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Doug OardView profile

Professor

• Information Retrieval
• Multilingual Information Access
• Speech Retrieval
• +3 more

Doug Oard is a Professor at the University of Maryland, College Park, holding joint appointments in the College of Information Studies and the Institute for Advanced Computer Studies (UMIACS). His research focuses on enhancing information retrieval across languages, speech, and sensitive content, with notable contributions to cross-language information retrieval (CLIR), eDiscovery, and evaluation design for information systems. He leads efforts in developing test collections for evaluating retrieval systems, including work on the TREC and CLEF evaluation forums. Key research areas include multilingual information access, speech retrieval, and computational social science. Oard has pioneered methods for evaluating machine translation, automated speech recognition, and the retrieval of historical or archival content. He has contributed to projects like the Apollo Mission Control Archive and the Multilingual TEDx Corpus, advancing accessibility to spoken and written materials across languages. His work emphasizes practical applications, such as improving legal eDiscovery processes and developing tools for searching sensitive or informal text. Oard has authored over 200 peer-reviewed publications and led major international evaluation initiatives, driving advancements in information retrieval systems' reliability and cross-cultural utility.

Razvan BunescuView profile

Associate Professor

• Machine Learning
• Natural Language Processing
• Music Information Retrieval
• +3 more

Razvan Bunescu is an Associate Professor in the Department of Computer Science at the University of North Carolina at Charlotte (UNCC), affiliated with the College of Computing and Informatics. His research focuses on machine learning, natural language processing, music information retrieval, and computational creativity. He holds a Ph.D. from the University of Texas at Austin and M.S./B.S. degrees from the University Politehnica of Bucharest. His work is funded by NIH (1R21EB022356) and NSF (1117489) grants, emphasizing diabetes management through wearable sensor data and blood glucose prediction models. Key research initiatives include the SmartHealth Lab , developing machine learning tools for healthcare, such as the OhioT1DM dataset for blood glucose prediction. He has co-chaired conferences like the Blood Glucose Level Prediction Challenge (BGLP) and served as an associate editor for IEEE Transactions on Computers . Notable awards include a Best Paper Award at the 2024 Figurative Language Processing Workshop. Education: Ph.D. Computer Science, University of Texas at Austin (2007) M.S. Computer Science, University Politehnica of Bucharest (1999) B.S. Computer Science, University Politehnica of Bucharest (1998) Professional Activities: Associate Editor, IEEE Transactions on Computers (2024–25) Program Co-Chair, International Summer School on Data Science (2018–24) Area Chair, ACL 2020 NLP Applications Track Awards: Best Paper Award at FigLang 2024. Grants: NIH R21EB022356: Adaptive Prediction of Blood Glucose Levels (2016–2020) NSF 1117489: Machine Learning for Blood Glucose Prediction (2011–2016) Advising: Supervised 20+ students, including PhD candidates Erfan Al-Hossami and Mika Chen. His lab’s work on blood glucose prediction has led to open-source tools like LSTM and ARIMA models, and datasets such as OhioT1DM. Current research explores creative AI, recommender systems, and hardware optimization.

• American Sign Language Education
• Deaf Education
• Sign Language Recognition
• +3 more

Kenneth DeHaan is an Associate Professor in the Language, Education, and Culture school at Gallaudet University, specializing in American Sign Language (ASL) education. He teaches advanced courses such as ASL-761 Seminar in Sign Language Education - Professional Preparation and ASL-790 Sign Language Teaching Internship . His research focuses on integrating technology into sign language education, including gesture recognition via wearables, radar-based interactive learning, and accessibility tools for deaf students. He has advised numerous graduate students in ASL education programs. Dr. DeHaan’s work bridges linguistics, computer science, and education, with publications on topics like the Computerized Revised Token Test for ASL assessment and SignQuery , a wearable-sensor-based search engine for sign languages. He has also contributed to initiatives like the Youth Enjoy Science (YES) Academy, promoting STEM access for underserved populations. His teaching spans both undergraduate and graduate levels, including courses on ASL methodology, practicums, and internship preparation. He actively collaborates with researchers in artificial intelligence, wearable technology, and deaf education to advance inclusive learning environments.

