جست‌وجوی استاد و پژوهشگر

• Data Science
• Machine Learning
• Spatial Analysis
• +۵ مورد دیگر

Curdin Derungs is a Lecturer in Data Science at the Lucerne School of Computer Science (HSLU), Switzerland. He specializes in applying statistical and machine learning techniques to energy systems, spatial analysis, and natural language processing. His professional competencies include data-driven automation, deep learning, time series analysis, and energy systems optimization. Derungs holds a PhD in Natural Sciences (University of Zurich, 2013) with a focus on NLP and spatial analysis, and a DAS in Applied Statistics (ETH Zurich, 2019). Earlier degrees include a Master's in Geography (2008) and Atmospheric Physics (ETH Zurich, 2008). His research explores intersections between data science and environmental sustainability, including energy efficiency optimization, spatial language dynamics, and landscape modeling. Recent work focuses on occupant behavior in energy systems, geotagged text analysis, and soil formation modeling. Key projects include SCCER FEEB&D Work Package 2 on renewable energy systems, the Romande Energy Demonstrator, and urban greening studies. He actively contributes to interdisciplinary initiatives like the IGE Innovationswettbewerb 2019 and Innovationspark Zentralschweiz.

Stefan Müllerمشاهده پروفایل

دانشیار

• Political Representation
• Party Competition
• Political Communication
• +۹ مورد دیگر

Stefan Müller is an Associate Professor at the School of Politics and International Relations , University College Dublin. He specializes in political representation , party competition , political communication , and quantitative text analysis . His work bridges computational methods with traditional political science research. Leads two funded projects: one on environmental/energy policy with NexSys, and another analyzing grant peer review reports using machine learning for the Swiss National Science Foundation Core member of the Connected_Politics Lab and co-author of the quanteda R package Maintainer of the Irish Polling Indicator and contributor to national/international media on Irish and European politics Research Focus: Combines NLP and machine learning to analyze political manifestos , legislative patterns , and policy communication . Key areas include campaign communication , agenda setting , grant peer review , and social media impact on politics. Scientific Recognition: Received multiple teaching awards including UCD’s University Teaching Excellence Award (2023) Won the Society for Political Methodology Software Award (2020) for quanteda package Walter Lippmann Best Article Award (2022) for televised debate analysis Collaborative Networks: Works with multidisciplinary teams across institutions including University of Zurich , Swiss National Science Foundation , and all-Island SFI Strategic Partnership Programme . Mentors PhD researchers and maintains open-access educational resources through GitHub repositories.

• Low-Resource Language Processing
• Natural Language Processing (NLP)
• Machine Translation
• +۷ مورد دیگر

Dr. Ignatius Ezeani is a Research Fellow in the Department of Computing and Communications at Lancaster University, specializing in Low-Resource Natural Language Processing (LowResNLP). His work focuses on developing tools and methods for under-resourced languages, particularly African languages such as Igbo, and minority languages like Welsh. He leads projects such as the Igbo-English Machine Translation initiative and the Welsh Summary Creator, advancing computational linguistics in these domains. His research interests include machine translation, part-of-speech tagging, named entity recognition, and corpus-based approaches to language processing. He is affiliated with the University Centre for Computer Corpus Research on Language (UCREL), contributing to data science and linguistic resource development. Ezeani supervises PhD student Chiamaka Chukwuneke and collaborates on initiatives like the National Corpus of Contemporary Welsh (CorCenCC). His projects emphasize participatory research, ensuring technologies address real-world linguistic needs in underrepresented communities. Key contributions include the IgboAPI Dataset, IgboBERT models, and frameworks for spatial narrative analysis. Ezeani’s work bridges computational linguistics with societal impact, fostering multilingualism through technology.

