جست‌وجوی استاد و پژوهشگر

• Natural Language Processing (NLP)
• Machine Learning
• Knowledge Representation
• +۳ مورد دیگر

Bhuwan Dhingra is an Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences. He focuses on Natural Language Processing (NLP) , machine learning , and knowledge representation , with specific interests in question answering , robustness to adversarial inputs , and model calibration . 2020 : Ph.D. in Language Technologies from Carnegie Mellon University 2013 : Master's Thesis on Local Quadrature Reconstruction on Smooth Manifolds at IIT Kanpur His research includes temporal language models , adversarial robustness , and combating misinformation . He leads the ALTER-Math NSF-funded project (2024-2027) and collaborates on CC* Integration-Large (2025-2027) for distributed GPU systems. His 2025 work on adversarial perturbations and 2024 studies on table understanding in materials science highlight his focus on LLM reliability and structured data integration . Notable awards include the Amazon Research Award (2022), NSF Medium Grant (2022), and Google Research Gift (2021). He advises PhD students like Rich Stureborg and undergraduates such as Angikar Ghosal (now at Stanford PhD). Teaching graduate courses like Introduction to NLP and Advanced NLP , he emphasizes long-form QA and collaborative writing in NLP.

Kathleen McKeownمشاهده پروفایل

استاد

• Natural Language Processing
• Text Summarization
• Natural Language Generation
• +۷ مورد دیگر

Kathleen McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University , where she has been a faculty member since 1982. She served as Founding Director of the Data Science Institute (2012–2017), Department Chair (1998–2003), and Vice Dean for Research (2003–2005) at the School of Engineering and Applied Science . Education : PhD in Computer Science and Information Science (1982) and MS in Computer and Information Science (1979) from the University of Pennsylvania , AB in Comparative Literature (1976) from Brown University Her research focuses on Natural Language Processing , Text Summarization , and Natural Language Generation , with applications in Social Media Analysis , Disaster Response , and Energy Behavior Messaging . She has pioneered neural methods for summarization, sentiment analysis in low-resource languages, and multi-media explanations. Her work includes 15 recent publications (2011–2013) on topics like discourse relation disambiguation , MT error correction , influencer detection , and hierarchical web summarization , reflecting trends in deep learning , cross-lingual NLP , and social media analytics . Honors & Awards : IEEE Innovations in Societal Infrastructure Award (2023) AAAI Fellow (1994) ACM Fellow (2003) Founding ACL Fellow (2012) Anita Borg Women of Vision Award (2010) NSF Presidential Young Investigator (1985) She leads the NLP Group at Columbia, focusing on real-time disaster updates , behavioral NLP , and sentiment analysis for marginalized communities. Her career spans leadership roles , grant-funded research , and foundational contributions to text generation and summarization .

Johanna Geißمشاهده پروفایل

پژوهشگر

• Event Detection
• Information Extraction
• Information Retrieval
• +۵ مورد دیگر

Johanna Geiß is a postdoctoral researcher at the Institute of Computer Science , Heidelberg University, specializing in Natural Language Processing , Information Extraction , and Event Detection . She contributes to projects like SCIDATOS (sepsis diagnosis) and EventAE (event-based Linked Data exploration). Education: PhD from University of Cambridge (2011), Magister Artium in Computational Linguistics (Heidelberg, 2006) Her research integrates Geoparsing , Social Network Analysis , and Graph Theory for tasks such as toponym disambiguation and entity resolution. Recent work includes frameworks like HeidelPlace and tools for semantic word clouds. Key awards include the 2010 Lundgren Research Award and multiple grants from the EPSRC and Cambridge European Trust. She has taught courses on Information Networks and Data Mining at Heidelberg University. As a postdoc , she advises on scientific computing applications in healthcare and contributes to open-source tools like NECKAr and EventAE .