• Blockchain
• Cloud Computing
• Grid Computing
• +5 more

Dr. David Buján Carballal is a Lecturer and Researcher at the University of Deusto in Spain. He holds a BSc, MSc, and PhD in Computer Sciences from the University of Deusto, with a dissertation focusing on semantic technologies in Grid computing. His research spans Blockchain, Cloud/Grid computing, Linked Data, and Semantic Technologies. He teaches Web Engineering, Operating Systems, and Distributed Systems, and works at the Mobile Communications Research Group (MORElab) within DeustoTech Institute and Telefónica Deusto Chair. Education: BSc in Computing - Faculty of Engineering, University of Deusto MSc in e-Business - University of Deusto PhD in Computer Sciences - University of Deusto (2016) Research focuses on integrating semantic technologies with distributed systems, including blockchain applications and cybersecurity in connected industries. He actively participates in national projects (ALASTRIA network) and international conferences like HAIS, SOCO, and IoT. His work includes developing context-aware tourism platforms and remote laboratory systems (WebLab-Deployer). Advising & Grants: Involved in national/international research projects and private contracts. Served on program committees for conferences such as CAEPIA and JENUI. Extensive collaboration with institutions like the University of Manchester, UCM, and the University of Westminster. Labs/Teams: Lead researcher at MORElab, part of DeustoTech’s Mobile Communications group.

Chris DevelderView profile

Associate Professor

• Natural language processing
• Information extraction
• Smart grid
• +2 more

Chris Develder is an Associate Professor with the research group IDLab in the Department of Information Technology (INTEC) at Ghent University - imec, Ghent, Belgium. He received his MSc degree in computer science engineering and PhD in electrical engineering from Ghent University in Jul. 1999 and Dec. 2003 respectively (as a fellow of FWO). He has held research visitor positions at UC Davis, CA, USA (Jul.-Oct. 2007) and at Columbia University, NY, USA (Jan. 2013 - Jun. 2015). Prof. Develder leads two research teams within the Internet Technology and Data Science Lab (IDLab): one focused on converting text to knowledge (NLP, primarily information extraction using machine learning), and another on data analytics and machine learning for smart grids. His Text-to-Knowledge (T2K) research group has a strong track record in information extraction across various domains including news, human resources, and biomedical applications, as well as text classification tasks such as sentiment analysis. More recently, the group has expanded into conversational agents and generative models for educational applications. With his team, Prof. Develder has published over 200 papers in international journals and conferences including EMNLP, CoNLL, EACL, ACL, ECIR, CIKM, WSDM, WWW, and NIPS. His recent publications demonstrate a growing focus on practical applications of NLP in healthcare (personality style recognition from speech, biomedical adverse drug event extraction), education (question generation, gap-filling exercises), and labor market analysis (career path prediction, skill extraction). His work bridges theoretical advances in machine learning with real-world applications across multiple domains. Prof. Develder actively supervises PhD students and has co-supervised numerous successful doctorates, including Maarten De Raedt (2024), Semere Kiros Bitew (2024), Yiwei Jiang (2024), Amir Hadifar (2023), and Klim Zaporojets (2022). His former students have gone on to work at organizations including EarlyTracks, Zoom, Nokia Bell Labs, Clarivate, and various academic institutions.

Jakob PrangeView profile

Researcher

• Computational Linguistics
• Natural Language Processing
• Formal Semantics
• +12 more

