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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.

• Art History
• Fakes and Forgeries
• Art and Law
• +4 more

Nicholas Evans is a Distinguished Professor in the Department of Linguistics at the School of Culture, History & Language, Australian National University. He is an ARC Laureate Fellow and serves as Director of the ARC Centre of Excellence for the Dynamics of Language (CoEDL), reflecting his leadership in integrating typology, descriptive linguistics, and cognitive science. His work spans linguistic diversity, endangered languages, and the interplay between language and culture. His research focuses on Australian and Papuan languages, with extensive fieldwork in remote communities. Key interests include linguistic typology, historical and contact linguistics, semantics, and the cognitive underpinnings of grammar. He has published foundational grammars and dictionaries of Kayardild, Bininj Gun-wok, and Dalabon, contributing significantly to the documentation of endangered languages. His recent publications reveal a strong trend in cross-linguistic studies of social cognition (SCOPIC), syntactic typology, and phylogenetic modeling of the Yam languages. These works integrate linguistic, cognitive, and genetic data, highlighting interdisciplinary approaches to understanding language evolution and human diversity. ARC Laureate Fellow Fellow of the Australian Academy of the Humanities (FAHA) Fellow of the Academy of the Social Sciences in Australia (FASSA) Fellow of the British Academy (FBA) Evans supervises research students and leads major projects such as 'The Wellsprings of Linguistic Diversity' and CoEDL, which involve extensive collaboration across institutions and disciplines. His grants include modeling Pacific creoles and phonemic analysis of Tok Pisin. He is also involved in digital archiving initiatives like PARADISEC and the Language Data Commons of Australia, ensuring long-term preservation of linguistic data. He leads and contributes to research teams focused on Southern New Guinea languages, paradigm syncretisms (PARABANK), and cultural heritage preservation. His work emphasizes reciprocal engagement with speech communities and training the next generation of language documenters.

• Linguistic Typology
• Historical Linguistics
• Contact Linguistics
• +12 more

Mari Ostendorf serves as an Adjunct Professor in the Department of Computer Science and Engineering within the College of Engineering at the University of Washington, where she conducts research at the intersection of speech technology and computational linguistics with emphasis on spontaneous speech phenomena. Her educational foundation includes: PhD in an unspecified field, Stanford University (1985) Her research program systematically investigates: Speech Processing: Specializing in spontaneous speech characteristics and prosodic modeling Natural Language Processing: Focusing on conversational speech parsing and stance analysis Computational Linguistics: Integrating linguistic theory with machine learning approaches Prosody: Analyzing idiosyncratic speech patterns for language processing applications Stance Recognition: Developing computational methods for identifying speaker attitude and commitment Her work manifests in significant projects including the LiCORICE Project (2009-2011) as Principal Investigator and the ATAROS Project as Co-Principal Investigator, both advancing automatic spoken language processing through detailed modeling of speech prosody and stance phenomena. Analysis of her publication record reveals a consistent trajectory toward interdisciplinary integration of linguistic theory and machine learning for spoken language understanding, with particular emphasis on non-lexical cues and discourse markers in conversational contexts that bridge computational and cognitive approaches to language processing. No scientific awards were mentioned in the provided documentation. Her advising record includes PhD candidates Jeremy Kahn (2010 dissertation on conversational speech parsing) and Esther Le Grézause (2017 dissertation on stance expression). Her grant portfolio features the LiCORICE Project (2009-2011, PI with Emily M. Bender and Mark Zachry) and the ATAROS Project (Co-PI with Richard Wright and Gina Levow), demonstrating sustained research funding in speech technology. Her research operates within an interdisciplinary framework connecting computer science and linguistics, evidenced by collaborations spanning departments and contributions to dissertation defenses in the Linguistics program at the University of Washington.

