جست‌وجوی استاد و پژوهشگر

Winnie Wongمشاهده پروفایل

استاد

• Art History
• Fakes and Forgeries
• Art and Law
• +۴ مورد دیگر

Winnie Wong is a Professor of Rhetoric at the University of California, Berkeley. Her research focuses on art history, particularly exploring themes of fakes, forgeries, and authorship through interdisciplinary approaches. She examines the global artistic networks of Hong Kong, Guangzhou, and Shenzhen, challenging Western narratives of Chinese artistic practices. Wong holds a PhD from MIT's History, Theory + Criticism program and was a Harvard Society of Fellows Junior Fellow. Her major works include Van Gogh on Demand: China and the Readymade (2014, Joseph Levenson Prize winner), Learning from Shenzhen (2017), and a forthcoming book on Canton trade portraitists. She teaches courses like Theory of the Copy and Rhetoric of the Image , mentoring undergraduates through the Undergraduate Research Apprentices program and advising PhD students in art history and visual studies. Wong's research has been supported by grants from Mellon Foundation, ACLS, SSRC, and others. Her work has been translated into multiple languages and appears in journals like Current Anthropology and Law & Literature . She frequently discusses her work on podcasts and collaborates with museums including M+ and the Asian Art Museum.

Lillie Padillaمشاهده پروفایل

استادیار

• Afro-Latinx Representation
• Discourse Analysis
• Language Ideologies
• +۴ مورد دیگر

Lillie Padilla is an Assistant Professor of Spanish Linguistics at Sam Houston State University's Department of World Languages and Cultures. Holding a Ph.D. and M.A. in Spanish Linguistics from Arizona State University alongside a Bachelor of Arts from the University of Ghana, her research explores interdisciplinary connections between: Afro-Latinx representation in educational materials Discourse analysis and language ideologies Sociolinguistic studies in Equatorial Guinea Critical race theory applications in language teaching Her work focuses on Spanish subject pronoun expression patterns in multilingual contexts and curriculum development for inclusive pedagogy. Recent publications examine: Representation gaps in Spanish language textbooks Methodological challenges in Equatorial Guinea research Critical discourse analysis of Netflix's Elite series Intersectional representation of Afro-Latinx women Scientific achievements include: Finalist for American Association of Linguistics 2025 award for co-edited volume Representation, Inclusion and Social Justice in World Language Teaching Padilla's research continues to shape conversations about linguistic diversity and marginalized voices in language education through both empirical studies and theoretical frameworks.

Sasha Su-Ling Wellandمشاهده پروفایل

استاد

• Art
• Asian American Studies
• Chinese Studies
• +۶ مورد دیگر

Sasha Su-Ling Welland is Chair & Professor in Gender, Women & Sexuality Studies at the University of Washington, with an adjunct role in Anthropology and affiliation in China Studies. She holds a PhD (2006) and MA (1998) in Anthropology from UC Santa Cruz, and a BA (1991) in Ethnic Studies from Stanford University. Her research focuses on transnational visual culture, women of color, ecocriticism, and post-socialist gender dynamics. Current work explores embodied ecologies, nuclear colonialism, and racial capitalism in the Anthropocene. Welland’s scholarship bridges art, ethnography, and public scholarship. Her book Experimental Beijing (2018) won major prizes for its analysis of gender in Chinese contemporary art. Recent research intertwines military/medical histories with environmental justice in St. Louis. She curates feminist art exhibits in Asia and serves on the Journal of Visual Culture editorial board. Her awards include the 2019 Francis L.K. Hsu Book Prize and 2020 Joseph Levenson Prize. She advises graduate students and leads lab teams investigating transnational feminisms and environmental humanities. Resources include her CV and online office hours via the department’s website.

Ryan Heuserمشاهده پروفایل

استادیار

• Digital Humanities
• Computational Prosody
• Literary and Intellectual History
• +۵ مورد دیگر

Ryan Heuser is an Assistant Professor in Digital Humanities at the University of Cambridge, specializing in computational approaches to literary and intellectual history, prosody, and artificial intelligence's impact on language. His work bridges data science, machine learning, and literary studies through digital methodologies. Doctoral training in Eighteenth-Century British Literature, Stanford University (2019) Founding member & Associate Research Director, Stanford Literary Lab Junior Research Fellow, King’s College Cambridge (2019-2022) Research interests span computational modeling of semantic revolutions, large-scale literary field analysis, and the intersection of digital methods with historical and intellectual studies. His book Explorations in the Digital History of Ideas (2023) co-edited with Peter de Bolla exemplifies this approach. Recent publications focus on historical semantics, metrical analysis, and digital mapping of emotions in literature, reflecting his interdisciplinary expertise in natural language processing, network theory, and literary data visualization. Currently leads teaching initiatives at Cambridge Digital Humanities and contributes to computational projects exploring textual rhythms and large language models.