Eric Millerمشاهده پروفایل

استاد

• Signal and Image Processing
• Tomographic Image Formation
• Inverse Problems
• +۴ مورد دیگر

Eric Miller is a Professor of Electrical and Computer Engineering at Tufts University's School of Engineering. He also holds adjunct professorships in Computer Science, Biomedical Engineering, and Mathematics. His academic roles include serving as Chair of the Electrical and Computer Engineering department and leading the Lab for Imaging Science Research (LaISR). Miller earned his SB, SM, and PhD in Electrical Engineering from MIT (1990–1994). His research focuses on signal and image processing, particularly inverse problems, tomographic imaging, and applications in medical imaging, environmental monitoring, and security screening. He has pioneered methods like the parametric level-sets (PaLEnTIR) for reconstruction and shape-based inversion algorithms. His work integrates physics-based modeling with computational techniques, addressing challenges in subsurface sensing, biomedical diagnostics, and materials science. Miller is a Fellow of IEEE and a member of honor societies like Tau Beta Pi and Phi Beta Kappa. Over 489 publications reflect his contributions to imaging science, with recent advancements in AI-driven pedestrian behavior analysis and X-ray anomaly detection.

• Probabilistic Programming
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Jan-Willem van de Meent is an Associate Professor at the University of Amsterdam where he co-directs the AMLab with Max Welling. He also maintains an Assistant Professor position at Northeastern University, though currently on leave while continuing to advise students and collaborate. His research develops AI models by combining probabilistic programming and deep learning, focusing on understanding inductive biases that enable models to generalize from limited data. His research spans multiple domains: Probabilistic programming frameworks and inference methods Inductive biases for generalization from limited data Physical system simulators incorporating domain knowledge Causal structure and symmetries in AI models Applications in robotics, NLP, healthcare, and physical sciences Van de Meent is one of the creators of Anglican, a probabilistic programming language based on Clojure, and currently develops Probabilistic Torch, a library for deep generative models extending PyTorch. He is writing a book on probabilistic programming (draft available on arXiv) and serves as co-chair of the international conference on probabilistic programming (PROBPROG). His recent publications show a strong trend toward developing more efficient inference methods for probabilistic models, exploring disentangled representations across vision and language domains, and applying these techniques to healthcare, robotics, and neuroscience. His work on nested variational inference, energy-based models, and state abstraction in reinforcement learning has been particularly influential. Awards and Recognition NSF CAREER award (2021) Van de Meent actively advises multiple PhD students and postdocs across interdisciplinary projects. His lab maintains strong collaborations with researchers in robotics, healthcare, neuroscience, and other scientific domains, applying advanced probabilistic modeling to challenging real-world problems. He also develops practical tools for the research community, making advanced inference techniques more accessible to practitioners.

Wei Xuمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Wei Xu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. She holds affiliations with the Machine Learning Center (ML@GT) and the NSF AI CARING Institute. Her work emphasizes ethical AI applications, cultural bias mitigation, and assistive technologies. Education: Xu earned a Ph.D. in Computer Science from New York University, and both B.S. and M.S. degrees from Tsinghua University. Research Interests Natural Language Processing (NLP) with a focus on text generation, stylistics, and controllability of ML models Machine Learning robustness and ethical implications Social media analysis, including misinformation detection and user privacy Assistive technologies for reading/writing accessibility Cultural biases in large language models (LLMs) Human-centered evaluation of AI systems Key Contributions Her recent work explores privacy risks in online self-disclosures, LLM responses to implicit misinformation, and regional cultural awareness in AI. She also develops benchmarks like CROSSNEWS for authorship verification and Thresh for text evaluation. Awards NSF CAREER Award (2021) CrowdFlower AI for Everyone Award (2021) Criteo Faculty Research Award (2021) Best Paper Award at COLING'18 (2018) Grants & Funding Xu has secured funding from DARPA, IARPA, and the NSF AI CARING Institute. Her collaborations include the Machine Learning Center at Georgia Tech.

