جستجوی استاد راهنمای دانشگاهی

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.

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

Robert West is an Associate Professor at EPFL (École polytechnique fédérale de Lausanne) in the School of Computer and Communication Sciences , leading the Data Science Lab (dlab) . His research focuses on Natural Language Processing , Machine Learning , and Computational Social Science , analyzing human-generated data from the web, social media, and online platforms. Education : PhD in Computer Science (2016) - Stanford University MSc in Computer Science (2010) - McGill University BSc in Computer Science (2007) - Technische Universität München Research Interests : West develops algorithms for analyzing large-scale web data, with emphasis on multilingual NLP , social network analysis , and AI ethics . His work bridges machine learning with social science to understand digital human behavior. Scientific Awards : ICWSM’22 Adamic–Glance Distinguished Young Researcher Award Google Faculty Research Award Facebook Research Award Multiple Outstanding Paper Awards at ICWSM and WWW Advising & Grants : He advises 12 PhD students and has secured funding from the Swiss National Science Foundation , Swiss Data Science Center , and industry partners. His lab maintains collaborations with Microsoft Research and CROSS . Labs & Collaborations : West leads the Data Science Lab at EPFL, which focuses on web-scale data analysis , privacy-preserving machine learning , and AI for social good . The lab develops tools like Wikispeedia and Quotebank for public data exploration.

• Natural Language Processing (NLP)
• Machine Learning
• Computational Social Science
• +۴ مورد دیگر

Kenji Kawaguchi is the Presidential Young Professor in the Department of Computer Science at the National University of Singapore (NUS), where he leads the Deep Learning Lab and is a faculty affiliate at the NUS Institute of Data Science. His research bridges theoretical and applied machine learning, focusing on deep learning, large language models, and physics-informed neural networks. His educational background includes a Ph.D. and S.M. in Computer Science and Electrical Engineering from the Massachusetts Institute of Technology (MIT), advised by Leslie Pack Kaelbling, and a postdoctoral fellowship at Harvard University’s Center of Mathematical Sciences and Applications. Dr. Kawaguchi’s research interests center on the theoretical foundations of deep learning, optimization, generalization, and applications in areas such as molecular modeling, AI safety, and efficient training of large models. He has made significant contributions to understanding in-context learning, diffusion models, and neural operators for partial differential equations. His recent publications (2023–2025) reflect a strong trend toward improving the efficiency, robustness, and interpretability of large-scale models, particularly in language and scientific domains. Key themes include LLM alignment and safety, diffusion model optimization, and physics-informed learning for high-dimensional problems. Presidential Young Professor He has served as Area Chair and PC Member for top-tier conferences including NeurIPS, ICML, ICLR, AAAI, and UAI, and as reviewer for journals such as JMLR and Annals of Statistics. He has delivered invited talks at Harvard, MIT, Stanford, CMU, Brown, and Google Research, reflecting his international recognition. He actively mentors students and welcomes PhD candidates and postdocs to join his research group.

• Deep Learning
• Machine Learning
• Theoretical Foundations of Deep Learning
• +۱۲ مورد دیگر

Mark Steedman is a Professor in the School of Informatics at the University of Edinburgh, where he conducts research in Artificial Intelligence, Computational Cognitive and Social Science, and Natural Language and Speech Processing. He is affiliated with the Institute for Language, Cognition and Computation (ILCC), the Centre for Speech Technology Research (CSTR), and the Human Communications Research Center (HCRC). He also holds an adjunct professorship in Computer and Information Science at the University of Pennsylvania. His research focuses on Combinatory Categorial Grammar (CCG) , computational linguistics , prosody and intonation , temporal semantics , gesture in communication , and computational music analysis . He has authored foundational books including Surface Structure and Interpretation , The Syntactic Process , and Taking Scope . The recent publications reflect a strong trend toward integrating formal grammatical frameworks like CCG with modern neural and distributional models, particularly in semantic parsing, entailment reasoning, and cognitive modeling. His work bridges symbolic and statistical approaches in NLP, often focusing on robust, wide-coverage parsing and semantic interpretation. Best Paper Award at AACL/IJCNLP 2023 for 'Smoothing Entailment Graphs with Language Models' Best Paper Award at ACL 2023 for 'Extrinsic Evaluation of Machine Translation Metrics' Influential Paper Award 2017 from IFAAMAS for 'Animated Conversation' Mark Steedman has supervised numerous PhD students and collaborated widely across institutions. He leads research in formal grammar applications to cognitive modeling, dialogue, and multimodal communication. His lab contributes to CCG software and semantic parsing tools, and he continues to be actively involved in advancing the integration of symbolic and neural AI.