Sasha Wellandمشاهده پروفایل

دانشیار مدعو

• Asian American
• East Asian Ethnography
• Feminism and Feminist Theory
• +۲ مورد دیگر

Sasha Welland is an Adjunct Associate Professor in the Department of Anthropology at the University of Washington, affiliated with Gender, Women & Sexuality Studies; China Studies; Comparative History of Ideas; and graduate certificate programs in Cinema & Media Studies and Public Scholarship. Her research focuses on Asian American studies, East Asian ethnography, feminism, visual culture, and sociocultural anthropology. Welland’s scholarship bridges historical and contemporary analyses. Her book Experimental Beijing (2018) explores Chinese contemporary art as a site of cultural negotiation around gender, globalization, and post-socialist transformations. Her earlier work A Thousand Miles of Dreams (2006) traces the transnational lives of two 20 th -century Chinese women activists. She contributes to Journal of Visual Culture and has published in Signs and Yishu . An engaged public scholar, Welland has curated feminist art exhibitions in Beijing, Shanghai, and Hong Kong, blending academic research with cultural practice. Her work interrogates intersections of gender, space, and global flows in East Asian contexts.

Sunita Sarawagiمشاهده پروفایل

استاد

• Machine Learning
• Data Analytics
• Databases
• +۱۰ مورد دیگر

Sunita Sarawagi is a Professor at Computer Science and Engineering , IIT Bombay, and a member of AI Labs@CSE . She is also associated with the Center for Machine Intelligence and Data Science (CMInDS), which she founded in 2020. Education: PhD in Computer Science from UC Berkeley (Thesis: Query Processing in Tertiary Memory Databases), BTech in Computer Science from IIT Kharagpur Research Interests span machine learning , data analytics , graphical models , and structured learning , with applications in text segmentation, sequence modeling, domain adaptation, and human-in-the-loop systems. Her publications reveal a strong focus on integrating data mining with database systems , temporal data analysis , and information extraction using probabilistic methods. Professional Activities include serving on the IEEE John Von Neumann Medal committee (2017-), VLDB 2011 Research Track Co-chair , and multiple program committee roles at top conferences like ICML, KDD, and SIGMOD. Labs & Teams : Leads the SS Lab , a research group focused on probabilistic graphical models, sequence modeling, and data integration techniques.

Luisa Calèمشاهده پروفایل

استاد

• Romantic Literature
• Visual Culture
• Material Cultures
• +۵ مورد دیگر

Dr. Luisa Calè is a Professor of Romantic and Nineteenth-Century Literature & Visual Culture and Research Lead at Birkbeck, University of London. She holds degrees from the University of Rome 'La Sapienza' (1993), University of Genoa (1997), and the University of Oxford (M.St. 1998, D.Phil 2002). Her research explores the interplay between literature and visual/material culture, focusing on book history, intermediality, and practices of reading/collecting. Her Leverhulme Trust-funded project 'The Book Unbound' examines how works by Blake, Walpole, and Dickens subverted traditional book formats. Education: PhD in Literary Studies, University of Genoa (1997) D.Phil., University of Oxford (2002) M.St. in English, University of Oxford (1998) Letters Degree, University of Rome (1993) Her research interests include Romantic aesthetics, material texts, and critical disciplinarity. Notable publications include Fuseli’s Milton Gallery (2006) and co-edited volumes like Dante on View (2007). She edits Blake/An Illustrated Quarterly and serves on editorial boards for Word & Image and Romantic Circles Gallery . Recent awards include a Leverhulme Trust Research Fellowship (2019) and a Mercator Fellowship (2018-19). She supervises graduate students in Romantic literature, material culture, and book history.

Kaiming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Machine Learning
• +۶ مورد دیگر

