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.
Gianluca Lebani is a Researcher at Ca' Foscari University of Venice, affiliated with the Department of Comparative Linguistic and Cultural Studies and the Research Institute for Digital and Cultural Heritage. His roles include membership in the Management Committee of the University Scientific Instrumentation Service Center (CSA), the Board of Directors of the Bembolab Laboratory, and the Academic Senate. His research focuses on theoretical and comparative linguistics, dialectology, and computational linguistics. Key areas include variation in Italian and Italo-Romance dialects, semantic role analysis using transformer models, psycholinguistic investigations of grammatical gender, and historical document analysis. He also explores machine learning applications in language processing, such as baby language models (BAMBI) and Old Saxon poetry scansion algorithms. Recent work includes studies on possessive pronouns in dialects, indefiniteness in bilingual speakers, and the expression of semantic gender. His contributions span corpus-based methods, distributional semantics, and experimental psycholinguistics. Collaborations involve projects like VariOpInTA (Variation and Optionality in Italo-Romance) and computational frameworks like SYMPAThy for extracting word combinations. Lebani is active in academic service, contributing to institutional committees and editorial roles. His lab affiliations include Bembolab and the Digital and Cultural Heritage Institute. He regularly publishes in journals like RID, RIVISTA ITALIANA DI DIALETTOLOGIA and ISOGLOSS , and presents at conferences such as CLiC-it and QITL.
Raúl Aranovich is a Professor in the Department of Linguistics at the University of California, Davis, within the College of Letters and Science. He has been a faculty member at UC Davis since 2001 and previously held academic positions at Ohio State University and the University of Texas at San Antonio. He earned his Ph.D. in Linguistics from UC San Diego in 1996 under the supervision of Professors S.-Y. Kuroda and John Moore. His research centers on theoretical linguistics, particularly the interfaces between syntax, morphology, and semantics, with a focus on grammatical mismatches across these domains. He specializes in Romance languages—especially Spanish—and lesser-studied languages such as Fijian and Shona. His work increasingly integrates computational methods, including corpus linguistics, natural language processing, and knowledge representation via ontologies. He has also contributed to interdisciplinary research in cybersecurity communication and neural machine translation. The recent publications of Raúl Aranovich span a wide range of subfields, from historical linguistics and construction grammar to computational modeling and cross-linguistic typology. A strong trend in his work is the use of formal and empirical methods to analyze syntactic variation, auxiliary selection, impersonal constructions, and morphological complexity, often with a comparative approach across languages. His interdisciplinary collaborations, especially with computer scientists, reflect a growing interest in applying linguistic theory to real-world digital communication and security challenges. Fellow, Linguistic Society of America Raúl Aranovich has mentored numerous students and collaborators, though specific advisees are not listed in the provided text. He has secured research funding through collaborative projects, particularly in computational linguistics and cybersecurity, as evidenced by co-authored publications with researchers in computer science. His work on low-resource machine translation and semantic web applications suggests active grant-supported research. He is involved in modeling grammars using OWL ontologies and has explored language use in digital communities such as GitHub and cybersecurity forums. His research group likely includes students and postdocs working at the intersection of linguistics and computer science, particularly in NLP and knowledge representation.
