing and computer graphics. He has also contributed to auction theory and hybrid systems simulation. Research interests include: Computational geometry Mesh refinement algorithms Scientific computing Data structures Algorithm design His recent publications analyze: Volume mesh size complexity Linear-time surface reconstruction Delaunay refinement guarantees Dynamic mesh updating Parallel mesh generation He maintains the sparse-meshing.com resource and previously worked on spacecraft diagnosis systems at NASA Ames Research Center. His academic advisors included Gary Miller (Carnegie Mellon University) and Tuomas Sandholm during early PhD years.

• Computer Vision
• Human Activity Forecasting
• First-Person Vision
• +۹ مورد دیگر

Kris M. Kitani is an Associate Research Professor at the Robotics Institute of Carnegie Mellon University and a Research Scientist at Meta's Artificial Intelligence division. He co-directs the Extended Reality Technology Center (XRTC) at CMU and holds courtesy appointments in the Electrical and Computer Engineering Department at CMU and as a Guest Professor at the University of Tokyo's Institute of Industrial Science. Education : BS in Electrical Engineering (University of Southern California), MS and PhD in Computer Science (University of Tokyo) Labs : Cognitive Assistance Laboratory His research focuses on computer vision , human activity forecasting , and inverse reinforcement learning with applications in human-centered robotics and assistive technologies . Recent work explores 3D scene understanding , multi-modal sensing , and generative models for physical simulation . Publications analyze pedestrian interaction modeling , human pose estimation , and zero-shot learning for robotic applications. Scientific Awards : Marr Prize Honorable Mention, ICCV 2017 Best Paper Honorable Mentions at CHI 2017/2020 Best Papers at W4A 2017/2019 Best Application Paper, ACCV 2014 Kitani has advised 8 current PhD students and 18 past PhD/MSc students. He has taught graduate-level courses in Computer Vision and Statistical Techniques in Robotics since 2013. The lab develops autonomous systems for real-world perception and interactive decision-making.

Shahriar Noroozizadehمشاهده پروفایل

پژوهشگر

• Representation Learning
• Machine Learning for Healthcare
• Multimodal Machine Learning
• +۴ مورد دیگر

Shahriar Noroozizadeh is a Ph.D. student at Carnegie Mellon University, jointly affiliated with the Machine Learning Department (School of Computer Science) and the Heinz College of Information Systems and Public Policy . Advised by Prof. George Chen (Heinz/MLD) and Prof. Jeremy Weiss (National Library of Medicine at NIH), he focuses on interpretable representation learning for temporal data in healthcare , particularly for patient risk progression modeling and hospital discharge delay prediction. Education : Ph.D. in progress (ML & Public Policy, CMU); M.S. in Machine Learning (2022) and Biomedical Engineering (2020) from CMU; B.A.Sc. in Engineering Physics (EECS specialization) from UBC. His research bridges machine learning methodology with high-stakes decision-making systems in healthcare, emphasizing interpretability, contrastive learning, and multimodal representation. He has pioneered supervised contrastive frameworks that outperform traditional models in clinical outcome prediction and synthetic dataset recovery, while also exploring embodied AI and large language models for molecular design and robotics. Recent work includes a patent-pending mRNA language model (mRNA-LM) for structure prediction and translation efficiency analysis, developed during his A.I. Research Scientist Internship at Sanofi . His publications span venues like NeurIPS , AAAI , and ML4H , with notable improvements in MIMIC-III and ADNI datasets . Key Awards : TCS Presidential Fellowship, NSERC CGS-D Doctoral Fellowship, CMU CMLH Digital Health Innovation Fellowship, Suresh Konda Memorial Award, and multiple undergraduate honors.

