جستجوی استاد راهنمای دانشگاهی

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.

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

Kai-Wei Chang is an Associate Professor at the University of California, Los Angeles (UCLA) in the Department of Computer Science, part of the Henry Samueli School of Engineering. He is also an Amazon Scholar at Alexa AI, focusing on advancing trustworthy AI and multimodal foundation models. His research bridges NLP, machine learning, and ethical AI, with a focus on fairness, robustness, and bias mitigation in language and vision-language systems. Education: Ph.D. in Computer Science (UIUC, 2015), M.S. and B.S. in Computer Science and Electrical Engineering from National Taiwan University. Research Interests: Trustworthy NLP (fairness, robustness), Multimodal Foundation Models (e.g., VisualBERT, GLIP), Reasoning in LLMs, and mitigating societal biases in AI systems. Notable contributions include pioneering work on aligning NLP models with human values and developing SOTA multimodal models like DesCo and GLIP. Awards: Sloan Research Fellowship (2021), Okawa Grant (2018), EMNLP Best Paper (2017), KDD Best Paper (2010). His work is funded by NSF, IARPA, ONR, and industry partners like Amazon, Google, and Facebook. Service: VP-Elect of SIGDAT, Ethics Committee Chair (NAACL 2022), Organizer of Trustworthy NLP Workshops, and Senior Area Chair for top conferences (ACL, NeurIPS, AAAI). Labs/Teams: Leads the UCLA Natural Language Processing Group, fostering interdisciplinary research on ethical AI and multimodal systems.

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

David A. Smith is an Associate Professor at the Khoury College of Computer Sciences, Northeastern University. His research focuses on Natural Language Processing (NLP) and computational linguistics, with applications in machine translation, information retrieval, digital humanities, and social sciences. He is a founding member of the NULab for Texts, Maps, and Networks, a research center focused on digital humanities and computational social sciences. Smith's work has been funded by grants from the Mellon Foundation, NEH, and IMLS, supporting projects such as the Viral Texts initiative analyzing 19th-century newspaper networks and the Oceanic Exchanges project tracking transnational information flows. He has contributed to advancements in OCR for historical texts, text reuse detection, and computational analysis of classical languages. He has advised numerous PhD students, including Shijia Liu, Si Wu, and Ryan Muther, and teaches courses like Natural Language Processing and Information Retrieval. His research has been featured in outlets like Wired and the Economist .

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +۳ مورد دیگر

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۴ مورد دیگر

Ram Rajagopal is an Associate Professor of Civil and Environmental Engineering and Electrical Engineering at Stanford University, and a Senior Fellow at the Precourt Institute for Energy. He leads the Stanford Sustainable Systems Lab (S3L), focusing on large-scale monitoring, data analytics, and stochastic control for infrastructure networks, particularly power systems. His research emphasizes renewable energy integration, smart distribution systems, and demand-side data analytics. Education: PhD in Electrical Engineering and Computer Sciences & MA in Statistics (UC Berkeley), MS in Electrical and Computer Engineering (UT Austin), and BEng in Electrical Engineering (Federal University of Rio de Janeiro). Research interests include power grid optimization, renewable energy systems, and data-driven approaches to infrastructure challenges. He has pioneered work in grid flexibility, distributed energy resources, and machine learning applications for energy systems. His lab develops technologies like Smart Dim Fuses and the EV-EcoSim platform for EV charging infrastructure optimization. Received NSF CAREER Award, Powell Foundation Fellowship, and Berkeley Regents Fellowship Over 30 patents and best paper awards Advises/founded companies in sensor networks, power systems, and data analytics Labs/Teams: Stanford Sustainable Systems Lab (S3L), Powernet Project. His work spans grid resilience, energy equity, and scalable energy solutions.

• Smart Grids
• Renewable Energy Integration
• Power Systems Optimization
• +۳ مورد دیگر

Rana Hanocka is an Assistant Professor of Computer Science at the University of Chicago, leading the 3DL research group focused on AI-driven 3D geometry processing. She earned her Ph.D. in 2021 from Tel Aviv University under Professors Daniel Cohen-Or and Raja Giryes. Her work explores neural networks for unstructured 3D data, including mesh convolutional networks and self-priors for shape reconstruction. Key research areas include geometric deep learning, human-AI collaboration in 3D modeling, and interpretable neural networks. Notable contributions include MeshCNN (SIGGRAPH 2019) and Point2Mesh (NeurIPS 2020), advancing mesh analysis and point cloud processing. Awards include the 2023 Pazy Research Award and 2020 Rising Star in EECS. Education : Ph.D. Computer Science, Tel Aviv University (2021) Labs/Groups : 3DL Group at UChicago Grants : NSF Grant for AI-driven 3D modeling tools (2023) Research directions emphasize creative human-AI partnerships, unsupervised learning from shape collections, and explainable 3D neural networks. Ongoing projects include style-aware 3D synthesis, interactive segmentation, and multimodal shape interfaces.