• Artificial Intelligence
• Computational Cognitive Science
• Natural Language Processing
• +۹ مورد دیگر

Colin Raffel , currently an Associate Professor at the University of Toronto and Associate Research Director at the Vector Institute , is a leading researcher in machine learning and natural language processing . His career spans roles at Hugging Face (Faculty Researcher), Google Brain (Senior Research Scientist), and UNC Chapel Hill (Assistant Professor). Education: PhD in Electrical Engineering (Columbia), MA in Music/Science (Stanford), BA in Mathematics (Oberlin) Key affiliations: Google Brain (2016-2020), Hugging Face (2021-present), Vector Institute (2023-present) His research focuses on language model development , attention mechanisms , efficient machine learning , and music information retrieval . Recent work explores model merging , parameter-efficient fine-tuning , and data-constrained language models . Teaching : Has instructed courses at University of Toronto and UNC Chapel Hill on Neural Networks , Deep Learning , and Information Theory . Academic service includes organizing ICLR workshops and serving as Senior Area Chair for NeurIPS and EMNLP . Notable awards : NSF CAREER (2022), Caspar Bowden Award (2023), NeurIPS Outstanding Paper (2023) Key contributions : Core developer of WT5 , Git-Theta , and mir_eval software

• Machine Learning
• Natural Language Processing
• Music Information Retrieval
• +۱۲ مورد دیگر

Professor Anna Korhonen is a leading academic at the University of Cambridge, holding positions as Professor of Natural Language Processing, Co-Director of the Language Technology Laboratory (LTL), Director of the Centre for Human-Inspired Artificial Intelligence (CHIA), and Fellow of Churchill College. Her work bridges computational linguistics, artificial intelligence, and interdisciplinary applications. Her research focuses on human-centric NLP with core interests in multilingual/low-resource systems, conversational AI, and responsible technology development. She emphasizes applications for social and global good, including healthcare, climate science, and equitable language technologies. Her methodology integrates cognitive modeling with machine learning to create interpretable, fair NLP systems. Key projects include ERC-funded initiatives like MultiConvAI (multilingual conversational AI) and Towards Globally Equitable Language Technologies , alongside Innovate UK's ESG RoboFactory and EPSRC's Modeling Idiomaticity project. Her work spans biomedical text mining ( PheneBank , LION ), educational technology ( EF Education First Research Lab ), and cross-lingual transfer learning. Fellow of the Association for Computational Linguistics (ACL) Fellow of ELLIS (European Laboratory for Learning and Intelligent Systems) Royal Society University Research Fellow (2005-2014) Google Faculty Award recipient EACL Chair Elect Korhonen actively supervises PhD/MPhil students in Computation, Cognition and Language programs and leads interdisciplinary collaborations across computer science, linguistics, and domain sciences. Her lab (LTL) develops foundational NLP techniques while addressing real-world challenges through partnerships with healthcare, environmental science, and education sectors. Current strategic initiatives include the Institute for Technology and Humanity and CHIA's human-inspired AI framework.

