جستجوی استاد راهنمای دانشگاهی

John F. Beacomمشاهده پروفایل

استاد

• Neutrinos
• Dark Matter
• High-Energy Astrophysics
• +۵ مورد دیگر

John F. Beacom is a Distinguished Professor of Physics and Astronomy at The Ohio State University and Director of the Center for Cosmology and AstroParticle Physics (CCAPP). His academic roles include leadership in astroparticle physics research and education. He holds joint appointments in both the Department of Physics and the Department of Astronomy within the College of Arts and Sciences. Beacom earned his Ph.D. in Physics from the University of Wisconsin (1997) and dual B.S. degrees in Physics and Mathematics from the University of Kansas (1991). He has held postdoctoral positions at Fermilab and Caltech before joining Ohio State in 2004. His research focuses on neutrinos, dark matter, and multi-messenger astrophysics, with emphasis on neutrino detection techniques, supernova physics, and cosmological implications. He leads major projects like the All-Sky Automated Survey for Supernovae (ASAS-SN) and contributes to the Deep Underground Neutrino Experiment (DUNE). Awards: APS Fellow (2014), NSF CAREER Award (2005–2010), multiple teaching awards for distinguished instruction. Grants: Extensive funding from NSF, DOE, and collaborative international initiatives. Labs/Teams: CCAPP, DUNE Collaboration, ASAS-SN project. His articles span neutrino physics, detector development, and observational astrophysics, reflecting interdisciplinary expertise in theoretical and experimental particle astrophysics.

Jean Ohمشاهده پروفایل

پژوهشگر

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۵ مورد دیگر

Jean Oh is a Researcher at the Robotics Institute of Carnegie Mellon University (CMU) , leading the interdisciplinary Bot Intelligence Group (BIG) . Her work focuses on developing persistent robots that co-exist and collaborate with humans in shared environments, emphasizing continuous improvement through training, exploration, and human interaction. Education: Ph.D. in Language and Information Technologies, CMU M.S. in Computer Science, Columbia University B.S. in Biotechnology, Yonsei University Oh's research integrates vision, language, and planning systems in robotics, with applications in human-robot teaming , self-driving cars , disaster response , eldercare , and creative robotics . She has pioneered projects like socially-compliant robot navigation in human crowds and AI-driven robotic painting systems. Recent publication trends highlight her work in vision-language planning , social navigation , computational creativity , and human-robot collaboration . Notable contributions include the StyleCLIPDraw algorithm for text-to-art generation and Social-PatteRNN for human-like trajectory prediction. Scientific Awards: Best Paper Award in Cognitive Robotics (ICRA'18, ICRA'15) Best Systems Paper Finalist (HRI'25) Best Oral Paper Finalist (Humanoids'24) Best Paper in Entertainment (IROS'24) Argoverse Challenge Winner (CVPR'24) Best Student Paper (AIAA'24) Best Demo Finalist (RoboSoft'24) Oh mentors a diverse team of PhD, MS, and undergraduate students from CMU departments including Robotics, Computer Science, and Mechanical Engineering. Her research is funded by US Army Research Lab , DiDi Chuxing , and DARPA , with collaborations across industry and academia .

Russell Epsteinمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Behavioral Neuroscience
• Spatial Navigation
• +۴ مورد دیگر

Russell Epstein is a Professor and Director of Graduate Studies in the Department of Psychology at the University of Pennsylvania. He is affiliated with the Center for Cognitive Neuroscience and Goddard Labs. His research focuses on neural mechanisms underlying visual scene perception, spatial navigation, and memory. Epstein holds a BA in Physics from the University of Chicago and a PhD in Applied Mathematics from Harvard University. Epstein’s research interests include high-level vision, spatial cognition, and the neural basis of environmental representations. His lab uses functional MRI and cognitive neuroscience techniques to study how scenes, objects, landmarks, and spaces are encoded in brain systems such as the parahippocampal place area and retrosplenial cortex. Recent work explores cognitive maps, grid-like neural representations, and the role of multisensory cues in navigation. His articles emphasize spatial navigation strategies, hierarchical cognitive maps, and the interplay between perception and memory. Notable contributions include investigations into hippocampal spatial metrics, olfactory navigation, and the neural underpinnings of environmental learning. Epstein teaches courses on cognitive neuroscience, including PSYC 149 and PSYC 600. He advises two graduate students in Psychology and has no listed scientific awards. His work is supported by grants (unspecified) and conducted within collaborative teams at the Center for Cognitive Neuroscience. Epstein’s research extends to labs focused on spatial cognition and neuroimaging, advancing understanding of how humans mentally map environments through visual and sensory integration.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +۳ مورد دیگر

Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.

