جست‌وجوی استاد و پژوهشگر

• Algorithmic Fairness
• Responsible AI
• Deep Learning
• +۵ مورد دیگر

Golnoosh Farnadi is an Assistant Professor at McGill University's School of Computer Science , an Adjunct Professor at Université de Montréal , and a Visiting Faculty Researcher at Google Research . She holds the Canada CIFAR AI Chair and is a core academic member of Mila – Quebec AI Institute . Her research focuses on algorithmic fairness , responsible AI , and optimization . She founded the EQUAL Lab (EQuity & EQuality Using AI and Learning algorithms) to address bias and discrimination in AI systems. Key publications explore fairness in kidney exchange programs, generative model geometry, multilingual LLM de-biasing, and prototype-based recommender systems. Her work bridges causal inference, adversarial robustness, and ethical AI. Google Award for Inclusion Research (2023) Women in AI Awards North America Finalist (2023) Facebook Privacy Enhancing Technologies Award (2021) IVADO Postdoctoral Fellowship (2018–2021) She has supervised over 15 PhD and Master's students, including Prakhar Ganesh (McGill) and William St-Arnaud (Université de Montréal). Her teaching includes Responsible AI and Machine Learning courses at McGill and HEC Montréal.

• Artificial Intelligence
• Natural Language Processing
• Computational Semantics
• +۵ مورد دیگر

Luis Espinosa-Anke is a Senior Lecturer at Cardiff University's School of Computer Science and Informatics. His academic journey includes working as a Natural Language Processing (NLP) scientist at Savana Médica, a Madrid-based healthcare AI company, prior to joining Cardiff. He completed his PhD at Pompeu Fabra University in Barcelona while working at Savana. Dr. Espinosa-Anke's research focuses on Artificial Intelligence and NLP, with particular emphasis on meaning representation, computational semantics, multilingual NLP, and computational lexicography. His work spans theoretical and applied aspects of language technology, with applications in healthcare, social media analysis, and multilingual systems. His recent publications reveal a strong trend toward analyzing language model behavior, bias detection in AI systems, and creating resources for semantic analysis. The publications show increasing focus on practical applications of NLP in healthcare, social media, and cross-lingual settings, with notable contributions to datasets like WIKITIDE and 3D-EX that support definition extraction and semantic understanding. laCaixa Fellow Fulbright scholarship recipient Erasmus Mundus program participant Dr. Espinosa-Anke has secured research funding including a Kaggle Open Research grant ($2,000 USD) as PI for the 'Don't Patronize Me!' project, a Snap Inc. grant ($10,000 USD) as CO-I for modeling meaning shift in social media, and a £90,000 Welsh Government grant for English-Welsh bilingual embeddings research. He currently supervises five PhD students working on meaning representations, contextual word embeddings, NLP for healthcare applications, and metaphor identification.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

• Machine Learning
• Time Series Analysis
• Space Weather Prediction
• +۴ مورد دیگر

Soukaina Filali Boubrahimi serves as an Assistant Professor in the Computer Science Department within the College of Engineering at Utah State University. Her academic appointment is based in the SER 332 building located at 4205 Old Main Hill, Logan, UT 84322-0001. She maintains a research-active position with a focus on computational methods for complex temporal data analysis. Dr. Filali Boubrahimi's research program centers on time series analysis , machine learning , and space weather prediction , with particular emphasis on solar flare forecasting and counterfactual explanation systems. Her work bridges theoretical machine learning advancements with practical applications in heliophysics, hydrology, and social media analysis. The research portfolio demonstrates significant expertise in handling imbalanced temporal datasets, developing novel data augmentation techniques, and creating interpretable AI systems for critical prediction tasks. Analysis of her recent publication trajectory reveals consistent contributions to counterfactual explanation frameworks for time series data (Info-CELS, M-cels, ACTS), space weather prediction systems (solar flare and energetic particle event forecasting), and generative modeling approaches (AVATAR, ChronoGAN). Her work frequently addresses the challenges of severely imbalanced datasets through contrastive learning and sophisticated preprocessing techniques, demonstrating methodological innovation in handling rare but critical space weather events. While no specific awards are documented in the available information, her research program appears substantial based on the volume and quality of recent publications spanning multiple high-impact domains. The research demonstrates strong interdisciplinary connections between computer science, space physics, and environmental science. Her laboratory activities focus on developing machine learning frameworks for temporal data analysis, with particular attention to space weather prediction systems. The research group appears to specialize in creating robust models for rare event prediction, explainable AI systems for time series classification, and novel data augmentation techniques for imbalanced temporal datasets. Current projects likely include the development of multimodal fusion approaches for solar energetic particle prediction and spatio-temporal modeling for hydrological applications.