Zhuang Liمشاهده پروفایل

مدرس

• Natural Language Processing
• Machine Learning
• Data-efficient Learning
• +۵ مورد دیگر

Dr. Zhuang Li is a Lecturer at RMIT University's School of Computing Technologies, specializing in Natural Language Processing (NLP), machine learning, and trustworthy AI. He earned his PhD from Monash University (2023), focusing on semantic parsing in low-resource conditions, and previously worked at Microsoft on Cortana and Bing. His research bridges NLP, computational social science, and cultural alignment of AI systems, emphasizing data-efficient training and LLM safety. Education: PhD in NLP, Monash University (2019–2023) M.Comp in AI, Australian National University (2014–2015) B.Eng in Electrical Engineering, Wuhan University of Science and Technology (2009–2013) Research Interests: Data-efficient learning, culturally-grounded AI, large language models, computational social science, and ethical AI deployment. Collaborations: Active in industry partnerships and academic collaborations with Monash University, TU Darmstadt, Microsoft Research India, and Ant Group. Recent work includes ACL 2025 papers on LLM safety and peer review analysis. Supervision: Open to PhD students in low-resource languages and culturally-aligned LLMs. Collaborates with senior faculty at RMIT and Monash.

• Corpus architectures
• Historical corpora
• Parallel corpora
• +۵ مورد دیگر

Martin Klotz is a Researcher at the Institute for German Language and Linguistics, Humboldt University of Berlin, affiliated with the Collaborative Research Center (SFB1412) and the Research Unit for Emerging Grammars (RUEG). His work focuses on corpus linguistics, computational linguistics, and digital humanities infrastructure. He holds an MSc in Cognitive Systems (2019, University of Potsdam) and a BA in German Linguistics & Computer Science (2015, Humboldt University). Key research interests include corpus architectures, historical and parallel corpora, language modeling for non-standard varieties, and research software engineering. He contributes to projects like the RUEG corpus and FALKO learner corpus, emphasizing multilingual and multimodal corpus development. His publications span corpus design, software tools (e.g., Annatto), and analyses of heritage languages. He actively organizes academic events, such as the 2021 DGfS short working group on contrastive corpus methodology. He participates in lab collaborations like Deutsch Diachron Digital and contributes to open-source corpus tools. His work bridges computational methods with linguistic theory, focusing on dynamic language systems.

Marcel Bollmannمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Under-Resourced Languages
• +۳ مورد دیگر

Marcel Bollmann is an Associate Professor at the Department of Computer and Information Science (IDA) at Linköping University, Sweden, where he leads research in the Artificial Intelligence and Integrated Computer Systems (AIICS) division. His work focuses on natural language processing (NLP), machine learning, and computational linguistics, particularly in under-resourced languages, multilinguality, and historical document analysis. Education: He holds a PhD in Computational Linguistics from Ruhr-Universität Bochum (2018), funded by projects on historical German language corpora. Previously, he was a postdoc at the University of Copenhagen (2018–2021), supported by an EU Marie Skłodowska-Curie Fellowship, and an assistant professor at Jönköping University (2021–2023), where he developed AI Engineering courses. Research: Bollmann’s interests include linguistically-informed NLP approaches, historical text normalization, and improving NLP accessibility across language genres. His work has addressed challenges like morphology-aware models and low-resource language support. Awards: His 2021 paper on morphology’s role in error analysis won the EACL Best Long Paper Award. He also contributed to the ACL Anthology as Site Development Lead, advancing NLP infrastructure. Advising & Grants: He has supervised projects on historical language corpora and received funding from EU initiatives. His lab collaborations focus on advancing AI through interdisciplinary methods. Labs/Teams: Active in the AIICS division at IDA, contributing to WASP (Wallenberg Autonomous Systems Program) and other interdisciplinary AI initiatives.