Thomas Bonaldمشاهده پروفایل

استاد

• Graph Analysis
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Thomas Bonald is a Professor at Télécom Paris, Institut Polytechnique de Paris, where he heads the Data, Intelligence and Graphs (DIG) team at the Information Processing and Communication Laboratory (LTCI). His academic career spans several decades with significant contributions to the fields of graph analysis, machine learning, and network systems. Professor Bonald's research interests focus on data analysis and machine learning, particularly for large graphs, text data, and time series. He has made substantial contributions to knowledge base construction, with involvement in the YAGO project, and has developed the scikit-network Python library for graph analysis. His work bridges theoretical foundations with practical applications across various domains including natural language processing, network analysis, and knowledge representation. His recent publication trends reveal a strong emphasis on graph-based machine learning techniques, knowledge graph refinement, and hierarchical classification systems. Bonald's research shows increasing integration of large language models with structured knowledge bases, as evidenced by his work on Wikidata taxonomy refinement and YAGO development. His publications span top-tier venues in machine learning, information retrieval, and network systems. Professor Bonald has mentored numerous PhD students throughout his career, including both current students (Roman Plaud, Yiwen Peng, Rajaa El Hamdani) and former students who have gone on to receive notable awards such as the Telecom Paris thesis award (Céline Comte) and the Gilles Kahn award (Mathieu Feuillet). He has been instrumental in developing educational resources in probability, statistics, graph learning, and reinforcement learning. He leads the DIG research team at LTCI, which focuses on developing advanced methods for analyzing complex graph structures and creating practical tools like scikit-network. His work demonstrates a consistent trajectory from theoretical network analysis to practical implementations in knowledge representation and machine learning systems.

• Network Analysis
• Social Networks
• Data Mining
• +۶ مورد دیگر

Michele Coscia is an Associate Professor in the Department of Data Science at the IT University of Copenhagen. He also serves as the Head of Programme for the BSc in Data Science. His research focuses on network analysis, social networks, data mining, and their applications in understanding human mobility, complex systems, economic development, and memetics. He has contributed to projects such as the Pioneer Centre for Artificial Intelligence and leads initiatives like ROMNET (Past social network reconstruction from material culture data) and Work2Vec (exploring deep learning in healthcare and work hours analysis). His research interests encompass interdisciplinary network science, including misinformation dynamics, ideological polarization, and the impact of social media systems. He has developed methodologies like the generalized Euclidean measure for multilayer networks and explored cultural data analytics through case studies like Italian music history. His work also addresses real-world challenges, such as quantifying the effects of violence on migration patterns and optimizing network sampling strategies for cost efficiency. Michele Coscia has received the Årets Forskningsmiljø 2022 award, recognizing his contributions to fostering outstanding research environments. He actively engages with media, discussing topics like misinformation and data visualization. As a principal investigator, he oversees projects funded by institutions such as the Villum Foundation and Danish National Research Foundation. His research spans academic collaborations across countries, reflecting his global network and commitment to advancing network theory and its applications.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web
• +۵ مورد دیگر

Benno Kruit is a Visiting Professor at the Department of Artificial Intelligence, Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on knowledge representation, semantic web technologies, and AI applications in data interpretation and system dynamics. He has contributed to projects involving knowledge graphs, ontology engineering, and question answering systems. Key areas include semantic interpretation of tables, fault diagnosis in cyber-physical systems, and scalable simulation models. His work spans interdisciplinary topics such as environmental data analysis, agricultural informatics, and multilingual entity disambiguation. Kruit has co-developed platforms like TAKCO and Tab2Know for knowledge extraction from tables. He teaches the course Knowledge and Data and has contributed datasets like Tab2Know evaluation data to the research community. Recent research highlights include exploring open-source RAG systems, ontology-based simulation frameworks, and constrained LLM approaches for official statistics querying. His publications reflect a strong emphasis on practical applications of AI in knowledge integration and domain-specific problem-solving.