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

Professor Janet B. Pierrehumbert is a leading academic in computational linguistics and natural language processing, holding the position of Professor of Language Modelling at the Oxford e-Research Centre, University of Oxford. She is also a Senior Research Fellow at Trinity College and affiliated with the Faculty of Linguistics, Philology and Phonetics. Her work bridges interdisciplinary research in phonology, sociolinguistics, and computational models of language dynamics. Education: PhD in Linguistics from MIT (1980), A.B. in Linguistics from Harvard University (1975). Research Interests: Focuses on computational linguistics, dialect variation, language dynamics, and the societal impacts of NLP. Her group develops algorithms for analyzing social media discourse, forecasting trends, and modeling language evolution. Recent work includes studies on dialect fairness in LLMs and semantic shifts in political discourse. Key Projects: EPSRC-funded research on online forum dynamics, the Wordovators project on lexical innovation, and collaborations with institutions like the Oxford Man Institute. Her work emphasizes robust NLP systems and theoretical linguistics. Awards: ISCA Medal (2020), National Academy of Sciences membership (2019), Fellowships from the American Academy of Arts and Sciences and Cognitive Science Society. Grants & Advising: Over £6M in research funding, including EPSRC and Templeton grants. Supervised over 30 PhD students and postdocs, many now leading academics and industry researchers in NLP and linguistics. Labs/Teams: Leads the Pierrehumbert Language Modelling Group, collaborating with the Oxford e-Research Centre and international partners like Stanford and the University of Canterbury.

• Computational Linguistics
• Natural Language Processing
• Phonology
• +۶ مورد دیگر

Sara Stymne is a Senior Lecturer in Computational Linguistics at the Department of Linguistics and Philology, Uppsala University, where she has been working since 2012. She initially joined as a post-doc (2012-2015), then worked as a researcher (2015-2017), and served as an assistant professor (2017-2023) before her current position as Senior Lecturer. Prior to Uppsala, she was a researcher at Linköping University's Department of Computer and Information Science. Dr. Stymne earned her PhD in Computational Linguistics from Linköping University in 2012 with the thesis 'Text Harmonization Strategies for Phrase-Based Statistical Machine Translation,' following a Licentiate degree in Computational Linguistics (2009) and a Master's degree in Cognitive Science (2006), both also from Linköping University. During her doctoral studies, she spent the autumn of 2010 and spring of 2009 at Xerox Research Centre Europe in Grenoble, France. Her primary research interests focus on cross-lingual natural language processing and digital humanities, with particular emphasis on multilingual dependency parsing. Dr. Stymne is passionate about applying computational linguistics to solve research questions in other fields, including language history, literary analysis, and political science. Her earlier work concentrated on machine translation, with specific interests in discourse-aware translation, compound processing, and error analysis. She has made significant contributions to the development of language technology tools for analyzing dialogue, narrative, and stylistic features in literature. Analysis of Dr. Stymne's recent publications reveals a strong focus on cross-lingual and cross-domain natural language processing. Her work spans multiple subfields including dependency parsing across genres and topics, discourse relation analysis in low-resource languages like Egyptian Arabic, direct speech identification in Swedish literature, and causality detection in governmental documents. A notable trend is her application of NLP techniques to digital humanities problems, particularly in analyzing literary texts and historical language change. Her research often involves creating and utilizing specialized datasets for specific linguistic phenomena across multiple languages. Dr. Stymne actively supervises graduate students, having guided numerous master's and bachelor's theses on topics ranging from speech recognition to multilingual parsing and causality detection. She leads or participates in several research projects including 'Fictional prose and language change' (funded by VR, 2021-2023) and 'Enabling climate-resilient development' (funded by Marianne and Marcus Wallenberg Foundation, 2023-2027), demonstrating her commitment to interdisciplinary research with practical applications. Her work has resulted in several notable software resources including uuPronPred for cross-lingual pronoun prediction, uuparser for dependency parsing, and Docent for document-level machine translation. Within the Computational Linguistics and Language Technology group at Uppsala University, Dr. Stymne contributes to multiple research initiatives focused on developing language technology tools for digital humanities applications. Her team works closely with literary scholars and historians to create computational methods for analyzing large corpora of literary texts, particularly focusing on Swedish literature across different historical periods. Her research bridges the gap between theoretical computational linguistics and practical applications in the humanities, creating new methodologies for quantitative analysis of literary and historical texts.