Grzegorz Krynickiمشاهده پروفایل

پژوهشگر

• Articulatory Phonetics
• Computer Assisted Pronunciation Teaching
• Laboratory/Experimental Phonology
• +۷ مورد دیگر

Grzegorz Krynicki is a Senior Researcher at the Faculty of English , Adam Mickiewicz University, Poznań, Poland. He leads the Speech and Language Processing Laboratory , focusing on experimental phonetics, machine translation, and computer-assisted language learning. With a Ph.D. in English (2006) and MA in English (2000) from Adam Mickiewicz University, he has contributed extensively to articulatory phonetics and Polish-English corpus linguistics. Education MA in English, Poznań (2000) Ph.D. in English, Poznań (2006) His research interests bridge Articulatory Phonetics and Machine Learning , with applications in Computer Assisted Pronunciation Teaching , Automatic Speech Recognition , and Corpus Linguistics . He has developed tools like Wordbuilder for corpus-based exercises and IPA2Polglish for phonetic transcription conversion. The articles reflect his expertise in electropalatography (EPG) for L2 pronunciation teaching, phonetic error detection in Polish learners of English, and machine translation systems like POLENG. His work spans Multilingualism , Speech Technology , and Polish-English Contrastive Analysis . Scientific Awards University of Bielefeld scholarship (1999) Professional Activities include participation in the International Society of Phonetic Sciences and development of the Speech and Language Processing Laboratory . He has presented at conferences such as the International Congress of Phonetic Sciences (2019) and Societas Linguistica Europaea meetings, often collaborating with institutions like the University of Colorado and Linköping University.

• Artificial Intelligence
• Natural Language Processing
• Intelligent Tutoring Systems
• +۷ مورد دیگر

Reva Freedman is an Associate Professor in the Department of Computer Science at Northern Illinois University . Her work bridges Artificial Intelligence , Natural Language Processing , and Intelligent Tutoring Systems , with additional interests in Scientometrics and Cognitive Science . Education: Ph.D. in EECS (Northwestern University) M.A. in EECS (Northwestern University) B.A. in Mathematics (University of Chicago) Research Interests include: AI and Computational Linguistics: Text generation, knowledge representation, declarative formalisms (logic programming, constraint-based programming), dialogue annotation Linguistics: Romance and Semitic language structures Educational Technology: Gamified learning tools, adaptive systems for anatomy and physiology education Key article trends show a focus on AI-driven educational tools (2023), deep learning for stylistic analysis (2024), and long-term research impact metrics (2022). Publications span NLP, ITS, and scientometric studies. Advising and Collaborations: Co-author of ITS projects (ALAST, CRAM) with teams including Pi-Sui Hsu, Virginia Naples, and Ben Kluga Collaborated on cross-disciplinary studies with Ashli Fain (syntax), Melissa Wright (literary analysis), and others Labs and Teams: Actively involved in developing interactive tutoring systems (ALAST, CRAM) and contributing to graduate research methods courses. Notable projects include gamified tools for middle-school science and minigame-based physiology teaching systems.

William W. Cohenمشاهده پروفایل

استاد مهمان

• Machine Learning
• Natural Language Processing
• Neuro-Symbolic Reasoning
• +۵ مورد دیگر

William W. Cohen is a Visiting Professor at Carnegie Mellon University's Machine Learning Department and holds a 20%-time appointment at Google. He earned his PhD in Computer Science from Rutgers University in 1990 and has held roles at AT&T Bell Labs, Whizbang Labs, and CMU. His research focuses on machine learning, NLP, neuro-symbolic reasoning, and knowledge representation. Education: B.S. (Duke, 1984), PhD (Rutgers, 1990). Research Interests: Cohen's work spans question answering, NLP tasks, and neuro-symbolic systems. He emphasizes scalable reasoning methods and has contributed to systems like Never-Ending Learning (NELL) and knowledge graph construction. Recent efforts include improving large language model evaluation and retrieval-augmented generation. Awards: AAAI Fellow (2006), 2008 SIGMOD Test of Time Award, 2014 SIGIR Test of Time Award, 2023 Semantic Web Science Ten-Year Award. His work on subtopic retrieval (SIGIR 2003) and data integration (SIGMOD 1998) are foundational. Grants & Advising: Supervised over 50 students, including notable figures like Bhuwan Dhingra (now at Duke) and Zhilin Yang. Active in funding projects related to AI ethics, knowledge graphs, and scalable learning systems. Labs & Teams: Core contributor to the NELL project and collaborator on initiatives like the Knowledge Vault and Open Information Extraction systems. Currently involved in developing robust AI evaluation frameworks and multimodal reasoning systems.

