Find Academic Supervisors

• Language Development
• Cognitive Science
• Developmental Psychology
• +3 more

Chiara Santolin is a Researcher at the Universitat Pompeu Fabra (UPF), affiliated with the Speech Acquisition and Perception (SAP) Group since October 2017. She holds a Ph.D. in Psychology from the University of Padova (2016) and an M.Sc. in Experimental Psychology & Cognitive Science (Summa cum Laude, 2013), also from Padova. She conducted postdoctoral training at the University of Wisconsin-Madison (USA, 2016-2017). Education: Ph.D., Psychology, University of Padova (2016) M.Sc., Experimental Psychology & Cognitive Science, University of Padova (2013) Her research explores the mechanisms underlying language acquisition, investigating whether these processes are unique to language or shared across domains. She employs interdisciplinary approaches, combining studies with human infants and animal models (e.g., domestic chicks) to examine statistical learning, syntactic structures, and categorization. Recent work includes analyzing infants’ preferences for speech prosody, constraints on non-linguistic grammar learning, and cross-species comparisons of visual and auditory learning. Collaborators include Jenny Saffran (University of Wisconsin-Madison), Giorgio Vallortigara (University of Trento), and researchers at the University of Padova. She actively participates in international conferences, presenting findings on topics like bilingual acquisition and infant cognitive development. Chiara’s research bridges developmental psychology, cognitive science, and comparative biology, aiming to uncover universal learning principles and their evolutionary foundations.

Omri AbendView profile

Associate Professor

• Computational Linguistics
• Natural Language Processing
• Semantic Representation
• +9 more

Omri Abend is an Associate Professor at The Hebrew University of Jerusalem, affiliated with the School of Computer Science and Engineering and serving as Chair of the Department of Cognitive and Brain Sciences. His research lies at the intersection of Computational Linguistics, Natural Language Processing, and Cognitive Science, with a focus on semantic representation and language acquisition modeling. His primary research interests include: Computational modeling of child language acquisition Semantic representation frameworks, particularly Universal Conceptual Cognitive Annotation (UCCA) Statistical learning and machine translation Unsupervised grammar learning and lexical relation induction Cross-lingual and cross-domain alignment in language models Evaluation methodologies for NLP systems His recent publications demonstrate a strong trend toward analyzing large language models (LLMs), exploring human-like patterns in AI, improving evaluation metrics, and applying NLP to humanitarian domains such as Holocaust testimony analysis. His work combines theoretical linguistic insights with practical machine learning applications. Scientific awards include: Outstanding Paper Award at ACL 2017 Area Chair Award for Best Paper in the Track at ACL 2023 He has supervised and collaborated with numerous researchers and students across projects in semantic parsing, machine translation, and cognitive modeling. His work has been supported by community-wide initiatives such as the MRP shared tasks, which he co-organized. He also leads research on ethical AI, open human feedback, and the computational analysis of historical narratives. Omri Abend leads several research teams focused on: The development and application of the UCCA framework for semantic annotation Cross-lingual and cross-domain knowledge representation in LLMs Computational modeling of language acquisition Evaluation and improvement of NLP systems Application of NLP to digital humanities and historical testimony analysis

Jean-Cédric ChappelierView profile

Senior Lecturer

• Computational Linguistics
• Natural Language Processing
• Machine Learning
• +3 more

Jean-Cédric Chappelier is a Senior Lecturer and Researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences. He works in computational linguistics and natural language processing, with a focus on robust parsing techniques, semantic indexing, and clustering algorithms. His research interests span computational linguistics, natural language processing, machine learning, tree substitution grammars, and semantic indexing. His publications from 2000-2006 demonstrate expertise in stochastic parsing, ontology-based indexing, and community structure analysis in complex networks. He has supervised multiple EPFL PhD students including Florian Seydoux and Emmanuel Eckard. Jean-Cédric holds an Ms.Sci. and Ph.D. in Computer Science from École Nationale Supérieure des Télécommunications de Paris. He teaches courses on object-oriented programming, computer systems, and natural language processing at EPFL.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +6 more

Tengda Han is a Research Scientist at Google DeepMind , specializing in neural networks for video understanding. He earned his PhD from the University of Oxford under Andrew Zisserman and a BEng in Mechanical & Material Engineering from the Australian National University . Prior to his PhD, he studied business administration and law at Renmin University of China for one year. PhD in Computer Science, University of Oxford (2022) BEng in Mechanical & Material Engineering, Australian National University (2016) One year of business administration and law at Renmin University of China His research focuses on video representation learning , self-supervised methods , multimodal learning , and efficient prompting techniques . He has pioneered training-free frameworks for audio description, zero-shot object counting, and hyper-realistic movie generation. Recent publications demonstrate trends in temporal alignment , discriminative prompting , 5D video modeling , and open-world counting . His work often bridges computer vision with natural language processing and speech recognition . Scientific Awards : Best Paper Award at ACCV2024 Best Poster Award at BMVC2023 BMVA Sullivan Doctoral Thesis Prize Runner-up He has co-organized workshops at ICCV25 , NeurIPS23 , and BMVA Symposium on Vision & Language . Collaborators include Andrew Zisserman, Dima Damen, and Flamingo project members at DeepMind.

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.