Wei Zhao is an Assistant Professor in Natural Language Processing at the School of Natural and Computing Sciences, University of Aberdeen, where he also leads research as a Principal Investigator. He is additionally an invited lecturer at Heidelberg University, Germany. His research focuses on evaluation of NLP systems, cross-temporal research, and applications of large language models in science and humanities. His research interests include: Evaluation of Large Language Models Diachronic and Cross-Temporal NLP AI for Scientific Discovery Lexicography and Lexical Semantics Machine Translation and Human-AI Collaboration Graph-based Representation Learning The recent articles highlight a strong trend in leveraging LLMs for scientific and linguistic tasks, particularly in evaluation, cross-temporal modeling, and human-AI collaboration. His work spans both theoretical foundations (e.g., graph embedding) and practical applications in translation, lexicography, and scientific content generation. Scientific awards and recognitions include: Outstanding Paper Award at NAACL Computing Grant from Google Sponsorship from Artificial Intelligence Journal Research Catalyst Funding from University of Aberdeen Computing Credits from OpenAI Research Collaboration Grant from Royal Society of Edinburgh Wei Zhao actively supervises multiple Master's and Bachelor's students, including Qianchen Luo, Gagan Bhatia, Ran Zhang, and Lydia Körber, among others. He is also co-supervising a PhD student with Steffen Eger. He has served as an Area Chair for ARR, COLING, and CoNLL 2025, and as a reviewer for EPSRC, Leverhulme Trust, and ISPF. His research is funded by Google, OpenAI, Royal Society of Edinburgh, and the Artificial Intelligence Journal. He leads a vibrant research group focusing on LLMs, cross-temporal bias, evaluation, and NLP for science, with ongoing projects in literary translation, lexicography, and multimodal scientific content.
Hongyu Zhang is a Lecturer in the Department of Earth, Geographic, and Climate Sciences at the University of Massachusetts Amherst, where he contributes to the Geographic Information Science and Technology (GIST) program. He is based at the Mount Ida Campus and is actively engaged in research and teaching at the intersection of geography, technology, and ethics. Education: PhD in Geography, McGill University, 2024 MSc in Geography, Western University, 2017 Bachelor of Environmental Studies (BES) in Geomatics, University of Waterloo, 2015 (with minor in Computer Science and Diploma of Excellence in GIS) Hongyu's research focuses on geoprivacy , GeoAI , and the ethical dimensions of spatial data . He investigates how individuals disclose location information on social media, particularly in Chinese digital environments like Weibo, using mixed methods to understand the sociotechnical dynamics of privacy. His work aims to promote responsible spatial data science by bridging GIScience with human behavior and digital ethics. His recent publications and open-source contributions reflect a strong trend in social media analysis , geoprivacy discourse , and computational ethics . He develops tools and datasets to analyze privacy-related language and behavior online, with a focus on Chinese platforms. His work also extends to GIS education , where he emphasizes project-based learning to improve student employability in geospatial fields. Scientific Contributions: Development of lexicons for analyzing geoprivacy in Chinese social media Creation of datasets on microblog content and user comments Open educational resources in data science and GIS Research on algorithmic price discrimination and digital surveillance Hongyu is committed to open science and student mentorship. While no formal list of advisees is available, his teaching and project-based approach suggest active engagement with students. He has no listed scientific awards, but his research output and GitHub activity indicate a growing scholarly presence in geospatial ethics and digital society. He maintains an active research website and GitHub profile, where he shares code, datasets, and educational materials, reflecting a transparent and collaborative research philosophy.
Professeur agrégé at the Department of Linguistics and Translation, Faculty of Arts and Sciences, Université de Montréal. Specializes in computational lexicology, multilingual text generation, and semantic-syntax interfaces. Serves as Director of the Observatoire de linguistique Sens-Texte (OLST). Holds a Ph.D. from Université de Montréal and Université Paris, with postdoctoral research at Macquarie University (Sydney), Universitat Pompeu Fabra (Barcelona), and Universität Stuttgart (Germany). Education: Ph.D. in Linguistics, Université de Montréal and Université Paris, 2008 Research Interests: Lexical semantics and morphology Automatic text generation Collocation processing Meaning-Text Theory applications Polarized Unification Grammar Key Grants/Projects (selected): Le lexique entre humains et machines , FRQSC grant (2025-2030), leading a multidisciplinary team Automatic generation of lexicographic definitions , SSHRC grant (2018-2023) Twitter-MPhon , SSHRC grant (2021-2025) analyzing morphophonological variation via social media Advising: Supervised over 20 graduate students since 2013, focusing on multilingual generation, collocation modeling, and computational lexicography. Labs/Teams: Directs OLST and collaborates with international networks like MARQUIS for environmental text generation systems.