• Computational Linguistics
• Natural Language Processing
• Cross-lingual NLP
• +۷ مورد دیگر

Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.

• Machine Learning
• Computer Vision
• Statistical Inference
• +۳ مورد دیگر

Karsten Lambers is Professor of Digital and Computational Archaeology at the Faculty of Archaeology, Leiden University, where he leads research and teaching in the application of computational methods to archaeological data. His work integrates machine learning, remote sensing, text mining, and citizen science to advance archaeological prospection and heritage management. He is affiliated with the Department of Archaeological Sciences and plays key roles in research groups and university-wide initiatives such as SAILS and ARCHON. His research interests span Digital Archaeology , Machine Learning in Archaeology , Remote Sensing , Geoarchaeology , and Human-Environment Interaction . He investigates how computational tools can extract meaningful archaeological information from large datasets, including LiDAR imagery and excavation reports. His fieldwork spans Central Europe and Latin America, with a focus on prehistoric landscapes and cultural heritage. The analysis of his recent publications reveals a strong trend toward automated detection using deep learning (e.g., R-CNN, WODAN), named entity recognition in archaeological texts (e.g., ArcheoBERTje), and citizen science integration for data validation. His work bridges archaeology with computer science, geomatics, and environmental science, emphasizing interdisciplinary collaboration and methodological rigor. His scientific awards include: Best Thesis Award (University of Zurich, 2005) EUROPA NOSTRA Award (2020, 2022) Membership in the German Archaeological Institute (since 2022) Lambers actively supervises students and leads major research projects such as ABMA, EXALT, and Heritage Quest. He has secured substantial research funding and collaborates widely with computer scientists, geophysicists, and palaeoecologists. His teaching includes digital methods, modeling, and simulation, often linked to ongoing research. He has also contributed to open educational resources and digital textbooks in archaeology. He leads or participates in several research labs and teams, including the Digital Archaeology Research Group (which he chairs), the Heritage Quest citizen science project, and interdisciplinary teams focusing on alpine terraces and Iraqi prospection. His work emphasizes the integration of digital tools into practical archaeological workflows, advocating for complementary human-computer strategies.

• Digital Archaeology
• Computational Archaeology
• Machine Learning in Archaeology
• +۱۳ مورد دیگر

John Gallagher is Associate Professor in English and Information Sciences at UIUC. His research examines how writers adapt to participatory audiences in digital environments, including social media interactions and AI writing tools. Using case studies and computational methods, he explores algorithmic audiences, ethical implications of AI, and technical communication in institutional contexts. He teaches courses in professional writing, digital rhetoric, and research methodologies. Recent projects analyze content creators' narratives about platform algorithms, inclusive emoji design, and academic integrity challenges posed by generative AI.

• Digital Writing
• Audience Analysis
• AI Communication
• +۱ مورد دیگر

Raquel Fernández is Full Professor of Computational Linguistics and Dialogue Systems at the University of Amsterdam, where she leads the Dialogue Modelling Group at the Institute for Logic, Language & Computation (ILLC). As Vice-Director for Research at ILLC and a Fellow of the ELLIS Society, she bridges computational linguistics, cognitive science, and artificial intelligence through her research on language use in multimodal and conversational contexts. PhD in Computational Linguistics from King's College London Prior research positions at University of Potsdam and Stanford University's CSLI Her work explores how cognitive constraints, social interaction, and perception shape language use, with a focus on: Visually-grounded language processing Multimodal dialogue modeling Model uncertainty and calibration Language grounding in multimodal data Language learning and semantic change Dialogue reference resolution Recent publications analyze multimodal reasoning limitations, cross-lingual knowledge consistency, and uncertainty modeling in dialogue systems. She has received multiple accolades including an ERC Consolidator Grant , NWO VENI/VIDI/Aspasia fellowships , and EMNLP/GenBench awards . Outstanding Paper Award (EMNLP 2023) Best Data Award (GenBench Workshop 2023) ELLIS Society Fellow ERC Consolidator Grant #819455 recipient NWO VENI/VIDI/Aspasia awardee As a leader in academic service, she serves on the SIGDAT Executive Committee and chairs multiple conference committees. Her lab develops models for multimodal dialogue, visual storytelling, and grounded language understanding.