• Natural Language Processing
• Multilingual NLP
• Low Resource NLP
• +۹ مورد دیگر

Adriana Kovashka is an Associate Professor at the University of Pittsburgh , affiliated with the School of Computing and Information and serving as Department Chair . Her academic journey began with BA degrees in Computer Science and Media Studies from Pomona College (2008) and a PhD in Computer Science from The University of Texas at Austin (2014). Joined Pitt’s faculty in January 2015 NSF CAREER awardee (2021) Google Faculty Research Award recipient Dr. Kovashka’s research spans Computer Vision , Machine Learning , and Natural Language Processing , focusing on visual rhetoric, weak multimodal supervision, and domain adaptation. She pioneered techniques for analyzing political imagery, developing robust object detection frameworks, and exploring the intersection of visual and textual persuasion through large-scale annotated datasets. Her recent work emphasizes geographic diversity in vision-language systems, audio-visual fusion for domain generalization, and shape-texture bias mitigation in CNNs. Key publications include groundbreaking studies on symbolic reasoning, multimodal dialogue systems, and ethical AI applications in education. Scientific honors include: NSF CRII Award (2016) NSF CAREER Award (2021) Pitt CRDF Award (2016, 2018) Best Paper at ECV Workshop (2021) Google Faculty Research Award (2016, 2018) Dr. Kovashka actively mentors students in multimodal learning projects and collaborates with interdisciplinary teams on NSF-funded initiatives. She co-organizes workshops like the first CVPR workshop on advertisement understanding and leads research groups exploring human-AI co-learning systems.

• Computer Vision
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Professor Bernd Möbius is a leading academic in Phonetics and Phonology at the Department of Language Science and Technology, Saarland University. His research bridges phonetic theory with speech technology applications, focusing on text-to-speech systems, prosody modeling, and computational simulations of speech processes. Current research projects: DFG SFB 1102, C1: Information density and phonetic structure predictability DFG SFB 1102, C4: Slavic intercomprehension and surprisal theory (INCOMSLAV) Research Themes: Key areas include text-to-speech synthesis, speech prosody analysis, experimental methods in speech production/perception, information density in phonetics, and cross-linguistic studies of Slavic-Germanic languages. Scientific Contributions: Recent work explores Parkinson-induced dysarthria detection, breath noise acoustics, surprisal-driven speech behaviors, multilingual BERT models for idiomaticity, and perceptual consequences of acoustic adjustments.

• Speech Technology
• Phonetics
• Phonology
• +۸ مورد دیگر

Alan Ritter is an Associate Professor at the School of Interactive Computing , Georgia Institute of Technology, with additional affiliation to the Machine Learning Center . His research focuses on Natural Language Processing , particularly robust models across domains/languages with fewer labels and efficient resource use, plus data-driven dialogue agents for open-topic conversations. Research Interests : Robust NLP models, cross-lingual transfer, resource-efficient learning, dialogue systems, cultural bias measurement, and privacy-aware language models Students : Mentors Ph.D. students in Georgia Tech's ML and CS programs, including Junmo Kang, Yang Chen, and Duong Minh Le. Alumni include Fan Bai (Ph.D. 2023), Yang Chen (Ph.D. 2024), and Andrew Li (M.S. 2024). Awards : NSF CAREER Award, Amazon Research Award, ACL 2024 Best Social Impact Paper, IUI 2009 Best Student Paper. Recent Work : Studies training budget allocation between supervised and preference-based finetuning, cross-lingual information extraction, cultural bias in LLMs, and privacy risk mitigation in social media disclosures. Service : Served as Program Chair for NAACL 2025, Area Chair for multiple top-tier conferences (COLM, EMNLP, ACL, EACL, AAAI). Email : alan.ritter@cc.gatech.edu