Linda Moyaمشاهده پروفایل

استاد

• Neuroscience
• Decision Science
• Cognitive Neuroscience
• +۳ مورد دیگر

Linda H. Moya is a Professor in the Social and Decision Sciences department at Carnegie Mellon University, with affiliations to the Heinz College and the Center for the Neural Basis of Cognition (CNBC). She holds a Ph.D. in Psychology/Cognitive Neuroscience and a Master of Philosophy in Public Policy and Management from Carnegie Mellon University, along with a Master of Science in Industrial Engineering from Stanford University and a Bachelor of Science and Engineering in Electrical Engineering and Computer Science from Princeton University. Ph.D. in Psychology/Cognitive Neuroscience, Carnegie Mellon University M.Phil. in Public Policy and Management, Carnegie Mellon University M.S. in Industrial Engineering, Stanford University B.S. in Electrical Engineering and Computer Science, Princeton University Linda's research focuses on the neuroscience of decision making, particularly how the social and emotional brain influences decision processes. She employs behavioral experiments, psychophysiology, and neuroimaging methods like MEG, EEG, and structural MRI to test hypotheses derived from interdisciplinary neuroscience theories. Her dissertation, The Microgenesis of Object-based vis-a-vis Space-based Visual Attention , explores the temporal and spatial dynamics of visual attention mechanisms via electromagnetic field recordings. Linda serves as Program Director for the Summer Undergraduate Program in Computational Neuroscience (CNBC) and has taught courses such as 88120 Reason, Passion and Cognition , 94800 Negotiation , and 88342 The Neuroscience of Decision Making , among others. She integrates advanced statistical learning techniques into her research to explore both predicted and emergent patterns in data.

David Plautمشاهده پروفایل

استاد

• Computational Neuroscience
• Cognitive Psychology
• Neural Networks
• +۵ مورد دیگر

David Plaut is a Professor at Carnegie Mellon University's Neuroscience Institute and Department of Psychology , with secondary appointments in computational neuroscience. His work combines connectionist modeling with empirical studies to explore cognitive processes in high-level vision, reading, language, and semantics. Holding a Ph.D. in Computer Science (CMU, 1991) and a B.A. in Cognitive Science & Mathematics (Rochester, 1984), Plaut's research spans three decades of computational neuropsychology. Education Ph.D., Computer Science, Carnegie Mellon University (1991) B.A., Cognitive Science & Mathematics, University of Rochester (1984) His research focuses on distributed neural networks and cognitive impairments . Key contributions include modeling semantic over-activation in N400 ERP components, hemispheric lateralization mechanisms, and topographic organization of visual cortex. Recent work examines visual word form networks and face processing through iScience and Imaging Neuroscience publications. Scientific recognition includes the Troland Research Award (2003), Fulbright Scholarship (2000), and NIH FIRST award (1997). Current grants (2022-26: NIH R01EY027018; 2021-25: NSF BCS-2123069) support studies on cortical reorganization and hemispheric specialization. Training initiatives include teaching Introduction to PDP (1994-present) and Cognitive Neuropsychology (2023-present). The Plaut Lab trains graduate students in Neural Computation and Psychology , with postdocs like Kelly Martin (2024-) and Masataka Nakayama (2016-18) now holding academic positions.

• Computer Vision
• Human Activity Forecasting
• First-Person Vision
• +۵ مورد دیگر

Kris Kitani is an Associate Research Professor at the Robotics Institute and Courtesy Professor in Electrical and Computer Engineering at Carnegie Mellon University (CMU). He also holds roles as a Research Scientist at Meta FAIR and Co-Director of CMU's Extended Reality Technology Center (XRTC). His research focuses on computer vision, human activity forecasting, first-person vision, inverse reinforcement learning, and assistive technologies for visually impaired individuals. Kitani's education includes a BS from the University of Southern California, and MS and PhD from the University of Tokyo. His work bridges perception, decision-making, and interaction in autonomous systems, with applications in wearable cameras, assistive devices, and human-robot collaboration. He has advised numerous PhD and master's students, including current researchers like Jinkun Cao and Erica Weng. Key awards include the Marr Prize honorable mention (ICCV 2017), Best Paper Honorable Mentions at CHI 2017/2020, and Best Paper Awards at W4A 2017/2019. His lab, the Cognitive Assistance Laboratory, develops systems for robust real-world perception and interactive decision-making. Kitani teaches courses such as Graduate Computer Vision (16-720) and Undergraduate Computer Vision (16-385) at CMU. His research spans robotics, AI, and human-centered computing, with a focus on first-person vision and assistive technologies.