• Speech Processing
• Natural Language Processing
• Computational Linguistics
• +3 more

Matthew Louis Mauriello is an Assistant Professor in the Department of Computer and Information Sciences at the University of Delaware , where he directs the Sensify Lab . He holds a PhD in Computer Science from the University of Maryland (2018) and completed postdoctoral work at Stanford University (School of Medicine, 2020; School of Public Policy & Environmental Engineering Department, 2019). Research interests span Human-Computer Interaction (HCI), Ubiquitous Computing, and User-Centered Design with applications in: Sustainability Human-Building Interaction Wearable Technology Personal Informatics Educational Game Design Mental Health Interventions Recent publications highlight trends in stress monitoring (skin-like biosensors, workplace wellbeing), energy auditing (thermography systems), and educational technology (block-based programming tools for teachers). His work appears in ACM CHI, ACM Human-Computer Interaction, and Building and Environment. Scientific awards include: Best Paper Honorable Mention, CHI 2016 Best Paper Honorable Mention, CHI 2015 Best of WebSci'18 Teaching at University of Delaware encompasses courses like Educational Game Design Operating Systems Advanced Web Technologies Computing for Social Good with a focus on project-based learning and systems thinking.

• Human-Computer Interaction
• Ubiquitous Computing
• Sustainability
• +7 more

Gierad Laput is a Senior Engineering Manager at Apple, leading the Perception and Interaction Engineering organization under Machine Intelligence Neural Design (MIND) at Apple AI/ML (AIML). He is also an Adjunct Professor at Carnegie Mellon University's School of Computer Science and Human-Computer Interaction Institute. His research sits at the intersection of sensing systems, interactive technologies, and machine learning, with applications in ambient intelligence, wearables, and AI-driven interactive experiences. Ph.D. in Computer Science from Carnegie Mellon University Founding member of CMU's Future Interfaces Group Former Google Ph.D. Fellow, Disney Imagineer, and recipient of 10 Best Paper Awards/Nominations His research agenda at Apple leverages cutting-edge hardware to develop multimodal ML systems for vision, audio, and motion sensors, enabling features like head gesture detection for AirPods, Apple Intelligence integrations, and health monitoring tools. Recent collaborative projects include Vision Pro hand interaction systems and WatchOS updates for cycling/fall detection. Key research areas include human-computer interaction , machine learning for sensor fusion , and smart environment sensing . Over 35 publications cover transformative technologies like ViBand (bio-acoustic sensing), EM-Sense (touch recognition), SkinTrack (arm as touchpad), and ElectroDermis (stretchable electronics). Scientific honors include: Fast Company Innovation by Design Award (2017) Google PhD Fellowship (2018) Swartz Entrepreneurial Fellowship (2018) Best Paper Awards at ACM UIST (2016) and ACM CHI (2015) As an educator, he has taught courses at CMU and the University of Michigan on on-device machine learning , signal processing , and human-centered design . His lab has mentored numerous researchers now pursuing PhDs at MIT, UW, CMU, and Northwestern.

• Human-Computer Interaction
• Machine Learning
• Sensing Systems
• +7 more

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, affiliated with multiple research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Berkeley Center for Responsible, Decentralized Intelligence (RDI). She holds leadership roles in AI safety, cybersecurity, and blockchain research. Her work bridges machine learning, security, and distributed systems, with notable contributions to formal verification, privacy-preserving technologies, and AI governance. Education: Ph.D. (2002) and M.S. (1999) in Computer Science from UC Berkeley and Carnegie Mellon University, respectively. She previously served as an Assistant Professor at Carnegie Mellon University before joining UC Berkeley in 2007. Research Interests: Dawn's work focuses on AI safety, cybersecurity, and the ethical implications of frontier AI. She explores topics such as large language model vulnerabilities, zero-knowledge proofs, blockchain security, and policy frameworks for AI governance. Her research combines theoretical rigor with practical applications, addressing challenges in secure systems, adversarial machine learning, and privacy-preserving computation. Publications: Her recent work includes groundbreaking studies on LLM memorization, smart contract decompilation, and AI agent cybersecurity evaluation. She also leads initiatives like the Singapore Consensus and California Report on AI safety priorities, emphasizing global collaboration in risk mitigation. Notable Awards: MacArthur Fellowship (2010), ACM Fellow (2019), IEEE Fellow (2019) Advising & Grants: She advises projects on AI policy and cybersecurity, securing grants from NSF, DARPA, and industry collaborations. Her lab develops tools like CyberGym for evaluating AI agents and zkPyTorch for secure machine learning. Labs & Teams: Her research groups at UC Berkeley focus on cutting-edge projects in AI safety, blockchain, and cybersecurity, collaborating with industry leaders and policymakers to advance both technical and ethical standards.