Mathieu Salzmannمشاهده پروفایل

دانشگاهی

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Mathieu Salzmann is a Senior Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) in the School of Computer and Communication Sciences (IC). He also holds a courtesy appointment with the EPFL College of Humanities and serves as Deputy Chief Data Scientist at the Swiss Data Science Center (SDSC). He has held concurrent roles in teaching units including SIN, SODH, and SSC, reflecting his interdisciplinary engagement. His research focuses on the intersection of machine learning and computer vision, particularly in deep learning for 2D and 3D visual scene understanding, efficient and robust models, domain adaptation, and interpretable AI. These interests are evident across his extensive publication record in top-tier venues. His recent publications (2023–2024) show a consistent trend in advancing deep learning methods for visual recognition, with strong representation at CVPR, ICCV, ECCV, ICML, ICLR, and NeurIPS. Topics include domain generalization, 3D understanding, model robustness, and multimodal learning, often with applications in real-world systems. His editorial roles as Associate Editor for IEEE TPAMI and Action Editor for TMLR further highlight his leadership in the field. Area Chair: ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024 Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Action Editor: Transactions on Machine Learning Research (TMLR) Mathieu Salzmann has supervised numerous PhD students at EPFL, both current and past, including Bouquet Yann Yanis, Javed Saqib, Li Shuangqi, and others. He has also been involved in research grants and collaborative projects, such as his work with S. Süsstrunk and R. Baroni on comics reconfiguration. His part-time role as Senior GNC Engineer at ClearSpace (2020–2024) illustrates his applied research engagement in aerospace systems. He is actively involved in EPFL’s data science and AI research ecosystem through SDSC and multiple labs.

Elizabeth Froodمشاهده پروفایل

دانشیار

• Ancient Egyptian self-presentation
• Biographical texts
• Graffiti
• +۵ مورد دیگر

Elizabeth Frood is an Associate Professor of Egyptology at the University of Oxford, affiliated with the Faculty of Asian and Middle Eastern Studies. She holds dual college fellowships as a Fellow of St Cross College and Honorary Fellow of The Queen's College, with research centered on ancient Egyptian self-presentation, sacred landscapes, and social experience including gender and disability studies. Her academic journey began with first degrees completed in Aotearoa New Zealand, establishing her foundation in Egyptology before her current Oxford appointment. Professor Frood's scholarship uniquely reconstructs non-royal Egyptian lives through biographical texts, graffiti, and visual culture, specializing in the late New Kingdom and Third Intermediate Period (mid-second to early first millennium BCE). Her work bridges textual analysis with material culture studies, examining how individuals expressed identity through sacred spaces, ritual practices, and social interactions. This approach has generated significant insights into elite self-representation and non-elite participation in religious contexts. Analysis of her 14 most recent publications (2007-2023) reveals consistent thematic threads: sustained focus on Karnak temple graffiti projects, evolving methodologies in biographical text interpretation, and increasing interdisciplinary engagement with gender/disability studies. Her collaborative work with the Centre Franco-Égyptien d’Étude des Temples de Karnak demonstrates fieldwork integration with epigraphic analysis. Her academic distinctions include: Fellow of St Cross College Honorary Fellow of The Queen's College Professor Frood actively mentors doctoral researchers across diverse topics including Deir el-Medina statuary, Hatshepsut's iconoclasm, ancient graffiti contexts, gendered domestic spaces, and kinship practices. Her Karnak Graffiti Project (2011-present), co-directed with Chiara Salvador and supported by CFEETK, represents a major sustained research initiative involving fieldwork at the temple of Ptah and eighth pylon, yielding publications, media features, and international scholarly collaborations. She co-directs the Karnak Graffiti Project, a decade-long international collaboration with the Centre Franco-Égyptien d’Étude des Temples de Karnak and Montpellier-based scholars, combining archaeological documentation, epigraphic recording, and digital analysis of temple graffiti across multiple Karnak sectors.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Andrew Zisserman is a Royal Society Research Professor at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). His research focuses on computer vision, artificial intelligence, and neural networks, with significant contributions to multimodal learning, video understanding, and 3D scene analysis. He leads projects exploring visual-language models, audio-visual synchronization, and clinical imaging applications. Key research areas include: Video analysis and temporal modeling Multimodal systems for sign language translation and action recognition 3D shape estimation and physical property inference Foundation models and cross-modal retrieval Recent work highlights: Developed Flamingo and Tapir models for video-language tasks Advancements in spinal MRI analysis and clinical imaging Leadership in EGO4D and VoxCeleb challenges Honors include Fellowship of the Royal Society (FRS) and the ISSLS Prize in Clinical Science 2023 for spinal analysis innovations. His lab collaborates globally, emphasizing real-world applications in healthcare and autonomous systems.