Franceska Xhakajمشاهده پروفایل

عضو هیئت علمی

• Educational Technology
• Intelligent Tutoring Systems
• Learning Analytics
• +۳ مورد دیگر

Franceska Xhakaj is an Assistant Teaching Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the School of Computer Science. She specializes in educational technology, intelligent tutoring systems, and learning analytics. Her work focuses on leveraging technology to enhance teaching practices, particularly through classroom sensing, data-driven instruction, and dashboard design for educators. She teaches courses such as 15110 ('Fundamentals of Programming and Computer Science'), 15121 ('Great Ideas in Theoretical Computer Science'), and 15890 ('Special Topics in CS'). Her research integrates artificial intelligence and human-computer interaction to improve educational outcomes, including studies on teacher dashboards, gaze-tracking classroom digital twins, and heuristic evaluations via multimodal LLMs. She has contributed to systems like EduSense and Luna, aiming to scale practical classroom analytics and support teacher professional development. Her teaching responsibilities include multiple semesters of introductory programming courses (2023-2025), reflecting her dual focus on both research innovation and foundational computer science education. Key projects emphasize bridging gaps between technological tools and pedagogical needs, with recent work exploring instrumentation-free classroom monitoring and adaptive learning systems.

Jessica Cantlonمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Developmental Psychology
• Quantitative Reasoning
• +۳ مورد دیگر

Jessica Cantlon is the Ronald J. and Mary Ann Zdrojkowski Professor of Developmental Neuroscience/Psychology at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. She leads the Concepts, Actions, and Objects Lab and the Kid Neuro Lab, focusing on the neural and cognitive foundations of quantitative reasoning in humans and non-human primates. Her research employs behavioral and neuroimaging techniques to explore how numerical and spatial concepts develop in children and how these relate to evolutionary processes. Key research themes include cross-species comparisons of numerical processing (e.g., studies on rhesus monkeys and olive baboons), the role of the intraparietal sulcus (IPS) in numerical cognition, and the impact of environmental and gender factors on mathematical development. She has contributed to debates on core knowledge theories, semantic frameworks, and the evolution of human intelligence. Her work bridges developmental psychology, cognitive neuroscience, and comparative psychology, with implications for education and public health policy. Her labs emphasize interdisciplinary approaches, combining fMRI studies with behavioral experiments to understand how abstract concepts emerge from neural systems. Current projects investigate recursive pattern processing, hierarchical reasoning, and the neural basis of mathematical learning from naturalistic environments like educational videos.

David Heldمشاهده پروفایل

دانشیار

• Robotics
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

David Held is an Associate Professor at Carnegie Mellon University's Robotics Institute, leading the Robots Perceiving And Doing (RPAD) lab. His work focuses on perceptual robot learning, integrating robotics, machine learning, and computer vision to enable robots to interact with complex environments. He holds a Ph.D. in Computer Science from Stanford University, an M.S. and B.S. in Mechanical Engineering from MIT, and conducted postdoctoral research at UC Berkeley. His research spans object manipulation, autonomous driving, and reinforcement learning, with a focus on robust perception and control in dynamic settings. Research Interests: Developing methods for robots to manipulate novel objects, handle deformable materials, and operate in unstructured environments through deep learning and simulation-to-real transfer. He explores autonomous driving via self-supervised learning and semi-supervised techniques. Notable Articles (2024–2025): Focus on articulated object manipulation, sim2real transfer, safety-aware policies, and perception in robotics. Recent work includes ArticuBot for universal manipulation policies and SplatSim for zero-shot transfer using Gaussian splatting. Awards: Google Faculty Research Award (2017), NSF CAREER Award (2021). Labs: RPAD Lab, CMU Center for Autonomous Vehicle Research. Teaching: Courses include Statistical Techniques in Robotics and Advanced Computer Vision.