Wilker Ferreira Aziz is an Assistant Professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning group. His primary affiliation is with the Natural Language Processing & Digital Humanities research unit. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Key areas of interest include language modeling, machine translation, syntactic parsing, text classification, and question answering. He develops techniques for probabilistic inference, gradient estimation, and uncertainty quantification in neural language models. Dr. Aziz's recent publications demonstrate a strong focus on uncertainty in natural language generation, with multiple papers at top-tier conferences like EACL, EMNLP, and ICLR. His work examines how language models represent uncertainty compared to humans, calibration issues when humans disagree on labels, and methods for more robust decision-making in text generation. Best Paper Award at Coling 2020 He actively supervises both PhD and MSc students, with several ongoing PhD projects focusing on uncertainty in language models and neural text generation. Dr. Aziz serves on program committees for major ML and NLP conferences including ACL, EMNLP, NeurIPS, and ICLR, and has acted as area chair for several of these venues. His research has been supported through positions at the Mercury Machine Learning Lab, a collaboration between Booking.com, TU Delft, and the University of Amsterdam.
Antti Kanner is a Researcher at the Department of Digital Humanities, University of Helsinki. His work focuses on computational linguistics, historical semantics, and interdisciplinary digital humanities projects. He is affiliated with the Helsinki Computational History Group and has contributed to projects like 'Flows of Power' and 'Formulaic Intertextuality.' Kanner's research explores topics such as modal grammar in political discourse, naming conventions in historical texts, and the evolution of Finnish national identity through language. His awards include the August Ahlqvistin Väitöskirjapalkinto (2023) and the Open Science and Research Award (2016). He collaborates internationally, particularly in Slavic and Finno-Ugrian studies, and has published widely on corpus linguistics, media analysis, and historical text mining. Key Projects: Helsinki Computational History Group (2018–present), Retoriset Ryhmästrategiat (2022–2026) Major Contributions: Developed workflows integrating automated text analysis with close reading, analyzed naming patterns in medieval Novgorod, and studied affectivity in political journalism.
Markus Egg is a Professor at the Department of English and American Studies , Humboldt University of Berlin. He is a principal investigator in the Collaborative Research Center 1412 (CRC 1412) focused on "Register: Language Users’ Knowledge of Situational-Functional Variation". His work bridges theoretical linguistics with computational approaches, particularly in metaphor annotation, register analysis, and discourse structure. He has organized international workshops such as MeStaR (Metaphors and Stance Markers in Register Variation). Current affiliation: Humboldt University of Berlin Key projects: CRC 1412, GeRMaN (German Register Marking by Non-Literal Expressions), MeStaR workshop Research Focus: His research examines how metaphors and metonymies function as markers of register variation across different contexts, including social media (e.g., LGBTQ+ slang on Twitter), religious language, and computational linguistics. He integrates methodologies from corpus linguistics, cognitive science, and natural language processing to model register knowledge. Teaching: Regularly teaches courses on linguistics, computational semantics, and machine learning applications in language research. In 2023, he conducted a webinar on "Register marking by metaphor and metonymy" at Nakhchivan State University. Collaborations: Collaborates with institutions like Charles University (Prague), Ruhr-Universität Bochum, and Università della Calabria. Frequent collaborator: Valia Kordoni (metaphor corpus development).