• Natural Language Processing
• Semantic Relations
• Knowledge Graphs
• +۳ مورد دیگر

Vivi Nastase is a senior researcher affiliated with Fondazione Bruno Kessler and the Heidelberg Institute for Theoretical Studies . Her work focuses on Natural Language Processing , Knowledge Graphs , and Machine Learning , with particular emphasis on multilingual semantic analysis and linguistic structure extraction. Key research areas: Semantic relations between nominals Transformer-based sentence embeddings Cross-lingual knowledge alignment Linguistic generalization in neural models Knowledge graph construction from Wikipedia Recent publications analyze syntactic information in sentence embeddings and develop structured synthetic datasets like Blackbird Language Matrices for Italian/French benchmarks. She contributes to advancing FAIR digital objects and scholarly knowledge graph integration.

• Cognitive Science
• Information Seeking Behavior
• Genre Analysis
• +۵ مورد دیگر

Malcolm John Clark is a Lecturer in Data Analytics at the University of the Highlands and Islands (UHI), based in Inverness. He serves as Deputy Head of Curriculum at UHI Moray and Programme Leader for the BSc Computing programme. His academic background includes a PhD in Cognitive Science from Robert Gordon University (2014), an MSc in Classifying XML Documents by Genre (2005), and a BSc in Interactive Media (2004). His research focuses on information seeking behavior, genre analysis, human computer interaction, and machine learning applications. He has led numerous interdisciplinary projects funded by IDEAs Fund, SFC Innovation Vouchers, and UIF Funds, including initiatives on mental health through wild swimming, virtual consultation platforms for rural communities, and neurodiversity support systems. Clark also explores AI-driven domain knowledge structuring and psychometrics in communication. He has published extensively on topics like genre recognition, eye-tracking studies, and semantic web technologies. Currently accepting PhD students, his work aligns with UN Sustainable Development Goals related to education and innovation.

Go Sugimotoمشاهده پروفایل

پژوهشگر ارشد

• Linked Data
• Entity Linking
• Data Integration
• +۴ مورد دیگر

Go Sugimoto holds multiple Visiting Fellow positions at Vrije Universiteit Amsterdam across key research units: Faculty of Science Business Web and Media Network Institute Intelligent Information Systems His research bridges Computer Science and Digital Humanities with emphases on: Quality assessment of Linked Data infrastructure Entity Linking for cultural heritage applications Data Integration challenges in historical research NLP-driven RDF generation from biographical texts FAIR principles implementation barriers Publication analysis reveals consistent focus on empirical evaluation of semantic web tools for humanities contexts, particularly examining data connectivity gaps in cultural heritage systems. His work demonstrates methodical investigation of real-world tool limitations while advancing practical integration frameworks for heterogeneous historical datasets. No major scientific awards are documented in available records. Information regarding student advising or research grant management is not disclosed in current profiles. Sugimoto operates within VU Amsterdam's Network Institute and Intelligent Information Systems ecosystem, leveraging interdisciplinary collaboration between computer science and humanities scholars to address data infrastructure challenges in cultural heritage domains.

• Complexity Science
• Network Science
• Computational Social Science
• +۷ مورد دیگر

Sune Lehmann Jørgensen is a Professor of Complexity and Network Science at the Technical University of Denmark and an Adjunct Professor in the Department of Sociology at the University of Copenhagen, affiliated with the Copenhagen Center for Social Data Science (SODAS). His interdisciplinary work bridges physics, computer science, and sociology, focusing on understanding human behavior through large-scale digital data. His research interests include: Complexity and Network Science Computational Social Science Statistical Physics of Human Systems Temporal and Mobile Network Analysis Machine Learning Applications in Social Data Digital Footprints and Behavioral Modeling The analysis of his recent publications reveals a consistent focus on extracting social patterns from digital traces—such as smartphone usage, vaccination decisions, and life-event sequences—using advanced computational and network-based methods. His work often involves nation-scale datasets and aims to uncover universal principles of human dynamics. Notable scientific contributions include studies on behavioral changes after terror attacks, methodological reflections on social media research, and predictive models of life trajectories using sequence data. His work has been widely disseminated and discussed across academic and public platforms. He has received significant attention in the media and scholarly networks, with his research picked up by over 268 news outlets and referenced in Wikipedia and social media platforms. Although no formal students are listed in the provided text, his professorial roles imply active mentorship and supervision in research projects. Sune Lehmann leads or contributes to collaborative research initiatives centered on social data science, particularly through SODAS and DTU. His ongoing work emphasizes ethical, methodological, and technical innovation in analyzing human-generated data at scale.