Jeff Huangمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Data Science
• Health Informatics (eHealth/mHealth)
• +۳ مورد دیگر

Jeff Huang is an Associate Professor and Associate Chair of Computer Science at Brown University. His research focuses on Human-Computer Interaction (HCI), with emphases on personalized systems using behavioral data, health informatics, and user interfaces. Huang holds a PhD from the University of Washington and degrees from the University of Illinois at Urbana-Champaign. His work is funded by NSF, NIH, and ARO, and he has received awards including the NSF CAREER Award and ARO Young Investigator Award. Education: PhD in Information Science, University of Washington (2013) MS in Computer Science, University of Washington (2013) BS and MS in Computer Science, University of Illinois at Urbana-Champaign (2007) Research Interests: His work spans health informatics (e.g., sleep tracking systems like Self-E and SleepCoacher), user interface design, and leveraging behavioral data for personalized systems. Notable projects include WebGazer (webcam eye tracking), filtered.ink (creative illustration tools), and Sochiatrist (mental health analytics). Teaching: He teaches courses on user interfaces, HCI, and research methods, including CSCI 0130/1300 (User Interfaces), CSCI 2300 (HCI Seminar), and CSCI 2000 (Research Methods). Awards: NSF CAREER Award Facebook Fellowship ARO Young Investigator Award Grants & Advising: Huang leads a research group advising over 50 students, many of whom have pursued academic and industry roles. His grants support projects in HCI, health tech, and collaborative systems.

• Computer Vision
• Machine Learning
• Data-Driven Learning
• +۵ مورد دیگر

Alexei A. Efros is the Howard Friesen Professor in the EECS Department at the University of California, Berkeley, and a core member of the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at Carnegie Mellon University’s Robotics Institute. His research focuses on data-driven computer vision, self-supervised learning, computational photography, and generative models. He has pioneered advancements in visual representation learning, including seminal work on neural radiance fields and generative adversarial networks. Education Background: Efros holds a PhD in Computer Science from MIT, though specific details of his academic journey are not explicitly provided in the text. His career includes postdoctoral research at the University of Oxford with Andrew Zisserman and collaborative work with Team WILLOW at INRIA Paris. Research Interests: Efros explores how vast uncurated visual data can be leveraged for understanding and synthesizing the visual world. Key areas include self-supervised learning, generative models, and applications in robotics and art. His lab has contributed influential techniques such as Style Transfer, GAN-based image synthesis, and neural scene representation learning. Recent work emphasizes real-time adaptation (Test-Time Training), 3D perception models, and ethical AI implications of generative systems. Publications: Over 150+ publications span topics like Generative Adversarial Networks (GANs), unsupervised learning, and visual-linguistic models. Notable works include Unpaired Image-to-Image Translation (CUT/GAU), Style Transfer , and Swapping Autoencoder . His research has significant industry impact, with techniques adopted in Adobe’s software and generative AI applications. Grants & Collaborations: Efros has secured major funding from NSF, DARPA, and industry partnerships (e.g., Adobe, NVIDIA). He co-leads projects on scalable vision models, ethical AI, and real-world perception systems. Current collaborations include work with MIT, NYU, and INRIA Paris. Labs & Teams: Leads the BAIR Vision Group at Berkeley, fostering interdisciplinary research between computer vision, graphics, and robotics. The group emphasizes Slow Science principles, prioritizing deep exploration over rapid publication.