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Min Yen Kan is an Associate Professor and Vice Dean of Undergraduate Studies at the National University of Singapore's School of Computing, Department of Computer Science. With a PhD from Columbia University (2002), he leads the Web Information Retrieval / Natural Language Processing Group (WING.NUS) and serves as ACL Ethics Committee co-chair. His research spans Natural Language Processing , Large Language Models , Digital Libraries , and Information Retrieval , with specific focus on scientific discourse analysis, fact verification, and multimodal systems. Current projects include Scholarly Document Information Extraction (TRL 6), Task-Oriented Dialogue Systems (TRL 4), and Recommendation Systems (TRL 5). Recent publications reveal strong trends in LLM limitations (bias, hallucination, evaluation), conversational recommendation systems , and misinformation detection . His work consistently bridges theoretical NLP with real-world applications in digital libraries and scientific communication. Award highlights include: CIKM 2019 Best Paper Award ACL Distinguished Service Awards Vannevar Bush Best Paper Award (JCDL 2012) ACM Distinguished Speaker designation Kan mentors PhD students with placements at Google and USTC, and serves as associate editor for Information Retrieval and survey editor for Journal of AI Research . His lab WING.NUS develops practical tools like SciWING for scientific document processing and FANG for fake news detection. Media engagements include commentary on AI regulations in Southeast Asia and workforce implications in the AI era.

• Natural Language Processing
• Large Language Models
• Digital Libraries
• +۷ مورد دیگر

WANG Ye is an Associate Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a PhD in Information Technology from Tampere University of Technology, Finland, and has been a tenured faculty member at NUS since 2002, following his industry research role at Nokia Research Center. He is the director of the Sound and Music Computing Lab at NUS, leading cutting-edge research in AI-driven music and health technologies. PhD, Information Technology, Tampere University of Technology, Finland (2002) MSc, Telecommunications, Braunschweig University of Technology, Germany (1993) BSc, Telecommunications, South China University of Technology, China (1983) His research is centered on Sound and Music Computing for Human Health and Potential (SMC4HHP) , with a focus on eHealth, eLearning, mobile/wearable computing, and music information retrieval. His work spans AI for stroke rehabilitation, language learning through singing, singing voice synthesis, and automatic music transcription. He has pioneered systems like SLIONS (language learning via karaoke), CocoLyricist (AI co-creation for stroke recovery), and SinTechSVS (expressive singing voice synthesis). The latest articles highlight a strong trend in AI-driven music and health technologies , particularly in controllable lyric generation, singing voice synthesis, automatic pronunciation assessment, and multimodal music transcription. The research increasingly integrates large language models, explainable AI, fairness, and real-world deployment, reflecting a shift from theoretical exploration to practical, human-centered applications in healthcare and education. Dr. Wang has received numerous scientific honors, including: Best Paper Awards at ACM MM, ISMIR, IEEE ISM, and CHI First Prize, Asia Pacific Assistive, Rehabilitative, and Therapeutic Technologies Challenge (2015) Faculty Teaching Excellence Award, NUS School of Computing (2024) Top Paper Award, ACM Multimedia 2022 AI in Medicine Collaborative Grant for CocoLyricist project He has supervised over 11 PhD and 20 MComp students and is currently guiding six PhD candidates. His grants come from MOE, NRF, A*STAR, Nokia, and Smule. He has served as General Chair of ISMIR2017 and TPC Co-Chair of ICOT2017, and is on the editorial boards of IEEE Transactions on Multimedia and Journal of New Music Research. He has also developed and taught the first course on Sound and Music Computing in Singapore. Dr. Wang leads the Sound and Music Computing Lab (SMC Lab) , a multidisciplinary team exploring the synergy of music computing, AI, mobile technology, and cloud systems for health and education. The lab actively collaborates with medical institutions such as NUS Yong Loo Lin School of Medicine, Singapore General Hospital, and Harvard Medical School, and is currently working on projects in AI-supported language learning, stroke rehabilitation, and intelligent music interfaces.