Jakob Prange is a Researcher (Akademischer Rat auf Zeit) at the Chair for Natural Language Understanding / Digital Humanities within the Faculty of Applied Computer Science at the University of Augsburg. He holds a Ph.D. in Computer Science with a concentration in Cognitive Science from Georgetown University, where his dissertation focused on neuro-symbolic models for syntactic and semantic representations. Education: Ph.D. in Computer Science (Cognitive Science), Georgetown University B.Sc. in Computational Linguistics, Saarland University His research lies at the intersection of computational linguistics and theoretical linguistics, emphasizing formal and distributional semantics, meaning representation, deep learning, and neuro-symbolic integration. He prioritizes model efficiency and explainability, often combining neural models with structured linguistic representations. His recent work includes projects on cross-lingual QA in migration contexts and Bayesian modeling of L2 preposition learning. His publications span top venues such as ACL, NAACL, TACL, and COLING, with a focus on semantic parsing, supersense tagging, UCCA, neuro-symbolic modeling, and robust NLP for German. He has contributed to multilingual annotation frameworks and low-resource language modeling. Scientific Awards: No awards explicitly mentioned. He has supervised M.Sc. student Steffen Kleinle on a QA project and contributes to teaching at the University of Augsburg, including Python for language processing and NLP tutorials. He is part of the HLT@Augsburg research group led by Prof. Annemarie Friedrich, focusing on natural language understanding and digital humanities.

Heather Christine LentView profile

Researcher

• Natural Language Processing
• Machine Translation
• Low-resource Languages
• +3 more

Heather Christine Lent is a Researcher in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. Her work focuses on advancing natural language processing (NLP) for low-resource and marginalized languages, particularly Creoles, and addressing security challenges in multilingual systems. She leads the "Multilingual Modelling for Resource-Poor Languages" project (2022–2025), funded by Carlsbergfondet and Google. Her research integrates computational linguistics with ethical considerations, emphasizing equitable technology access. Notable contributions include the CreoleVal benchmark for multilingual evaluation and studies on NLP security in multilingual contexts. She collaborates internationally, including with institutions like George Mason University, and engages in community-driven dataset creation. Publications highlight themes like adversarial attacks on multilingual models, data quality audits, and cross-lingual transfer learning. She actively participates in workshops and tutorials aimed at fostering inclusive NLP practices, such as the "Connecting Ideas in Lower-Resource Scenarios" initiative.

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +3 more

Jaap Kamps is a Professor at the University of Amsterdam, Netherlands , with a focus on information retrieval , natural language processing , and machine learning . His work spans theoretical and applied research, including contributions to neural ranking models domain adaptation scientific text simplification exploratory search digital libraries for enhanced user access. Recent research highlights include revisiting bag-of-words representations for transformers, context embeddings for retrieval-augmented generation, and positional bias analysis in generative systems. He actively contributes to the CLEF SimpleText Track , promoting simplified scientific communication for diverse audiences. His collaborations involve co-authors like David Rau , Mostafa Dehghani , and Hosein Azarbonyad , with publications in venues such as ACM Transactions on Information Systems and ECIR . He mentors students and participates in conferences like ICTIR and SIGIR , driving advancements in efficient text ranking and user-centric search.

Josef RuppenhoferView profile

Researcher

• Natural Language Processing for Educational Applications
• Corpus Linguistics
• Frame Semantics
• +3 more

Josef Ruppenhofer is a computational linguist working as a Researcher at FernUniversität in Hagen, where he contributes to the CATALPA research cluster. Since March 2023, he has served as a Research Assistant at the research professorship for Computational Linguistics in the DAKODA project, which focuses on applying NLP methods to study second language acquisition. His work bridges theoretical linguistics with practical applications in language education technology. Dr. Ruppenhofer earned his PhD in Linguistics from the University of California at Berkeley in 2004, following MA degrees from UC Berkeley (1999) and the University of Colorado at Boulder (1997). His academic journey includes postdoctoral positions at prestigious institutions including the International Computer Science Institute in Berkeley, Saarland University, Hildesheim University, and the Leibniz-Institute for the German Language. Ruppenhofer's research centers on applying computational methods to linguistic phenomena, with particular expertise in frame semantics, construction grammar, and corpus linguistics. His work spans educational applications of NLP, sentiment analysis, and the challenging domain of offensive language detection. He has developed innovative approaches for analyzing learner language corpora and identifying implicit forms of abusive language that deviate from social norms. Analysis of his recent publications reveals a consistent focus on the intersection of theoretical linguistics and practical language technology applications. His work shows increasing specialization in analyzing implicit forms of abusive language and developing methods for operationalizing developmental stages in second language acquisition, particularly for German as a foreign language. The DAKODA project represents a major focus of his current research trajectory. Ruppenhofer actively contributes to the computational linguistics community through his work on FrameNet (celebrating its 25th anniversary in his 2024 publications) and the development of linguistic resources for analyzing user-generated content and parliamentary discourse. His methodological approach consistently combines theoretical linguistic insights with practical computational techniques. As a member of the CATALPA research cluster, Ruppenhofer collaborates with an interdisciplinary team working at the intersection of language education, theoretical linguistics, and computational methods. His current work in the DAKODA project involves building tools and methodologies for analyzing learner corpora to better understand developmental stages in second language acquisition.