Zhun Dengمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Theoretical Computer Science
• +۴ مورد دیگر

Zhun Deng is a tenure-track Assistant Professor at the Department of Computer Science, University of North Carolina at Chapel Hill. His research bridges machine learning, statistics, and theoretical computer science, focusing on rigorous frameworks for responsible AI systems. He previously held postdoctoral positions at Columbia University and completed his Ph.D. at Harvard's Theory of Computation group under Cynthia Dwork. Ph.D. in Computer Science, Harvard University (2022) B.Sc. in Mathematics, Chu Kochen Honors College, Zhejiang University His research spans theoretical foundations of machine learning, including: Quantile-based risk control and conformal prediction Fairness guarantees in algorithmic decision-making Uncertainty quantification for LLMs Copyright frameworks for generative AI Physics-informed hybrid models Multi-agent reinforcement learning with constraints Recent work analyzes LLM alignment through distribution-free methods (ICML 2025), explores performativity challenges (ICML 2025), and develops calibration techniques (ICLR 2024). Collaborations include research interns from Stanford, MIT, and NYU. Students in his group include: Ruomeng Ding (Ph.D., UNC) Xiaowei Yin (Ph.D., UNC) Kaicheng Zhang (Ph.D., UNC)

Qi Wuمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Vision-to-Language
• +۴ مورد دیگر

Qi Wu is an Associate Professor in the Department of Computer Science, School of Computer and Mathematical Sciences, Faculty of Sciences, Engineering and Technology at the University of Adelaide, based at the Australian Institute for Machine Learning on North Terrace campus. His decade-long career in computer vision includes leadership in Vision-and-Language research and prior roles as an ARC Senior Research Associate at the Australian Centre for Robotic Vision (ACRV) and a Postdoc Researcher at the Australian Centre for Visual Technologies (ACVT). His educational qualifications consist of a Master of Science (MSc) in Global Computing and Media Technology (2011) and a PhD in Computer Science (2015), both from the University of Bath, United Kingdom. Research expertise centers on computer vision and machine learning , with pioneering work in Vision-to-Language systems. Key contributions include state-of-the-art models for image captioning and visual question answering (VQA) , contextual object modeling across depictive styles, and object detection using environmental cues. His focus on neural architectures for image-text interaction has established him as a leader in multimodal AI. Scientific recognition includes: Microsoft COCO Image Captioning Challenge winner (2015) Visual Question Answering Challenge winner (2015) Dr Wu is eligible to supervise Masters and PhD students but is currently at capacity; prospective students must email directly for availability discussions. His research has been supported by Australian Research Council (ARC) funding through prior Senior Research Associate and Postdoctoral roles. Current work involves leading a dedicated Vision-and-Language team at the University of Adelaide, building on his extensive publication record in top venues. He maintains active affiliations with the Australian Institute for Machine Learning and previously contributed to the Australian Centre for Robotic Vision (ACRV) and Australian Centre for Visual Technologies (ACVT), reflecting deep integration with national AI research infrastructure.

Huaxiu Yaoمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Robotics
• +۷ مورد دیگر

Huaxiu Yao is an Assistant Professor at the University of North Carolina at Chapel Hill, holding a joint appointment in the School of Data Science and Society and the Department of Computer Science (College of Arts & Sciences). His research focuses on building reliable large-scale AI models (foundation models) with applications in healthcare, robotics, genomics, and transportation. He leads the AIMING Lab, which explores adaptive intelligence through alignment, interaction, and learning. Education: Ph.D. from Pennsylvania State University (2021), Postdoctoral Scholar at Stanford University (hosted by Chelsea Finn). Research Interests: Generalizable AI agents, preference alignment, out-of-distribution generalization, embodied AI, and multimodal reasoning. Key applications include biomedicine, robotics, and vision-language systems. Notable Awards: KDD Best Paper Award (2024), Amazon Research Awards (2025), TMLR Outstanding Paper Award (2024). Advising & Labs: Recruits Ph.D. and intern students. Leads the AIMING Lab, affiliated with UNC NLP Group. Organizes workshops on foundation models (ICML 2024) and trustworthy AI systems. Publications: Over 40 peer-reviewed papers, including top venues like ICLR, NeurIPS, and ICML. Focuses on AI alignment, multimodal systems, and domain generalization.