• Comparative Literature
• Anglophone Indian Literature
• German Literature and Culture
• +۸ مورد دیگر

Dr. Leila Essa is an Assistant Professor of Comparative Literature at Utrecht University, affiliated with the Department of Languages, Literature and Communication within the Humanities faculty. She co-leads the Modern and Contemporary Literature research group at Utrecht University alongside Ewout van der Knaap. Her academic work bridges German, English, and Indian literary traditions with a focus on postcolonial perspectives and contemporary cultural production. Her educational background includes a BA in Comparative Literature from King's College London (2014), an MPhil in European Literature and Culture from the University of Cambridge (2015), and a PhD in German, English and Comparative Literature from King's College London (2020). Her doctoral research involved extensive study visits to Jawaharlal Nehru University in Delhi and was funded by the UK Arts and Humanities Research Council. Essa's research centers on comparative literature, postcolonial studies, and the intersection of literature with social justice issues. Her current Veni-funded project 'Intention & Intervention' examines authorial strategies against exclusionary discourses in Germany and Britain, with a particular focus on migration narratives and postmigrant authorship. She explores how contemporary literature navigates questions of national identity, memory, and spatial politics, particularly in the context of partitioned histories between India and Germany. Her scholarly output reveals a consistent engagement with political dimensions of literature, examining how authors intervene in exclusionary discourses through narrative strategies. Her recent work shows increasing focus on collaborative authorship, anthologies as resistance forms, and the role of translation in disrupting power dynamics. She has produced significant scholarship on postcolonial literature, particularly examining Indian and German literary responses to historical trauma. Women in German Studies Book Prize for first monographs in preparation (for 'Partitioned Nations, Shared Narratives') Veni grant by the Dutch Research Council (2022-2026) for 'Intention & Intervention' project Judge for the Kurt-Tucholsky-Preis for politically engaged writing Essa actively mentors prospective PhD students and has developed teaching focused on literatures of migration and diaspora in the BA Literary Studies program and contemporary publishing in the MA Literature Today program. Her public engagement includes regular contributions to platforms like Die Zeit, Berlin Review, and the Goethe Institute, where she discusses the political dimensions of literature. She has curated numerous public events through the Goethe Institute on topics ranging from anti-racist publishing strategies to migrant narratives in contemporary literature. Essa is deeply embedded in collaborative academic networks, serving on the Editorial Board for Forum for Modern Language Studies and the Advisory Board for FRAME Journal of Literary Studies. Her work with the #Vorzeichen project demonstrates her commitment to creating spaces for critical dialogue about literature's role in societal transformation.

Michalis Mountantonakisمشاهده پروفایل

پژوهشگر

• Large-Scale Semantic Data Integration
• Linked Open Data
• Semantic Web
• +۴ مورد دیگر

Michalis Mountantonakis is a Postdoctoral Researcher at FORTH and Laboratory Teaching Staff in the Department of Computer Science at the University of Crete, Greece. He holds a PhD (2020), MSc (2016), and BSc (2014) in Computer Science from the University of Crete, all with top grades. His research focuses on Large-Scale Semantic Data Integration, Linked Open Data, and Semantic Web technologies, with over 45 publications in top venues like ACM VLDB, ISWC, and ECML. He has been awarded the prestigious SWSA Distinguished Dissertation Award (2020) and the Maria Michael Manasaki Fellowship (2020). His work includes tools like LODsyndesis and LODChain, addressing challenges in knowledge graph connectivity and validation of AI-generated content. Education: PhD in Computer Science (2016-2020), University of Crete (Excellent GPA 9.74/10) MSc in Computer Science (2014-2016), University of Crete (Excellent GPA 9.87/10) BSc in Computer Science (2010-2014), University of Crete (2nd in class with GPA 8.42/10) Research Interests: His work bridges semantic web technologies with modern AI challenges, emphasizing large-scale data integration, knowledge graph applications, and validation frameworks. He has contributed to cultural heritage informatics, machine learning-augmented semantic systems, and cross-lingual NLP solutions. Recent trends include leveraging LLMs for query generation and semantic enrichment while ensuring factual accuracy through knowledge graph-driven validation. Key Achievements: Developed LODsyndesis, a global-scale semantic integration service Pioneered real-time validation of ChatGPT responses using RDF knowledge graphs Won Best Paper Award (ISWC 2022) for entity enrichment techniques Recipient of Stelios Orphanoudakis Undergraduate Fellowship (2013-2014) Participated in Roche Continents 2019 (top 100 European science students) Grants & Labs: His research has been supported by GSRT/HFRI. He collaborates with FORTH-ICS and leads projects in EU-funded initiatives like iMarine and BlueBridge. Current work focuses on governance models for ontologies, interoperable thesaurus creation (e.g., FoodEx2), and semantic analytics for cultural heritage datasets.

