جست‌وجوی استاد و پژوهشگر

Nicholas Evansمشاهده پروفایل

استاد

• Linguistic Typology
• Historical Linguistics
• Contact Linguistics
• +۱۲ مورد دیگر

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.

Craig J. Fennieمشاهده پروفایل

استاد

• Computational Materials Physics
• Condensed Matter Physics
• Solid State Chemistry
• +۴ مورد دیگر

Craig J. Fennie is Professor in the College of Engineering at Cornell University , where he has been on the faculty since July 2008. His appointment spans the interdisciplinary space between physics, materials science, and solid-state chemistry. Education: Ph.D. in Physics, Rutgers University (2006) M.S. in Electrical Engineering, Villanova University (1996) Research Focus: Fennie’s group pursues computational and theoretical materials physics , with a long-term goal of realizing a first-principles “materials-by-design” paradigm. By integrating microscopic Hamiltonians, symmetry analysis, and density-functional theory, the team explores how crystalline motifs—composition, symmetry, geometry, and topology—govern emergent phenomena such as multiferroicity, magnetoelectric coupling, and tunable dielectric response. Particular attention is paid to complex oxides, layered heterostructures, and materials under extreme conditions where chemical intuition can fail. Publication Trends: From 2011 to 2016, Fennie co-authored a series of high-impact papers in Nature family journals and Physical Review Letters . These works collectively advance the understanding and design of room-temperature magnetoelectric multiferroics, bulk magnetoelectric effects in hexagonal manganites, tunable microwave dielectrics through defect mitigation, and the fundamental scarcity of ferroelectric perovskites. The studies span synthesis, characterization, and theory, underscoring a collaborative, interdisciplinary approach. Awards & Honors: MacArthur Fellowship, John D. and Catherine T. MacArthur Foundation (2013) Presidential Early Career Award for Scientists and Engineers (PECASE, 2012) NSF CAREER Award (2011) Army Research Office Young Investigator Award (2010) Fellow of the American Physical Society (2015) Advising & Funding: While specific student names are not listed, Fennie leads a vibrant research program supported by NSF, ARO, and other agencies. His group trains graduate and post-doctoral researchers at the intersection of physics, materials science, and chemistry, preparing them for careers in both academia and industry. Labs & Teams: Work is conducted within Cornell’s shared high-performance computing ecosystem and leverages national user facilities such as the Cornell High Energy Synchrotron Source (CHESS) and collaborations with experimental groups worldwide.

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.

Yarin Galمشاهده پروفایل

دانشیار

• Bayesian Deep Learning
• Machine Learning
• AI Safety
• +۴ مورد دیگر

Yarin Gal is an Associate Professor of Machine Learning at the University of Oxford's Department of Computer Science and a Tutorial Fellow at Christ Church College. He is also a Turing AI Fellow at the Alan Turing Institute and Director of Research at the UK Government’s AI Safety Institute. He leads the Oxford Applied and Theoretical Machine Learning (OATML) Research Group, focusing on Bayesian deep learning, AI safety, and uncertainty quantification. Education PhD in Uncertainty in Deep Learning (2016) Research Interests His research integrates Bayesian methods with deep learning to address challenges in AI safety , uncertainty quantification , and robustness . Key areas include: Bayesian neural networks and approximate inference Uncertainty estimation in deep learning AI safety and interpretability Applications in autonomous driving, medical imaging, and NLP Publications & Trends His recent work spans Bayesian optimization , adversarial robustness , and continual learning . Notable contributions include Targeted Dropout for model pruning, Uncertainty in Autonomous Driving , and theoretical studies on adversarial examples in Bayesian networks. Awards & Honors Turing AI Fellow Teaching & Supervision He has taught Advanced Machine Learning , Uncertainty in Deep Learning , and contributed to NASA's Frontier Development Lab. His current students include Kelsey Doerksen, Gunshi Gupta, and Shreshth Malik. Labs & Teams He leads the OATML Group , a multidisciplinary team advancing theoretical and applied machine learning, with a focus on safety and interpretability in AI systems.

• 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 .