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +7 more

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.

• Computer Vision
• Deep Learning
• Machine Learning
• +6 more

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 .

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +5 more

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.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +5 more

Tianqi Chen is an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University (CMU), with a courtesy appointment as a Professor in the Electrical and Computer Engineering Department within the College of Engineering. His research focuses on scalable machine learning systems, compiler optimization, and efficient deep learning frameworks. He holds a PhD from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Key contributions include the creation of XGBoost, Apache TVM, and MLC-LLM—widely adopted systems for machine learning and large language models. His work bridges algorithmic innovation with high-performance computing, emphasizing efficient deployment, quantization, and edge computing. Recent publications highlight advancements in LLM serving (e.g., WebLLM, Flashinfer), compiler-driven optimizations (e.g., TVM, Relax), and low-latency inference techniques (e.g., Magicdec, Tilus). These efforts address scalability, energy efficiency, and cross-platform compatibility in modern AI systems. Chen’s research has been applied to diverse domains, including music AI, browser-based inference, and microservice architectures for LLMs. His work underscores the importance of system-level thinking in advancing AI capabilities.

• Machine Learning
• Deep Learning Systems
• Compiler Optimization
• +6 more

Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays

• Computer Science
• Artificial Intelligence
• Computer Vision
• +5 more

Prof. Konrad Schindler holds the position of Full Professor at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. He is also the Head of the Institute of Geodesy and Photogrammetry (IGP), leading research and educational activities in geomatics and computer vision. His career spans roles as a Photogrammetric Engineer, scientific assistant, postdoc researcher, and academic faculty across institutions including Graz University of Technology, Monash University, and TU Darmstadt before joining ETH Zürich in 2010. Education: Undergraduate studies in Geodesy (1992–1995), Graz University of Technology, Austria MEng in Photogrammetry and Geoinformation (1995–1999), Vienna University of Technology, Austria PhD in Computer Science (2001–2003), Graz University of Technology, Austria Research focuses on Photogrammetry , Remote Sensing , Computer Vision , and Image Understanding with interdisciplinary applications in environmental monitoring, geospatial analysis, and disaster response. He develops computational methods for 3D reconstruction, fusion of multi-modal data, and AI-driven solutions for satellite imagery interpretation. His work bridges geomatic engineering and machine learning to address challenges in urban mapping, climate modeling, and biological systems analysis. Publications reflect expertise in geospatial AI, diffusion models, and benchmarking datasets for disaster resilience. Notable works include Marigold (image analysis adaptation) and BRIGHT (building damage assessment). His research emphasizes practicality and scalability, such as affordable depth estimation and global biomass datasets. He has received the 2013 Marr Prize Honourable Mention (IEEE) and the 2012 U.V. Helava Award (ISPRS), alongside several Best Presentation Awards. His contributions span technical leadership, editorial roles (ISPRS Journal), and service to Swiss remote sensing commissions. Advising and grants: While no specific advisee names or grant details are listed, his career trajectory includes mentoring postdocs and junior faculty. He teaches advanced courses in Photogrammetry , Image Interpretation , and Machine Vision , integrating cutting-edge AI techniques into curricula. His research group collaborates on global-scale projects like canopy height mapping and satellite-based climate variable assessments. Labs/Teams: As Institute Head, he oversees the IGP lab at ETH Zürich, with prior affiliations including the Digital Perception Lab (Monash University) and the Computer Vision Lab (ETH Zurich). His work often involves multi-institutional collaborations focused on geospatial AI and environmental science.