Lydia-Mai Ho-Dac is a Lecturer in Language Sciences at the University of Toulouse Jean Jaurès, affiliated with the Cognition, Languages, Ergonomics (CLLE) laboratory and the Language & Cognitive Processes team. She teaches corpus linguistics and Natural Language Processing , focusing on quantitative corpus methods and digital tools for linguistic research. Teaching: Corpus linguistics, NLP, data collection methodologies Research Themes: Discourse organization, textual cohesion, online discussion forums (health/Wikipedia), text typologies Her research combines corpus analysis with computational linguistics , particularly examining: Discourse structuring mechanisms Referential continuity in student writing Epistemic regimes in collaborative platforms like Wikipedia Textual cohesion markers in educational contexts Quantitative methods for analyzing web 2.0 corpora Annotation frameworks for multi-scale discourse Recent publications include studies on Wikipedia talk pages and the E-CALM student writing corpus. She contributes to projects like ANNODIS and WikiDisc, focusing on annotated resources for discourse analysis. As a supervisor, she advises PhD and Master's students in projects involving: Reference chain modeling Discourse annotation tools Linguistic approaches to health forums Computational analysis of under-specified names
Prof. Dr. Dirk Reinel serves as Professor at Hof University of Applied Sciences within the Faculty of Interdisciplinary and Innovative Sciences since the 2023/2024 winter semester. He leads the B.Sc. program in Innovative Health Care and acts as StartupLab Ambassador for Innovative Health Care, driving healthcare innovation initiatives. His academic journey includes Computer Science studies at Hof University followed by doctoral research at the University of Bamberg, where he earned his Dr. rer. nat. in 2018 through work on corpus-based lexical resource generation for opinion mining. Reinel's research bridges Health Informatics and Natural Language Processing , with dual specializations in health data management (particularly migraine epidemiology) and sentiment analysis . His publication record reveals two distinct trajectories: clinical studies analyzing migraine patterns relative to school schedules, weekly cycles, and temperature variations; and technical contributions to opinion mining including phrase generation algorithms and domain-specific applications for insurance and social media. His recent work demonstrates increasing focus on healthcare applications, culminating in the 2022 adolescent migraine study, while maintaining foundational NLP contributions from his 2018 dissertation. The integration of clinical research with advanced text analytics represents his distinctive interdisciplinary approach. Co-founding smartlytic GmbH in 2017 enabled practical implementation of his research, notably through the DMKG Kopfschmerzregister (Headache Register) developed with the German Migraine and Headache Society. His current StartupLab role extends this translational focus, mentoring healthcare innovation projects while directing the B.Sc. program in Innovative Health Care.
William Andreopoulos serves as an Assistant Professor in the Department of Computer Science at San José State University's College of Engineering. With a strong interdisciplinary background spanning computer science, bioinformatics, and molecular biology, he bridges computational methods with biological applications. His academic journey has taken him through prestigious institutions including Lawrence Berkeley National Laboratory, Columbia University, and TU Dresden. Ph.D. in Computer Science and Engineering, York University, Toronto, Canada (2006) M.Sc. in Computer Science, University of Toronto, Canada (2001) B.Sc. in Computing and Software, McMaster University, Canada (1999) Dr. Andreopoulos specializes in applying machine learning and computational approaches to biological problems, with particular emphasis on genomics, metagenomics, and bioinformatics. His research spans fungal genomics, microbial community analysis, plasmid identification, and the development of tools for omics data integration. He has extensive experience working with environmental data as well as cancer datasets from PCAWG and TCGA projects. His publication record reveals a strong interdisciplinary focus, with recent work spanning from fungal genomics and microbial identification to natural language processing applications. The research demonstrates consistent integration of machine learning techniques across diverse biological contexts, with notable contributions in developing computational pipelines for high-throughput sequencing data analysis. As an educator, Dr. Andreopoulos has mentored numerous graduate students through CS297/CS298 projects and CS280/CS180 courses, with students working on bioinformatics-related computational projects. His professional experience includes 8 years as a data scientist at the Joint Genome Institute, Lawrence Berkeley National Laboratory, where he developed software pipelines for automated processing of high-throughput sequencing data. His laboratory focuses on computational biology projects that require expertise in Java, Python, Linux command line, machine learning libraries, and data visualization tools. Current research directions include strain separation in microbial communities, plasmid identification, 16S sequence reconstruction, and deep learning applications to molecular biology problems.
Owen Rambow is a Professor at Stony Brook University's AI Innovation Institute, specializing in natural language processing and computational linguistics with a focus on formal linguistic analysis. Education and Career: Ph.D. in Computer and Information Sciences, University of Pennsylvania 15-year tenure as Research Scientist at Columbia University Industry experience at AT&T Labs—Research and Elemental Cognition LLC Research Focus: Rambow's work centers on morphology, syntax, and semantics within Tree Adjoining Grammar (TAG) frameworks, bridging phrase structure and dependency representations. His research spans natural language generation/understanding, discourse analysis of belief/sentiment signaling in email/Twitter communications, and sociolinguistic studies of power/gender dynamics in written conversations across Arabic, English, German, and Hindi. Publication Trends: Recent work (2024-2025) heavily explores large language model capabilities in multi-dimensional writing assessment, emotion recognition, theory of mind validation, and morphophonological processing. Key themes include zero-shot learning limitations, cross-dialectal analysis, and pragmatic marker recognition in specialized domains like roadrunner cartoon dialogues.