Marlyse Baptistaمشاهده پروفایل

استاد

• Language Contact
• Cognitive Linguistics
• Pidgins and Creoles
• +۴ مورد دیگر

Marlyse Baptista is the President's Distinguished Professor of Linguistics at the University of Pennsylvania, Department of Linguistics. She is affiliated with the School of Arts & Sciences and MindCore initiative. Her research focuses on language contact, creolization processes, bilingualism, and experimental methods in creole studies. Baptista leads the Language Contact and Cognition Lab, previously known as the Cognition, Convergence and Language Emergence (CCLE) group at the University of Michigan. Education includes a PhD in Linguistics (Harvard, 1997), MA degrees from Harvard and the Université de Bordeaux III, and extensive training in multilingual education. Her work bridges generative syntax with experimental approaches, including artificial language learning to study language convergence. Key themes include the cognitive underpinnings of creole formation, bidirectional influences in Cape Verdean Creole, and the role of congruence in language acquisition. Research highlights include studies on Cape Verdean Creole's grammatical properties, genetic-linguistic admixture correlations, and pedagogical resources for creole language education. Recent projects explore experimental validation of creole genesis theories through multilingual acquisition studies. Scientific Awards: President's Distinguished Professorship (University of Pennsylvania) Labs/Teams: Language Contact and Cognition Lab, MindCore Grants/Projects: MULTI Project (creole language educational resources), NSF-funded studies on creole genesis mechanisms Baptista's work emphasizes interdisciplinary approaches, integrating syntax theory, psycholinguistics, and sociolinguistics to address foundational questions in contact linguistics and creolistics.

• Robotics
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Prof. Ingmar Posner is a leading figure in applied artificial intelligence at the University of Oxford, where he serves as Principal Investigator for the Applied Artificial Intelligence Lab (A2I) and founding Director of the Oxford Robotics Institute. His work focuses on enabling robots to operate effectively in complex real-world environments through experience-driven learning. Key research areas: robot learning, scene interpretation, data-efficient learning, and transfer learning Applications in manipulation, autonomous driving, logistics, and space exploration His team has produced groundbreaking work in world models, sim-to-real transfer, and constraint-based manipulation systems (e.g., COMBO-Grasp). Notable contributions include the TWIST distillation framework and foundational research in tactile data generation (TactGen). He has received multiple best paper awards at top robotics venues. Publications reveal evolving research themes: 2025 work emphasizes language-conditioned learning (Lumos) and multi-agent decision-making, while 2024 focused on diffusion models for locomotion and differentiable simulators. Earlier work spans from urban scene analysis to physically plausible scene synthesis (RELATE).

Konrad Schindlerمشاهده پروفایل

استاد

• Photogrammetry
• Remote Sensing
• Computer Vision
• +۳ مورد دیگر

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.

• Medical Physics
• Diagnostic Imaging Physics
• Radiation Dose Management
• +۶ مورد دیگر

Adel Mustafa is Professor in the Department of Radiology and Biomedical Imaging at Yale School of Medicine. He serves as Director of the Yale Diagnostic Medical Physics Residency Program and Chief of Diagnostic Radiology Physics at Yale New Haven Health System. His work integrates clinical service, education, and research in medical physics. His primary research interests are in image quality optimization , radiation dose management , and the quantification of disease conditions using multiple imaging modalities, with a particular focus on CT imaging. He leads a multidisciplinary team of medical physicists and collaborates with radiologists and clinicians to advance detection, quantification, and optimization in CT. Dr. Mustafa is board certified by the American Board of Radiology (ABR) and the American Board of Medical Physics (ABMP) in diagnostic imaging physics. He is an elected Fellow of the American Association of Physicists in Medicine (AAPM), recognizing his contributions to the field. Fellow of the American Association of Physicists in Medicine (2007) He plays a significant national and international leadership role in medical physics, having served on numerous AAPM committees and as an examiner for the ABR since 2004. Currently, he is Chairman of the Accreditation Committee and Chief Examiner for the International Medical Physics Certification Board (IMPCB), shaping standards and certification globally. He established the Yale Diagnostic Medical Physics Residency under GMEC and continues to lead it as Program Director. Dr. Mustafa's team focuses on advancing clinical imaging through physics-driven innovation, with an emphasis on safety, accuracy, and quantitative analysis in diagnostic radiology.

Hutan Ashrafianمشاهده پروفایل

استاد

• Artificial Intelligence in Medicine
• Metabolic Surgery
• Health Policy
• +۴ مورد دیگر