Prasad Tadepalliمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Prasad Tadepalli is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University, serving as the AI Graduate Program Director. He is affiliated with the Collaborative Robotics and Intelligent Systems Institute. His expertise spans artificial intelligence, machine learning, reinforcement learning, and automated planning, with impactful contributions to explainable AI and natural language processing. Tadepalli holds a Ph.D. from Rutgers University and M.Tech/B.Tech degrees from Indian institutions. He has authored over 100 papers, organized international conferences, and received awards such as the AAAI Outstanding Paper Award (2013) and ICAPS Best Student Paper (2009). Education: Ph.D. (Rutgers University, 1990), M.Tech (IIT Madras, 1981), B.Tech (Regional Engineering College, 1979) His research focuses on advancing AI through techniques like relational planning, reinforcement learning, and interpretable models. Recent work includes integrating planning and RL for multiagent systems and developing explainable models via tree ensemble compression. His articles highlight contributions to time-series imputation, adversarial attacks on bandits, and chess rating estimation using CNN-LSTM networks. Awards: AAAI Outstanding Paper Award (2013), ICAPS Best Student Paper (2009) Tadepalli emphasizes independent thinking in students and has advised numerous researchers. His work bridges theoretical AI with practical applications, such as robotics and data-driven decision-making.

• Natural Language Processing
• Computational Linguistics
• Multilingual Embeddings
• +۵ مورد دیگر

Simon Clematide is a Professor in the Department of Computational Linguistics within the Faculty of Arts and Social Sciences at the University of Zurich. With a publication record spanning over two decades from 2001 to 2025, he has established himself as a leading researcher in computational linguistics, particularly focusing on multilingual natural language processing, historical document analysis, and OCR applications. His research interests encompass Natural Language Processing, Computational Linguistics, Multilingual Embeddings, Historical Document Analysis, OCR Processing, and Text Normalization. Clematide's work demonstrates a strong emphasis on practical applications of NLP in digital humanities, with significant contributions to processing historical texts and multilingual resources. His recent work has increasingly incorporated large language models and transformer architectures for tasks ranging from text embedding evaluation to historical document processing. Clematide's publication trends show a consistent focus on multilingual NLP with particular expertise in Germanic languages and historical text processing. His work bridges theoretical linguistics with practical computational approaches, often addressing challenges in low-resource language scenarios and historical document digitization. Recent publications demonstrate increasing integration of LLMs into traditional NLP pipelines for tasks like paraphrase detection, job advertisement analysis, and historical text normalization. His collaborative work includes significant contributions to shared tasks and benchmarks in the NLP community, particularly through the HIPE (Historical Information Processing Evaluation) shared tasks. These efforts have helped establish standardized evaluation frameworks for named entity recognition in historical documents. Clematide has been actively involved in several major research projects related to historical newspaper content mining, including the impresso project which focused on cross-lingual historical document processing. His work often involves interdisciplinary collaboration between computer scientists, linguists, and historians.

• Semantics
• Computational Linguistics
• Natural Language Processing
• +۷ مورد دیگر

Dr. Denis Paperno is an Assistant Professor of Computational Linguistics at Utrecht University in the Department of Languages, Literature and Communication within the Humanities faculty. He maintains a dual affiliation, working at Utrecht University while on détachement from a researcher position at CNRS (the National Center of Scientific Research). His academic background includes a PhD in Linguistics from the University of California Los Angeles and prior work at the University of Trento in Marco Baroni's research group. Dr. Paperno's research expertise spans several critical domains in computational linguistics: Semantics and meaning representation Artificial Intelligence applications to language Distributional and neural semantic models Word embeddings and semantic compositionality Cross-modal and cross-lingual language understanding Language typology and lexical semantics His research program investigates fundamental questions about computational meaning representation, exploring how word meanings interact to form complex expressions and how to integrate diverse information sources like images, knowledge bases, and linguistic resources into unified frameworks. He has developed significant benchmarks for evaluating language models and made important contributions to semantic typology and quantification theory. Dr. Paperno's publication record demonstrates consistent contributions to top-tier computational linguistics venues, with recent work spanning theoretical linguistics to practical deep learning applications. His articles reveal expertise bridging formal semantic theory and cutting-edge neural network approaches to linguistic problems. He is actively involved in teaching courses including 'Formal and Natural Languages,' 'Machine Learning I,' 'Models for Language Processing,' and 'Semantics.' Current research projects include work with Pablo Mosteiro funded by an Applied Data Science research grant from Utrecht University, and he is scheduled to deliver a keynote at the 16th International Conference on Computational Semantics (IWCS) in Düsseldorf in September 2025.