• Photogrammetry
• Remote Sensing
• Computer Vision
• +3 more

Shrikanth (Shri) Narayanan is a University Professor and holder of the Niki and Max Nikias Chair in Engineering at the University of Southern California (USC), serving as the inaugural Vice President for Presidential Initiatives. He leads the Signal Analysis and Interpretation Lab (SAIL) and holds joint appointments in Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head and Neck Surgery. His research focuses on speech and audio processing, behavioral signal processing, and real-time MRI of speech production, with applications in healthcare, education, and technology. Education: B.E. in Electrical Engineering from College of Engineering, Guindy (Chennai, India, 1988); M.S., Engineer, and Ph.D. in Electrical Engineering from UCLA (1990, 1992, 1995). Research interests span computational linguistics, machine learning, and multimodal human behavior analysis. He pioneered technologies for speech biomarkers in mental health, real-time MRI of speech production, and wearable sensor systems for longitudinal health studies. His work in speech emotion recognition, forensic interviews, and clinical applications has been recognized through over 40 awards, including the IEEE Flanagan Award and ISCA Medal. He has published extensively in journals like Proceedings of the IEEE , Journal of the Acoustical Society of America , and PLOS One . Key Grants: NSF CAREER, Okawa Research, IBM Faculty, Google/Amazon awards. Labs/Teams: Signal Analysis & Interpretation Lab (SAIL), USC Information Sciences Institute (ISI), Google Visiting Faculty Researcher.

• Speech and Audio Processing
• Behavioral Signal Processing
• Real-Time MRI of Speech Production
• +4 more

Dr. Diyi Yang is an Assistant Professor in the Computer Science Department at Stanford University. She leads the Social and Language Technologies (SALT) Lab, affiliated with the Stanford NLP Group, Stanford HCI Group, Stanford AI Lab (SAIL), and Stanford Human-Centered Artificial Intelligence (HAI). Her research focuses on socially aware natural language processing, large language models (LLMs), and human-AI interaction, aiming to improve human-human and human-computer communication through socially grounded AI systems. Education: Ph.D. in Language Technologies Institute, Carnegie Mellon University (2013–2019) B.S. in ACM Honored Class, Shanghai Jiao Tong University (2009–2013) Research Interests: Dr. Yang’s work bridges computational social science and NLP. She explores how AI can understand social contexts in language use and develop systems that respect cultural norms, ethical standards, and human values. Her lab’s projects include AI companions for skill training (e.g., Rehearsal Dialects), norm-aware LLMs (NormBank), and frameworks for human-AI collaboration (Co-Gym). Recent efforts address bias in AI, societal impacts of LLMs, and ethical evaluation of human-AI systems. Awards & Honors: 2024: Sloan Research Fellowship, ONR Young Investigator Award 2023: Adamic-Glance Young Distinguished Award (ICWSM), Kavli Fellow (NAS) 2022: NSF CAREER Award, Microsoft Research Faculty Fellow 2020: IEEE AI’s 10 to Watch Advising & Grants: Dr. Yang advises over 10 PhD students and postdocs, co-leading projects on LLM evaluation (SWE-bench/SWE-smith), human-AI ethics, and culturally aware NLP. Her research is supported by NSF, Amazon, DARPA, Google, and Stanford’s HAI initiative. Labs & Teams: The SALT Lab collaborates across disciplines, with projects spanning computer science, linguistics, and social sciences. Current initiatives include developing AI tools for mental health support (AI Partner & Mentor) and auditing societal impacts of LLMs.

• Computational Social Science
• Natural Language Processing
• Socially Aware NLP
• +4 more

Jerrica Ty Rowlett serves as an Assistant Professor of Communication and Language Studies within the College of Arts and Sciences at Bryant University, where she examines digital communication's societal implications through research and teaching in digital culture and social media dynamics. Education Ph.D., Florida State University M.A., Clemson University B.A., Georgetown College Her research program investigates identity formation, civic engagement, and digital inequity through critical analysis of social media platforms. She explores how digital environments shape interpersonal relationships, political discourse, and community building while addressing power dynamics in online spaces, with particular attention to abusive contexts, gender representation, and emotional experiences. Analysis of her publication record (2015-2024) reveals a consistent trajectory examining social media's evolving societal impact. Early work focused on educational applications and youth culture, progressing to sophisticated analyses of political meme culture, relationship dissolution in abusive contexts, and emotional contagion online. Her scholarship demonstrates increasing attention to social justice dimensions within digital communication. Scientific Awards No awards documented in current sources Advising and Grants Student advising details and research grant information are not specified in available materials, though her extensive publication record indicates active scholarly mentorship and research productivity. Labs and Teams No dedicated research laboratories or formal collaborative teams are mentioned in the provided information.

• Social Media
• Identity
• Civic Engagement
• +1 more