Professor Hutan Ashrafian is a clinician-scientist and surgeon at the University of Leeds Business School, holding dual roles as Professor of Research Impact and Senior Research Fellow at Imperial College London. His expertise spans AI in healthcare, metabolic surgery, and ancient history. He pioneered STARD-AI and QUADAS-AI guidelines for AI diagnostics, collaborated on AI-driven breast cancer screening with Google and NHS, and led global health policy initiatives. Ashrafian’s work bridges medicine, history, and philosophy, including his contributions to understanding historical figures’ medical conditions, such as King Tutankhamun’s epilepsy. He has authored over 550 publications, 12 books, and holds a PhD in computational physiology and an MBA with distinction. Awards include the Hunterian Prize and Wellcome Trust Fellowship. His research also explores AI ethics, quantum physics paradoxes, and art-based medical diagnostics. Education: PhD in Computational Physiology (Imperial College London) MBA (Warwick Business School) MD (University College London) Bachelor of Science in Immunology (University College London) Research Interests: Ashrafian’s work spans: AI in Medicine: Diagnostic algorithms, guideline development (STARD-AI/QUADAS-AI), and ethical frameworks. Metabolic Surgery: Bariatric interventions, gut microbiome, and obesity treatments. Ancient History: Medical analyses of historical figures (e.g., Tutankhamun, Julius Caesar) and art-based pathology identification. Philosophy: AI rights (AIONAI law), temporal paradoxes, and the Simulation Argument. Awards: Royal College of Surgeons Arris and Gale Lectureship Hunterian Prize Wellcome Trust Research Fellowship Advising & Innovation: Supervised >50 PhD students, co-founded Oxford Medical Products (weight loss tech), and serves as CSO at Flagship Pioneering’s Preemptive Health division. He advises on NHS digital transformation and global health policy. Labs/Teams: Leads AI initiatives at Imperial’s Institute of Global Health Innovation and collaborates with Google, NICE, and international institutions on health tech solutions.

• Speech and Audio Processing
• Behavioral Signal Processing
• Real-Time MRI of Speech Production
• +۴ مورد دیگر

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.

Diyi Yangمشاهده پروفایل

استادیار

• Computational Social Science
• Natural Language Processing
• Socially Aware NLP
• +۴ مورد دیگر

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.

Laurel MacKenzieمشاهده پروفایل

دانشیار

• Variationist Sociolinguistics
• Dialectology
• Language Change
• +۵ مورد دیگر

Laurel MacKenzie is an Associate Professor in the Department of Linguistics at New York University (NYU), affiliated with the Faculty of Arts and Science. She specializes in variationist sociolinguistics, dialectology, and language change, with a focus on English and French varieties. Her work integrates quantitative analysis of speech data to explore intra-speaker variation and language evolution. She co-directs the NYU Sociolinguistics Lab and leads the NSF-funded NYC Individual Differences Corpus project, alongside the Our Dialects initiative, an online atlas of British English dialects. Education: PhD in Linguistics, University of Pennsylvania (2012) BA in Linguistics and French, University of California, Berkeley (2006) Research Interests: Morphological and syntactic variation Regional dialects of English and French Linguistic pedagogy and public engagement Recent Projects: Recent work includes publications on participle leveling in English, sociolinguistic replication studies, and grammatical variation analysis. She has also collaborated on dialect mapping tools and consulted for media projects on language change and accents. Awards: No awards explicitly listed in provided texts. Labs/Teams: NYU Sociolinguistics Lab (Co-Director) Our Dialects Project (Academic Lead)

Abhishek Jainمشاهده پروفایل

دانشیار

• Cryptography
• Computer Security
• Privacy
• +۵ مورد دیگر

Abhishek Jain is an Associate Professor in the Department of Computer Science at Johns Hopkins University and a Senior Scientist at NTT Research. He is affiliated with the Data Science and AI Institute, the Information Security Institute, and the Algorithms and Complexity group. University: Johns Hopkins University Department: Computer Science Academic Rank: Associate Professor Institutional Roles: Co-director of the Advanced Research in Cryptography group, member of the Theory Group, and co-leader of the Cryptography Group Education: Ph.D. in Computer Science from the University of California Los Angeles (2012), advised by Amit Sahai and Rafail Ostrovsky. Recipient of the Symantec Outstanding Graduate Student Award during his Ph.D. Research Interests Cryptography Secure computation Proof systems Program obfuscation Privacy Blockchain Theoretical computer science Research Trends: His recent publications emphasize cryptographic protocols for homomorphic encryption, zero-knowledge proofs, and secure multi-party computation, alongside applications in blockchain and privacy-preserving systems. Key themes include scalability, verifiable evaluation, and adapting cryptographic techniques to dynamic and distributed environments. Scientific Awards 2020 NSF CAREER Award Best Paper Awards at Eurocrypt and the International Conference on the Theory and Applications of Cryptographic Techniques Advising & Grants: Mentors Ph.D. students and interns/postdocs at NTT Research. Research funded by NSF, DARPA, JP Morgan, Ethereum Foundation, Stellar, Cisco, Samsung, and JHU Catalyst awards. Labs & Teams: Co-leads the Cryptography Group at Johns Hopkins, participates in the DC Area Crypto Day, and organizes the weekly Theory Seminar. Collaborates with institutions including MIT CSAIL, BUSEC, and Microsoft Research New England.