Kaiming He is an Associate Professor with tenure in the Department of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT), holding the Douglas Ross (1954) Career Development Professor of Software Technology chair. He also works part-time as a Distinguished Scientist at Google DeepMind. Prior to joining MIT in 2024, he was a research scientist at Facebook AI Research (FAIR) from 2016 to 2024, and a researcher at Microsoft Research Asia (MSRA) from 2011 to 2016. Dr. He received his PhD from the Chinese University of Hong Kong in 2011 and his Bachelor of Science from Tsinghua University in 2007. His academic journey reflects a strong foundation in computer science and engineering that has led to transformative contributions in artificial intelligence. His research primarily focuses on computer vision and deep learning, with pioneering work on deep residual networks (ResNets), visual object detection and segmentation, and self-supervised learning. He is best known for his work on Deep Residual Networks (ResNets), recognized as the most-cited paper of the twenty-first century. The residual connections he pioneered are now fundamental components in modern deep learning architectures including Transformers, AlphaGo Zero, AlphaFold, and various generative AI models. His recent publications demonstrate continued innovation across generative models, transformer architectures, and cross-disciplinary AI applications. His work bridges theoretical advances in neural network design with practical implementations that address real-world challenges in physics, biology, and other scientific domains. PAMI Young Researcher Award (2018) Best Paper Award, CVPR (2009, 2016) Best Paper Award, ICCV (2017) Best Student Paper Award, ICCV (2017) Everingham Prize, ICCV (2021) Most-cited paper of the twenty-first century Dr. He advises graduate students including Jake Austin, Xingjian Bai, and Mingyang Deng, and teaches advanced courses such as "6.S978: Deep Generative Models" (Fall 2024) and "6.8300/6.8301: Advances in Computer Vision" (Spring 2024). His research group actively explores how AI can serve as a unifying framework across scientific disciplines, breaking down traditional barriers between fields through shared methodologies and tools.

Wei Xuمشاهده پروفایل

دانشیار

• Large Language Models
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Wei Xu is an Associate Professor at Georgia Institute of Technology's College of Computing and School of Interactive Computing, with affiliations to the Machine Learning Center. Their research bridges machine learning, natural language processing, and social media with focus areas in large language models, cultural bias mitigation, multilingual capabilities, and human-AI collaboration in text evaluation. NSF CAREER and Google Academic Research Award recipient Director of NLP X Lab PhD from New York University, BSMS from Tsinghua University Research interests span: Multilingual Multicultural LLMs addressing representational gaps and cultural adaptation in language models (NAACL 2025, ACL 2024); Robustness and Reasoning through dynamic AGI evaluations (ACL 2024, EMNLP 2024); Interdisciplinary NLP applications in security, healthcare, and law (EMNLP 2024, ACL 2024). Recent publications focus on multilingual alignment (NAACL 2025), privacy risk estimation (arXiv 2025), cultural bias analysis (ACL 2024), and medical text simplification (EMNLP 2024). Key themes include bias mitigation, multimodal processing, and practical LLM evaluation. Scientific Awards : NSF CAREER, Google/Sony/Criteo research awards, ACL'24 Best Social Impact Award, COLING'18 Best Paper Advising 15 PhD/MS/BSMS students including Yao Dou (human-centered LLM evaluation), Tarek Naous (multilingual LLMs), and alumni like Chao Jiang (Apple AI/ML) and Yang Chen (NVIDIA research scientist). Teaches graduate courses on NLP and LLMs.

Andrea Vedaldiمشاهده پروفایل

استاد

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

Andrea Vedaldi is a Professor of Computer Vision and Machine Learning at the University of Oxford's Department of Engineering Science, affiliated with the Visual Geometry Group (VGG). He specializes in unsupervised methods for understanding images and videos, focusing on 3D geometry and semantics. His research bridges foundational AI and practical applications, with contributions to generative models, neural fields, and self-supervised learning. Education: PhD in Computer Science (2008), University of California, Los Angeles MSc in Computer Science (2005), UCLA BSc in Information Engineering (2003), University of Padua Research Interests: Unsupervised learning, 3D perception, generative AI, neural rendering, and scalable vision systems. His work emphasizes ethical, responsible AI aligned with ERC-funded projects like UNION (ERC Consolidator Grant). Key Contributions: Co-developer of VLFeat and MatConvNet libraries Leader in 3D reconstruction and diffusion models (e.g., CatFree3D) Recipient of the PAMI Thomas S. Huang Prize and multiple best paper awards Grants & Service: Principal Investigator on £2.3M ERC Consolidator Grant (UNION) Co-organizer of major conferences (ECCV 2020 Program Chair, CVPR 2023 Area Chair) Reviewer for top journals/conferences (PAMI, CVPR, NeurIPS) Labs & Teams: VGG Group at Oxford, collaborating on projects like Meta 3D Gen and Common Objects in 3D (CO3D).