• Natural Language Processing
• Clinical Informatics
• Machine Learning
• +۳ مورد دیگر

Alexander Loeser is a researcher active in natural language processing (NLP) and its applications in clinical and financial domains. He has contributed to diverse areas including clinical outcome prediction, transformer-based reinforcement learning environments, domain knowledge integration, and information extraction from text. His recent work focuses on evaluating large language models' financial literacy via domain-specific languages and addressing data drift in clinical NLP tasks. Key Research Areas: Clinical decision support systems and outcome prediction Domain knowledge injection into transformer models Biased news article detection Interactive NLP systems for entity linking Methodological Focus: Reinforcement learning and attention mechanisms Multi-task and self-supervised learning Active sampling for annotation efficiency Topic segmentation and classification Loeser has collaborated extensively with researchers like Wolfgang Nejdl, Betty van Aken, Felix Gers, and Paul Grundmann, with publications spanning from 2012 to 2025. His work emphasizes interpretability, generalization, and practical deployment of NLP models in real-world domains.

Toomas Hinnosaarمشاهده پروفایل

دانشیار

• Economics
• Game Theory
• Industrial Organization
• +۴ مورد دیگر

Toomas Hinnosaar is an Associate Professor in Economics at the University of Nottingham, holding a PhD from Northwestern University. He specializes in game theory, mechanism design, and behavioral economics, with a focus on strategic interactions, market structures, and digital public goods. His research explores sequential contests, network pricing, and the impact of online information on real-world outcomes. Key findings include the role of transparency in strategic environments and the economic implications of social media dynamics. His work has been featured in major outlets like the Guardian , BBC Radio , and VoxEU . Scientific recognition includes selection for the Journal of the European Economic Association 's Virtual Issue: Editor's Choice Collection (2019). His methodological contributions span randomized field experiments, auction theory, and dynamic pricing models.

Niranjan Balasubramanianمشاهده پروفایل

استادیار

• Natural Language Processing
• Information Retrieval
• Question Answering
• +۳ مورد دیگر

Niranjan Balasubramanian is an Assistant Professor in the Department of Computer Science at Stony Brook University with additional affiliations in the Department of Biomedical Informatics and the Center of Excellence in Wireless & Information Technology (CEWIT). His research focuses on Natural Language Processing and Information Retrieval systems that extract, understand, and reason over textual information. Education: PhD, University of Massachusetts Amherst (Center for Intelligent Information Retrieval) MS, Computer Science, University at Buffalo (2003) His research spans question answering for elementary education, event schema generation from news, machine learning for information retrieval, energy-efficient mobile search, and automatic Wikipedia content generation. Recent work explores causal reasoning in event extraction, multimodal claim verification, authorship fairness, and secure coding with large language models. Analysis of his 15 most recent publications (2024-2025) reveals intensive focus on advancing NLP through causal/temporal reasoning, multimodal verification, and LLM optimization. Key trends include psychological modeling of human language, energy-efficient architectures, and addressing misattribution in authorship analysis. Scientific Awards: No awards mentioned in provided text Advising and grants information was not provided in the source materials. His postdoctoral background at the University of Washington's Turing Center and industry experience at Syracuse University's Center for Natural Language Processing inform his applied research approach. Labs and Teams: Affiliated with Stony Brook's Center of Excellence in Wireless & Information Technology (CEWIT), contributing to interdisciplinary AI initiatives while maintaining primary focus in the Computer Science department.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۲ مورد دیگر