Eytan Adarمشاهده پروفایل

استاد

• Human-Computer Interaction
• Artificial Intelligence
• Data Mining
• +۷ مورد دیگر

Eytan Adar is a Professor of Information and Computer Science at the University of Michigan, holding dual appointments in the School of Information and the College of Engineering's Electrical Engineering and Computer Science department. His work sits at the intersection of human-computer interaction and artificial intelligence, focusing on large-scale systems analysis and novel interface design. Adar's research spans multiple domains including social media analysis (Twitter, Reddit), academic citation networks, meme propagation, information extraction, and political networks. His methodological expertise includes data mining, data visualization, and graph-based network analysis. His work often operates at internet scale, examining language, creativity, social network dynamics, and text production. His recent publications reveal a strong focus on AI-human collaboration, with particular attention to generative AI interfaces, visualization techniques for complex data, and ethical considerations in AI systems. His work shows a consistent pattern of bridging theoretical insights with practical system implementations. Best Paper Award at ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces, IUI'21 Honorable Mention Award at CHI'24 for feminist interaction techniques research Best Paper Award at ICWSM'18 for Wikipedia language edition analysis Best Paper Award at ICML 2016 Workshop for neural language model visualization Best Student Paper at WSDM'09 for web dynamics research Best of CHI at CHI 2008 for web revisitation patterns analysis Adar has advised numerous PhD students who have gone on to prominent positions at organizations including Google, Apple, RAND, Northwestern University, and Stanford. His research is generously supported by the NSF, IARPA, NIH, the Education Department, and major technology companies including Adobe, Microsoft, Facebook, Google, and Yahoo. He is also a founder of the International Conference on Web and Social Media (ICWSM) and has served as Co-General Chair for WSDM and Co-Program Chair for UIST.

Alaina Lemonمشاهده پروفایل

استاد

• Socio-cultural anthropology
• Performance and language
• Historical narrative
• +۵ مورد دیگر

Alaina Lemon is a Professor of Anthropology at the University of Michigan, affiliated with the College of Literature, Science, and the Arts (LSA). She serves as Director of Undergraduate Studies in Anthropology and holds affiliations with the Center for Russian, East European, and Eurasian Studies (CREES), the Weiser Center for Europe and Eurasia, and the Interdepartmental Program in Anthropology and History. Her research focuses on socio-cultural anthropology, performance studies, and visual culture, with expertise in Russia, the Romani diaspora, and post-Socialist states. Education: BA in Anthropology (University of Wisconsin, 1987), MA (University of Chicago, 1988), and PhD (University of Chicago, 1996). Notable awards include the Wayne S. Vucinich Book Prize (2001), Heldt Book Prize (2001), and a 2023 visiting scholar position at the École des Hautes Études en Sciences Sociales (Paris). Her work bridges historical and contemporary issues, exploring themes like phatic labor, media infrastructures, and affective politics. Recent projects include editing the documentary Tremors (2023) and researching Soviet-era moral projects through studies of Moscow’s Metro and canine-human interactions. Lemon’s interdisciplinary approach integrates performance studies, linguistic anthropology, and critical race theory to analyze cultural dynamics in transitional societies. Grants include a 2019-2024 University of Michigan Office of Research grant for film collaboration and a 2002-2003 International Research and Exchanges Board fellowship. She has authored influential books such as Between Two Fires (2000) and Technologies for Intuition (2017), and her films document Romani experiences and urban cultural phenomena.

Carlo Harveyمشاهده پروفایل

استاد

• Virtual Reality
• Game Design
• Computer Graphics
• +۸ مورد دیگر

Professor Carlo Harvey is a creative technologist at the School of Digital Arts (SODA), Manchester Metropolitan University. His interdisciplinary research merges games , machine learning , virtual production , and cultural heritage reinterpretation . He leads industry collaborations with entities like Jaguar Land Rover and Epic Games, focusing on AI-driven interactive audio, real-time visualization, and accessibility solutions. Award-winning projects : TIGA, Innovate UK, and Epic Games MegaGrant for Accession Industry partnerships : Automotive sector, cultural institutions His research spans human-computer interaction , multisensory virtual environments , and acoustic-visual cross-modal perception . Recent publications address robotic simulations, motion alignment, and haptic feedback systems. Scientific recognition : TIGA Award, Innovate UK Funding, Epic Games MegaGrant Advocacy : Digital inclusion, creative collaboration, social impact of technology