• Machine Learning
• Computer Vision
• Data Mining
• +۶ مورد دیگر

Dr. Chang Xu is an Associate Professor in Machine Learning and Computer Vision at the University of Sydney's School of Computer Science. He holds a Bachelor of Engineering from Tianjin University and a PhD from Peking University. His research focuses on machine learning, data mining, and their applications in AI and computer vision, including multi-view learning, visual search, and face recognition. He is an ARC Future Fellow and a member of the Sydney Southeast Asia Centre and The Net Zero Institute. Education: B.E. in Engineering (Tianjin University), Ph.D. in Computer Science (Peking University). His research interests emphasize handling heterogeneous data, exploring data variety, and developing algorithms for robust AI systems. His work includes adversarial robustness, neural architecture search, and efficient deep learning models. Research trends in his articles include adversarial robustness in neural architectures, efficient vision transformers, multimodal 3D style transfer, and underwater image restoration. Key contributions span image restoration, video super-resolution, and lightweight network design. He has advised multiple PhD and master's students on topics like diffusion models, radar image synthesis, and graph similarity. Awards: ARC Future Fellow. Collaborations focus on cross-domain data integration and AI applications. His labs and teams explore generative models, robust learning, and scalable robotics policies. Recent work includes diffusion models for action segmentation and robust vision-language systems.

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 .

Antoine Bosselutمشاهده پروفایل

استادیار

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Antoine Bosselut is a Tenure Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the EPFL NLP group. His research focuses on developing AI reasoning agents that can model, represent, and reason about human and world knowledge, with applications in health, education, and global fairness. His research interests span multiple critical areas in modern AI: LLM Representations of Knowledge: Understanding what language models know and how they represent that knowledge internally Reasoning Algorithms: Developing methods to improve LLMs' reasoning capabilities through symbolic systems, neuroscience, and cognitive science Large-scale AI Development: Creating open-source foundation models with multilingual capabilities AI Democratization: Ensuring equitable access to AI technologies across different cultural and regulatory contexts His recent publications reveal a strong focus on the intersection of language models and cognitive science, with multiple studies examining how LLMs align with human cognition. He's also deeply engaged in practical applications of NLP technology, particularly in education and multilingual settings, as evidenced by his work on evaluating AI's impact on higher education and developing culturally-aware language models. His scientific achievements have been recognized with several prestigious awards: Outstanding Paper Award at NAACL 2025 ELLIS Scholar designation in 2024 Outstanding Paper Award at ACL 2023 Inclusion in Forbes 30 Under 30 list for Science & Healthcare in 2021 Bosselut actively mentors numerous PhD students across diverse NLP research areas and has secured significant recognition for his work in both academic and media circles. His lab has been featured in major publications including Communications of the ACM, The Atlantic, and Quanta Magazine, highlighting the societal impact of his research on commonsense reasoning and AI capabilities.

Deva Kannan Ramananمشاهده پروفایل

استاد

• Computer Vision
• 3-D Vision and Recognition
• Visual Servoing and Tracking
• +۸ مورد دیگر

Deva Kannan Ramanan is a Professor at the Robotics Institute of Carnegie Mellon University , focusing on computer vision , machine learning , and human-centered robotics . His work bridges neurorobotics and visual perception , with applications in autonomous driving and 4D reconstruction . Research Topics Computer Vision 3-D Vision and Recognition Visual Servoing Neurorobotics Human-Centered Robotics Graphics & Creative Tools His recent publications in CVPR , ICRA , and ICCV emphasize 4D human reconstruction , neural rendering , and vision-language models for autonomous systems. He serves as General Chair of CVPR 2027 and Program Chair of CVPR 2018 , with IARPA funding for aerial-ground rendering (2023-2027). Current students include PhD candidates Sally Chen, Kangle Deng, and Zhiqiu Lin, while past advisees like Arun Vasudevan and Olga Russakovsky now hold positions at Amazon and Meta respectively.

• Computer Vision
• Machine Learning
• Visual Reasoning
• +۷ مورد دیگر

Justin Johnson is an Assistant Professor at the University of Michigan's College of Engineering, Department of Electrical Engineering and Computer Science, and a Research Scientist at Facebook AI Research (FAIR). His work bridges computer vision, machine learning, and deep learning, focusing on visual reasoning, vision-language tasks, image generation, and 3D reasoning using neural networks. PhD, Stanford University (advised by Fei-Fei Li) His research interests span visual reasoning , vision and language , 3D vision , and image generation , with a focus on innovative applications of deep neural networks. Recent publications highlight work on 3D consistency, self-supervised learning, and multimodal integration of vision and text. Notable contributions include PyTorch3D for 3D data processing, and foundational work in visual question answering , neural style transfer , and scene graph-based image generation . Publications span top conferences like ICCV, CVPR, and NeurIPS. He teaches courses including EECS 498/598: Deep Learning for Computer Vision and EECS 442: Computer Vision at University of Michigan, with prior involvement in Stanford's CS 231N in co-teaching roles. Software projects include open-source frameworks like fast-neural-style for real-time artistic style transfer, and PyTorch3D for efficient 3D deep learning. These tools demonstrate his commitment to practical implementations and community-driven research.