• Sound and Music Computing
• Music Information Retrieval
• eHealth
• +۱۲ مورد دیگر

David Alvarez-Melis is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). He leads the Data-Centric Machine Learning (DCML) group and holds affiliations with the Kempner Institute, Harvard Data Science Initiative, and the Center for Research on Computation and Society. His research focuses on making machine learning more data-efficient and trustworthy, with applications in natural and medical sciences. He also serves as a researcher at Microsoft Research New England. Affiliations: SEAS, Kempner Institute, Harvard Data Science Initiative, CRCS Education: PhD in Computer Science (MIT), MS in Mathematics (NYU Courant), BSc in Applied Mathematics (ITAM) Research Interests: Optimal Transport, dataset distillation, interpretable AI, medical imaging, robustness, and large language models. His work bridges theory and applications, emphasizing geometric and probabilistic methods. Recent Trends in Publications: Focused on advancing optimal transport for data manipulation, distributional deep equilibrium models, and repurposing LLMs for specialized domains. Key themes include synthetic dataset generation, gradient flows in probability spaces, and robust interpretability frameworks. Awards: Aramont Fellowship, Dean’s Competitive Fund, Top Reviewer awards at major conferences (ICLR, NeurIPS, ICML). Grants: Supported by the Aramont Fund and Harvard’s Dean’s Fund. His lab advises students across Harvard and MIT, with notable contributions to medical imaging, NLP, and foundational ML theory. He actively mentors interns and fosters collaborations with industry and academia.

• Machine Learning
• Artificial Intelligence
• Optimal Transport
• +۴ 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

Karthik R. Narasimhan is a Professor at Princeton University's School of Engineering and Applied Science in the Department of Computer Science. Previously, he earned his PhD from MIT under Regina Barzilay and served as a visiting research scientist at OpenAI during 2017-18. His research focuses on the intersection of language and decision-making, building autonomous agents that learn from both experience and human knowledge. His research spans multiple high-impact areas including language agents (Text-DQN, CALM, ReAct, Tree of Thoughts), reinforcement learning (h-DQN, Multi-Objective RL), and AI safety (Toxicity in ChatGPT, DataMUX). He has developed critical datasets and benchmarks such as WebShop, InterCode, SWE-bench, and SILG that have become standard evaluation tools in the field. Current work emphasizes agent capabilities, software engineering automation, and multimodal interaction. His publication trends show strong focus on practical agent deployment (SWE-agent, Tree of Thoughts), safety evaluation (Probing AI Safety), and efficiency improvements (DataMUX). Recent work increasingly addresses real-world challenges in software engineering, security, and human-AI collaboration through rigorous benchmarking. Co-author of foundational GPT (2018) paper Key developer of Text-DQN (2015), CALM (2020), ReAct (2022), Tree of Thoughts (2023) Creator of influential benchmarks: WebShop (2022), SWE-bench (2023), InterCode (2023) He actively advises students through Princeton's computer science program, with research supported by multiple grants focused on autonomous agent development and language-based decision systems. His GitHub repositories (nlp-datasets, text-world-player) demonstrate strong community engagement in open-source research tools. Current projects include advancing language agent capabilities through Reflexion (2023) and Tree of Thoughts (2023) frameworks while addressing critical safety and efficiency challenges.

• Language Agents
• Reinforcement Learning
• Natural Language Processing
• +۳ مورد دیگر

Lyle Ungar is a Professor at the Department of Computer and Information Science at the University of Pennsylvania . He is affiliated with multiple graduate groups, including Genomics and Computational Biology in the School of Medicine , Operations, Information and Decisions in the Wharton School , and Psychology in the School of Arts and Sciences . His research focuses on explainable machine learning , deep learning , and natural language processing for psychology and medical research , analyzing social media and sensor data to understand well-being, empathy, and stress. His work spans bioinformatics , applied economics , and group decision-making . Recent publications examine LLM-based tutoring , cross-cultural translation , and AI in palliative care , showing trends in reinforcement learning , mobile health , and health data analytics . He has contributed to Google Scholar , PubMed , and DBLP with over 15 papers since 2023. Scientific Awards : 2019 Alan I. Leshner Leadership Institute Public Engagement Fellow His students include Vitoria Aquino Guardieiro , Yihao Li , and co-advised researchers like Shreya Havaldar with Eric Wong. He leads projects at interdisciplinary centers such as the Annenberg Public Policy Center , Center for Cognitive Neuroscience , and Institute for Translational Medicine .

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۱۵ مورد دیگر