Nizar Bouguilaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Clustering
• +۷ مورد دیگر

Prof. Nizar Bouguila is a Professor at the Concordia Institute for Information Systems Engineering (CIISE), Concordia University, and holds the Concordia University Research Chair in Applied Artificial Intelligence. His research focuses on machine learning, data clustering, smart building systems, and energy management. He actively supervises graduate students in Computer Science, Electrical and Computer Engineering, and Information Systems Security programs. Dr. Bouguila's work integrates probabilistic models, deep learning, and domain adaptation techniques. He has pioneered methods in energy disaggregation, load forecasting, and anomaly detection in smart infrastructure. His recent projects address challenges in occupant behavior prediction, speech emotion recognition, and fake news detection using Arabic datasets. Key research themes include: Probabilistic clustering with Bayesian nonparametric mixtures Smart building analytics via IoT and sensor data Domain adaptation for cross-domain learning Explainable AI in energy systems His 2023-2025 publications emphasize: Advanced transformer networks for load forecasting Graph neural networks in transportation Multimodal data fusion for anomaly detection Topic modeling for short texts Dr. Bouguila's advising spans multiple engineering and computer science disciplines, reflecting his interdisciplinary research profile.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۷ مورد دیگر

Professor Julie Weeds is a faculty member at the School of Engineering and Informatics , University of Sussex. Her academic journey includes a DPhil in Natural Language Processing (2003), MPhil in Computer Speech and Language Processing (2000), and MA in Computer Science (1998). She has held positions including Postdoctoral Research Fellow (2003-2005, 2012-2016, part-time), Lecturer in Data Science (2016-present), and Professor of Artificial Intelligence (2023-present). MA Computer Science - Trinity Hall, Cambridge University (1995-1998) MPhil Computer Speech and Language Processing - Cambridge University (1999-2000) DPhil Natural Language Processing - University of Sussex (2000-2003) Her research spans Natural Language Processing , Machine Learning , and Computational Linguistics , focusing on semantic compositionality , vector representation analysis , and linguistic variation . Recent work applies NLP to dream reports , wildlife conservation , and mental health support . She leads grants related to hate speech analysis , suicide prevention , and illegal wildlife trade detection . Publications reveal trends in semantic entailment modeling , dream analysis using LLMs , and ecological informatics applications . Key themes include syntax-driven compositionality , multilingual modeling , and structure-aware paraphrase identification . Professor Weeds supervises research projects and teaches courses including Advanced Natural Language Engineering, Natural Language Processing, and Data Science methods. Collaborators include David Weir, Luca Bertolini, and Qi Peng, with affiliations to CASM Consulting LLP and Innovate UK projects.

• Machine Learning
• Large-Scale Data Analysis
• Bioinformatics
• +۳ مورد دیگر

Inderjit Dhillon , Professor at the University of Texas at Austin , is a leading researcher in machine learning and big data analytics. As Director of the Center for Big Data Analytics, his work focuses on scalable algorithms for high-dimensional data, social network analysis, and gene-disease association prediction. ACM, IEEE, SIAM, and AAAS Fellow Gottesman Family Centennial Professor (2014) ICES Distinguished Research Award (2013) Research Interests: His contributions span machine learning , bioinformatics , and scientific computing , with recent work emphasizing deep learning for time series, adversarial robustness, and distributed optimization. Publications in top venues like NeurIPS and ICML highlight his expertise in inductive matrix completion and non-convex optimization . Scientific Awards: 2016: AAAS Fellow 2014: ACM, SIAM, IEEE Fellow 2013: ICES Distinguished Research Award 2011: SIAM Outstanding Paper Prize His students and collaborators frequently publish in areas like extreme classification , kernel methods , and multi-scale spectral decomposition . Labs and teams under his leadership have advanced nomadic computing and asynchronous optimization for massive datasets.