• Computational Linguistics
• Dialogue Systems
• Cognitive Science
• +۳ مورد دیگر

Renée J. Miller is a Professor and Canada Excellence Research Chair in Data Intelligence at the Cheriton School of Computer Science, University of Waterloo. Her research focuses on data integration, data management, and open data systems. She holds a PhD in Computer Science from the University of Wisconsin-Madison and bachelor’s degrees in Mathematics and Cognitive Science from MIT. Her work addresses challenges in data preparation, integration, and curation, aiming to reduce the burden on data scientists. She co-authored foundational papers on data exchange and schema mapping, earning the ICDT Test-of-Time Award (2013) and the Alonzo Church Award (2020). Miller has led major initiatives like the NSERC Business Intelligence Network and the International Very Large Data Base Foundation. Her grants include NSERC Accelerator Awards and funding from IBM, SAP, and Microsoft. Notable students include Ariel Fuxman (SIGMOD Dissertation Award winner) and Oktie Hassanzadeh (IBM PhD Fellow). Her research group, the Miller Lab, develops tools like Clio for schema mapping and systems for data lake exploration (RONIN, JOSIE).

• Data Integration
• Data Management
• Data Preparation
• +۵ مورد دیگر

Manex Aguirrezabal Zabaleta is an Associate Professor in the Department of Nordic Studies and Linguistics at the University of Copenhagen. He previously held positions as a Postdoc (2017-2019) and Assistant Professor at the same institution. His educational background includes: PhD in Natural Language Processing from the University of the Basque Country (UPV/EHU), conducted at the IXA NLP group. Master's degree in Natural Language Processing from UPV/EHU. Bachelor's degree in Computer Science (5 years) from UPV/EHU. Dr. Aguirrezabal's research focuses on the computational analysis of poetry , particularly stress patterns in English. He explores whether computers can effectively analyze poetic structures, a field with roots in the 1980s but revitalized by modern techniques. Additionally, he investigates language generation , computational morphology and phonology , and finite-state methods . His work bridges traditional linguistic inquiry with cutting-edge natural language processing. Recent publications (2023-2024) demonstrate a diverse engagement with computational linguistics, including poetry generation, multimodal corpus development, clickbait analysis, and fact-checking. His research often employs zero-shot learning and language models, reflecting current trends in AI-driven linguistic analysis. He has contributed to international collaborations such as ParlaMint (multilingual parliamentary corpora) and the GEHM Zoom corpus. While specific grant details are not provided, his active publication record indicates ongoing research support. Dr. Aguirrezabal maintains a strong connection to his Basque heritage, having pursued his early education in the Basque language.

• Natural Language Processing
• Computational Linguistics
• Poetry Analysis
• +۴ مورد دیگر

Associate Professor Mohsen Kalantari is a Geospatial Engineering academic at the University of New South Wales (UNSW) School of Civil and Environmental Engineering , with concurrent roles as co-founder of the startup Faramoon . His career spans roles at the University of Melbourne's Department of Infrastructure Engineering and Victorian government's land administration initiatives through DELWP. Education : PhD in Geomatics Engineering (2008, University of Melbourne), Master of GIS Engineering (2004), Bachelor of Surveying Engineering (2001) His research bridges geospatial engineering with construction automation , focusing on 3D cadastre , BIM-GIS integration , and smart cities . Recent publications show trends in underground land administration , digital twins , and LADM standard implementations . Scientific Awards : National educational recognition (2019), Victorian educational grants (2018), and prestigious fellowships (2012) As a supervisor , he guides PhD candidates in topics ranging from BIM for waste management to underground cadastral systems . His industry engagement includes partnerships with the United Nations , Open Geospatial Consortium , and Singapore Land Authority .

• Geospatial Engineering
• Construction Engineering
• Building Information Modelling (BIM)
• +۷ مورد دیگر