• Machine-learning-aided Antenna/Circuit Design
• Data-Driven Education
• Learner Engagement Analytics
• +۳ مورد دیگر

Dr. Chao Shu is a Lecturer (Assistant Professor) in the School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL). He holds a PhD in Electronic Engineering from QMUL (2021) and has prior industry experience as an R&D software engineer and division manager. His research focuses on machine-learning-aided antenna/circuit design, data-driven education, and AI-enabled personalized learning. He has authored/co-authored over 20 journal/conference papers, a book chapter, and 13 patents. He currently co-leads the Data Analytics Scholarship working group in his school and teaches modules such as Data Design, Digital Circuit Design, and Control Theory. Research & Awards : Dr. Shu secured a £960,000 grant (2023–2025) for a wireless power transmission project. His work has earned a Best Conference Paper Award at IEEE TALE 2023. He reviews for journals like IEEE Transactions on Antennas and Propagation and serves on conference committees, including IEEE EDUCON 2023. Teaching : He organizes modules covering data analytics, digital circuits, communication systems, and microprocessor design. His courses emphasize practical skills in Python, embedded systems, and control theory.

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +۴ مورد دیگر

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.

Haim Dubossarskyمشاهده پروفایل

مدرس

• Natural Language Processing
• Artificial Intelligence
• Historical Linguistics
• +۳ مورد دیگر

Haim Dubossarsky is a Lecturer in Computer Science at the School of Electronic Engineering and Computer Science , Queen Mary University of London , and an Affiliated Lecturer at the Language Technology Lab , University of Cambridge. His work bridges Natural Language Processing (NLP) , Artificial Intelligence (AI) , and interdisciplinary studies in linguistics , cognitive neuroscience , and mathematical modeling . Dr. Dubossarsky's research explores semantic change and language variation through computational methods, focusing on: Detecting historical and contemporary linguistic shifts Integrating neurocognitive insights into NLP systems Developing interpretable AI models for language tasks Recent publications highlight his contributions to multilingual NLP , word-in-context (WiC) tasks , and robust semantic analysis . His work includes collaborations on hate speech detection , topological representations , and temporal modeling in language evolution. Dr. Dubossarsky leads projects funded by: UKRI AI Hub (£1,026,205, 2024–2029) RJ Riksbankens Jubileumsfond (£381,511, 2022–2027) Innovate UK (£320,887, 2024–2025) He supervises PhD students at Queen Mary's Centre for Human-Centred Computing , including Alaa Bazaid , Roksana Goworek , and Dina Pisarevskaya , working on topics like multilingual polysemy disambiguation and language variation analysis .

Sabine Schulte im Waldeمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Germanic Linguistics
• +۳ مورد دیگر

Sabine Schulte im Walde is an Apl. Prof. (Adjunct Professor) at the Institute for Natural Language Processing (IMS) , University of Stuttgart. Her research bridges computational linguistics and cognitive semantics, focusing on noun compounds, semantic change, and multimodal representations. Key research areas: semantic drift, compositionality, German particle verbs Instrumental in creating datasets for semantic change detection (DWUGs, DURel) Recent work explores BERT's limitations in compound semantics and cross-lingual metaphor detection She contributes to major European NLP initiatives, with publications in top venues like ACL. The IMS group specializes in language-technology intersections, managing 6 professorships, 50+ researchers, and 200+ students in NLP programs.

Måns Magnussonمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Probabilistic Machine Learning
• Statistical Inference from Textual Data
• +۴ مورد دیگر

Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health

Mark Stevensonمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +۸ مورد دیگر

Mark Stevenson is a Senior Lecturer in the School of Computer Science at the University of Sheffield, UK. He leads undergraduate programs and serves as a key member of the Natural Language Processing research group , focusing on knowledge extraction from text and user information access solutions. Research Interests : Natural Language Processing Information Retrieval Machine Learning Biomedical Text Disambiguation Lexical Semantics Exploratory Search Systems Scientific Awards : EPSRC Advanced Research Fellowship (2006-2011) Best Paper Award at CLEF 2004 Grants & Projects : He has secured significant funding including the EU FP7 PATHS project (£709,407), EPSRC grants for biomedical disambiguation (£239,920) and Lexical Adaptation (£30,000), and NIHR funding for public health research access systems.