Paulo Miguel Torres Duarte Quaresma is a Full Professor at the Department of Informatics, Universidade de Évora, Portugal, and Senior Researcher at Centro ALGORITMI. His career spans roles as Vice-Rector for Research and Innovation (2022–2025), Director of the School of Science and Technology (2009–2013), and Member of FCT’s Board of Directors (2021). Holding a PhD in Informatics (specialized in AI/NLP) from Universidade Nova de Lisboa and a Habilitation in Informatics from Universidade de Évora, he leads the AI & BigData Lab (equipped with 10 petaflop NVIDIA DGX-A100 supercomputers) and co-coordinates PORTULAN CLARIN, a €2M FCT-funded language technology infrastructure. Current Affiliation: Universidade de Évora (since 1997) Research Labs: AI & BigData Lab, PORTULAN CLARIN, VISTA Lab, NOVA LINCS (2019–2021) His research focuses on Artificial Intelligence and Natural Language Processing , with applications in legal reasoning , medical informatics , geospatial accident analysis , and semantic web technologies . Recent work includes creating European Portuguese BERT models, analyzing 18th-century health texts, and developing AI solutions for clinical triage. Advising highlights include supervising 7 PhD and 26 MSc theses. He chairs international conferences like PROPOR (2020) and IDEAL (2023), and participates in projects integrating AI with public administration and regional development through Alentejo2020 funding.
Muhsin Menekse serves as an Associate Professor at Purdue University with a joint appointment between the School of Engineering Education and the Department of Curriculum & Instruction. His academic journey includes a Ph.D. in Curriculum and Instruction (Science Education specialization) from Arizona State University, complemented by advanced degrees in Educational Psychology and Physics from the same institution and Bogazici University. Ph.D. in Curriculum and Instruction (Science Education), Arizona State University M.A. in Educational Psychology (Measurement and Statistics), Arizona State University M.S. in Physics (Teaching specialization), Bogazici University B.S. in Physics (Teaching specialization), Bogazici University Menekse's research centers on engineering and science education with emphasis on student cognition, collaborative learning dynamics, metacognition, and technology-enhanced environments. His work bridges cognitive science with practical classroom applications through tools like CourseMIRROR, which leverages natural language processing to analyze student reflections. Current investigations explore AI-driven scaffolding, reflection quality metrics, and large-scale implementation of mobile learning platforms. Analysis of his recent publications reveals strong thematic continuity in reflection-based learning technologies, with increasing integration of NLP and AI methodologies since 2020. His work consistently addresses scalability challenges in engineering education while maintaining rigorous experimental designs across diverse classroom settings. Seed for Success Excellence in Research Awards (2017-2021) William Elgin Wickenden Award for best Journal of Engineering Education article (2014) NSF Fellow for Summer Institute in Advanced Research Methods (2021-2024) School of Engineering Education Excellence in Undergraduate Teaching Award (2020) AAAS scholarship for Next Generation Science Learning Goals workshop (2014) Menekse mentors doctoral students through dissertation committees while leading the CourseMIRROR project adopted across 15+ institutions including Purdue, University of Pittsburgh, and Bogazici University. His research has secured over $1 million in external funding annually since 2017. The CourseMIRROR platform, developed through collaborative grants, serves as both research instrument and pedagogical tool across engineering, computer science, and physics courses. His laboratory work focuses on the CourseMIRROR ecosystem, integrating mobile interfaces with NLP pipelines to analyze reflection data from thousands of students. Current initiatives involve large language model applications for sentiment analysis and adaptive scaffolding, with deployments spanning undergraduate engineering courses and middle school STEM classrooms.