Daisuke Kawahara is a Professor at Waseda University's Faculty of Science and Engineering and a Visiting Professor at the National Institute of Informatics. He holds a PhD in Informatics from Kyoto University (2005) and has previously served as Associate Professor at Kyoto University and Senior Researcher at NICT. His research spans natural language processing, computational linguistics, and AI infrastructure. Education: Ph.D. in Informatics, Kyoto University (2005) Graduate Studies in Intelligent Informatics, Kyoto University (1999–2002) M.Eng. in Electronic & Communication Engineering, Kyoto University (1997–1999) B.Eng. in Electrical Engineering, Kyoto University (1993–1997) Research Focus: Kawahara specializes in NLP, including syntactic parsing, semantic role labeling, language resource development (e.g., JGLUE benchmark), and multilingual corpus construction. His work integrates machine learning with linguistic theory to improve text understanding systems, error correction tools, and dialogue agents. Publication Trends: His recent articles emphasize Japanese and Chinese NLP, neural network-based parsing, and practical applications like educational tools and pandemic information systems. Common themes include benchmarking, corpus annotation, and cross-lingual adaptation. Awards: 情報処理学会 自然言語処理研究会 優秀研究賞 (2025) 言語処理学会最優秀論文賞 (2024, 2023) 科学技術分野の文部科学大臣表彰 (2017) Multiple Best Paper Awards from NLP conferences (2000–2025) Projects & Advising: He leads JSPS-funded projects like Building General Language Understanding Infrastructure (2021–2025) and Acquisition of Knowledge Frames (2018–2021). No student advisees are listed. Labs & Teams: Collaborates with RIKEN Center for Advanced Intelligence Project and maintains ties to Kyoto University's NLP lab. Focuses on large-scale language modeling and collaborative AI-human intelligence frameworks.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire, where she leads the TREMA lab. Her academic journey began with a PhD from the Max Planck Institute for Informatics in Saarbruecken, Germany (2011), followed by postdoctoral positions at the University of Massachusetts Amherst (2010-2015) and University of Mannheim (2015-2016). Her educational background includes PhD studies at both the Max Planck Institute for Informatics (2007-2011) under Prof. Gerhard Weikum and Prof. Tobias Scheffer, and earlier research at Humboldt University in Berlin. She has built a distinguished career bridging theoretical computer science with practical applications in information retrieval and machine learning. Dietz's research primarily focuses on the intersection of information retrieval, natural language processing, and knowledge graphs, with a parallel research initiative in watershed data science. She is particularly known for her work on entity-aspect linking, complex answer retrieval, and the vision of automatic Wikipedia construction. Her approach integrates fine-grained knowledge annotations with text understanding to create comprehensive information systems that go beyond traditional 10-blue-links search paradigms. In watershed data science, she applies similar machine learning techniques to environmental data streams, focusing on solute transport analysis during storm events. Her recent publications reveal a strong trend toward fine-grained semantic understanding, particularly in entity-oriented search tasks. She has pioneered methods for entity-aspect linking that significantly improve retrieval accuracy by capturing different contexts in which entities appear. Her work increasingly integrates knowledge graphs with neural architectures, showing sophisticated understanding of how to leverage both structured and unstructured information for better search experiences. Best paper award at JCDL 2018 for work on entity-aspect linking NSF CAREER Award (2019-2023) for "Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval" OSSI Award 2013 from UMass ICB3 for open-source hardware/software Dietz actively mentors PhD and Masters students through the TREMA lab, with current research focusing on entity ranking, topic extraction, conversational search, and watershed forecasting. Her grant portfolio includes the NSF CAREER award and funding from the Northeast Big Data Innovation Hub for forecasting salinity in rivers during storm events. She has also coordinated the TREC Complex Answer Retrieval track (2017-2019), creating important benchmarks for the IR community. The TREMA lab (Text Retrieval, Entity Modeling, and Applications) serves as the hub for Dietz's research activities, bringing together students and collaborators to work on cutting-edge problems in information access. The lab's work spans both theoretical contributions to information retrieval and practical applications in domains ranging from environmental science to scientific publication analysis.