David SchmidtView profile

Researcher

• Natural Language Processing
• Information Extraction
• Coreference Resolution
• +3 more

David Schmidt is a Researcher at the Chair of Computer Science VI (Artificial Intelligence and Knowledge Systems) within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. He has contributed to NLP research and teaching since joining the chair in April 2018 following his Master's completion. His educational background includes: Master of Science in Computer Science, University of Würzburg (2018) Schmidt specializes in Natural Language Processing with focused expertise in Information Extraction, Coreference Resolution, and Relation Extraction. His research applies these techniques to literary analysis, particularly generating character networks from German fairy tales and novels, bridging computational linguistics and digital humanities to automate structural analysis of narrative texts. His publication on semi-automatic coreference resolution highlights a research trajectory emphasizing human-AI collaboration, where user-specified knowledge enhances system accuracy. This work reflects broader trends in adaptable NLP tools for specialized domains like literary scholarship. As part of the Center for Artificial Intelligence and Data Science (CAIDAS) and the Chair of Artificial Intelligence and Knowledge Systems, Schmidt contributes to open-source tool development including ATHEN and CaseTrain, supporting information extraction and knowledge management initiatives across university projects.

Ido DaganView profile

Professor

• Natural Language Processing
• Textual Entailment
• Semantic Representation
• +5 more

Ido Dagan is a Professor at the Department of Computer Science at Bar-Ilan University , Israel, and founder of the Natural Language Processing (NLP) Lab . He is a Fellow of the Association for Computational Linguistics and served as ACL President (2010) and Executive Committee member (2008–2011), leading the establishment of Transactions of the Association for Computational Linguistics . Dagan’s research focuses on applied semantic processing , including textual entailment , natural semantic representation , multi-text information consolidation , and interactive text summarization . His recent work addresses attributable text generation , summary-source alignment , and cross-sentence argument detection , with applications to fact verification and hallucination detection. Key trends in his publications include cross-document coreference resolution , question-answering systems , semantic parsing , and interactive summarization . Notable collaborative projects involve UI trajectory analysis and long-context QA with Arman Cohan and Jacob Goldberger. Scientific Awards Fellow, Association for Computational Linguistics (ACL) President, ACL (2010) Executive Committee, ACL (2008–2011) Advising & Collaborations Dagan has supervised numerous PhD, MSc, and postdoctoral students since 1997, including Shachar Mirkin and Shmuel Amar . He collaborates with researchers like Ori Ernst , Avi Caciularu , and Aviv Slobodkin , with grants from institutions like IBM Haifa Scientific Center and AT&T Bell Laboratories .

• Digital Transformation
• Smart Communities
• Blockchain Technology
• +7 more

David Buján Carballal is a Lecturer and Researcher at the Faculty of Engineering, University of Deusto, and a member of the MORElab 'Envisioning Future Internet' Research Group. He is affiliated with DeustoTech (Deusto Institute of Technology) and the Telefónica Deusto Chair. His academic career spans software engineering, semantic web technologies, and digital transformation. BSc in Computing, Faculty of Engineering, University of Deusto MSc in e-Business, University of Deusto PhD in Computer Sciences, University of Deusto Dr. Buján's research focuses on Digital Transformation for smart communities and rural areas, Blockchain Technology applications in traceability, and Cybersecurity solutions for connected industries. His work addresses Grid Computing , Semantic Web , and Context Modelling for tourism applications. Recent publications highlight his contributions to digital ecosystems for rural innovation, blockchain-based energy traceability, and cybersecurity frameworks for connected industries. His projects include EU-funded AURORAL , HAZITEK BLOCKCHAINFOOD , and collaborations with Accenture, Telefónica, and BBK. Scientific Contributions: Member of Deustek Research Group Program Committee member for CAEPIA workshops Article reviewer for IoT, Ubicomp, and Semantic Web conferences Dr. Buján has participated in numerous national and international conferences on semantic technologies, grid computing, and digital ecosystems. He contributes to academic governance through curriculum development and teaching innovation initiatives at the Faculty of Engineering.

Zheng ZhangView profile

Professor

• Quantum Computing
• Compiler Design
• Parallel Computing
• +3 more

Zheng Zhang is a Professor in the Department of Computer Science at Rutgers University, School of Arts and Sciences. He holds academic expertise in compiler design, quantum computing, and parallel computing. Previously, he earned a Ph.D. in Computer Science from the College of William and Mary, an M.S. in Computational Operations Research from the same institution, and a B.S. in Electronic Engineering from Shanghai Jiaotong University. His research focuses on advancing compilation techniques for emerging architectures, including quantum computers, GPUs, and multi-core processors. Key projects include optimizing quantum circuit compilation for NISQ devices and enhancing qubit reuse through dynamic circuits. He has received NSF grants and contributed to the ISCA Distinguished Artifact Award (2024). Teaching: Currently instructs CS314 (Principles of Programming Languages). Research groups include Computer and Networked Systems and Programming Languages & Compilers. Recent activities include publications on VQE compilation and leadership in ASPLOS, PLDI, and MICRO conferences. Grants: NSF Grant, NSF XPS Grant Labs/Teams: Quantum Compilation Research Group Notable Achievement: Erdős Number 2

Estrid HeView profile

Senior Lecturer

• Natural Language Processing
• Data Mining
• Deep Learning
• +3 more

Dr. Estrid He is a Senior Lecturer at RMIT University's School of Computing Technologies. She obtained her PhD from the University of Melbourne in 2020, where she subsequently served as a postdoctoral research fellow. Her research bridges natural language processing, data mining, and deep learning optimization, with applications spanning healthcare, communications, and algorithmic fairness. Research Focus: Core NLP techniques for knowledge extraction from complex texts (patents, medical records) Enhancing security/efficiency of deep learning models in resource-constrained environments Multimodal learning integrating text, sensor data, and biomedical signals Fairness-aware AI systems for computer vision and graph neural networks Her publication portfolio demonstrates strong cross-disciplinary collaboration, with recent work in: Wireless communications (terahertz signal processing, 6G hardware) Biomedical applications (brain disorder prediction, clinical NLP) Generative models for sensor data and multimodal content Algorithmic fairness in computer vision and graph networks She actively supervises graduate research, with current projects including: Graph learning for brain disorder prediction Multimodal health data mining Privacy-preserving AI for urban sensing Integration of spiking neural networks with LLMs

Dag Trygve Truslew HaugView profile

Professor

• Syntax
• Formal Semantics
• Computational Linguistics
• +5 more

Dag Trygve Truslew Haug is a Professor at the Department of Classics at the University of Oslo, specializing in Greek and Latin linguistics with a focus on syntax and computational approaches. His research bridges classical philology with modern computational linguistics, particularly through treebank development and semantic analysis. His primary research interests include: Greek and Latin grammatical structures Lexical-Functional Grammar and formal semantics Computational linguistics and treebank development Historical linguistics and syntactic change Semantic annotation frameworks Morphological tagging systems His recent publications demonstrate a strong focus on computational linguistics applications for classical languages, semantic parsing methodologies, and the development of linguistic resources for Norwegian. The work shows consistent innovation in formal semantics, corpus linguistics, and interdisciplinary applications in computational biology. He leads the DAMOS project (Database of Mycenaean at Oslo) and participates in multiple research groups including Digital Humanities, Grammar and Meaning, Language Change, Linguistics and Poetics, Pragmatics: Theory & Experiments, and Super Linguistics.