• Computational Semantics
• Syntax-Semantics Interface
• Natural Language Processing (NLP)
• +2 more

Gábor Kata is a Lecturer at INALCO (National Institute of Oriental Languages and Civilizations) in Paris, France. He works within the Computer Science Multilingualism department, contributing to the ERTIM research team . His expertise spans Computational Semantics , Syntax-Semantics Interface , and Natural Language Processing (NLP) . CNU Discipline: 27 - Computer Science Areas of Expertise: Europe Teaching Themes: Lexical Semantics, Computational Semantics, Linguistics for NLP, NLP His research focuses on Computational Semantics and Syntax-Semantics Interface , with particular attention to NLP applications. His scholarly output reveals trends in semantic relation extraction , anomaly detection in text data , and knowledge base population using both supervised and unsupervised learning approaches. This work often employs graph-based methods and vector space analysis across multilingual contexts.

Tobias StraußView profile

Lecturer

• Mathematics Education
• Historical Document Analysis
• Machine Learning
• +5 more

Dr. Tobias Strauß is a Lecturer at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, University of Rostock. He teaches courses such as Elementary Algebra and Number Theory and Analytical Geometry, focusing on foundational mathematical concepts and their applications. His research interests span historical document analysis, machine learning, and neural networks, with a particular emphasis on handwritten text recognition (HTR) and computational solutions for cultural heritage digitization. He actively contributes to the Mathematics Society RHO eV, supporting students in mathematics competitions and enrichment programs. Dr. Strauß’s research combines computer vision and machine learning to address challenges in document analysis, including text line detection, cursive script recognition, and keyword search in historical manuscripts. His work also explores semi-supervised learning techniques and neural network architectures tailored for HTR tasks. Through RHO eV, he promotes mathematics education through weekend seminars, district clubs, and game-based learning activities, fostering student engagement and problem-solving skills. His publications highlight advancements in HTR systems, such as the CITlab Recognition & Retrieval Engine, and address topics like regular expression-based decoding and Arabic handwriting recognition. These contributions underscore his expertise in bridging theoretical mathematics with practical applications in digital humanities and education. Dr. Strauß can be contacted via tobias.strauss@uni-rostock.de or through the university’s chat platform. No formal awards or grants are noted in the provided materials, though his involvement in international competitions (e.g., ICFHR, ICDAR) reflects his scholarly engagement.

Zakaria KurdiView profile

Associate Professor

• Text & Data Mining
• Intelligent Tutoring Systems
• Social Network Analysis
• +5 more

Dr. Zakaria Kurdi is an Associate Professor of Computer Science at the University of Lynchburg. He joined the institution in 2016 and specializes in interdisciplinary research at the intersection of artificial intelligence, computational linguistics, and data science. His work spans text and data mining, intelligent tutoring systems, social network analysis, and cognitive modeling of spoken language. Education: PhD in Computer Science, Joseph Fourier University, France MS in Software Engineering, École Nationale Supérieure des Mines de Saint Étienne, France MS in Cognitive Science, Institut National Polytechnique de Grenoble, France BA in Linguistics, Université Lumière, France Research Interests: Dr. Kurdi’s research focuses on applying computational methods to linguistic and healthcare challenges. Key areas include analyzing spoken language disfluencies, developing adaptive educational tools for ESL learners, and leveraging machine learning for dementia detection through language analysis. He also explores bioinformatics applications, such as studying bacterial protein distributions, and social network analysis using datasets like Enron’s emails. Recent Research Trends: His publications (2024–2017) highlight a strong focus on interdisciplinary AI applications, with recent emphasis on medical diagnostics via natural language processing and computational biology. Earlier work includes foundational contributions to spoken language understanding systems and text complexity classification. Labs/Teams: While no specific lab is named, his affiliations imply involvement in interdisciplinary teams within the University of Lynchburg’s Computer Science department, collaborating on projects blending linguistics, AI, and biomedical data analysis.

Joseph Le RouxView profile

Associate Professor

• Natural Language Processing
• Machine Learning
• Optimization
• +3 more

Joseph Le Roux is an Associate Professor at the University of Paris 13, affiliated with the LIPN laboratory. His research focuses on Natural Language Processing (NLP), Machine Learning (ML), and their applications to optimization problems. He has supervised multiple PhD students, including Yash Kankanampati, Nicolas Floquet, and Francesco Demelas, among others. Le Roux's work emphasizes dependency parsing, attention mechanisms, and optimization techniques in NLP. His research has been published in top venues such as NAACL, ICML, and EMNLP. He also contributes to academic service, including roles as an Action Editor for ARR and a member of the TAL journal's editorial board. His teaching responsibilities include courses on neural networks, generative models in NLP, and programming for AI. He co-manages the Data Science hub at LIPN and leads research projects like SemiAmor (ANR CE23-2023-0005) and ParSiTi (ANR-16-CE33-0021). Le Roux's academic contributions span over two decades, with a PhD in 2007 and an HDR (Habilitation) in 2024. His lab collaborations include work on XMG, a metagrammar compiler, and projects addressing challenges in social media NLP and MIP solving via ML.

MARCOS GARCIA GONZALEZView profile

Research Fellow

• Computational Linguistics
• Natural Language Processing
• Multilingual Language Technologies
• +3 more

Marcos Garcia Gonzalez is a Ramón y Cajal Research Fellow at the University of Santiago de Compostela (USC), affiliated with the Department of Spanish Language and Literature and the Center for Research in Intelligent Technologies (CITIUS). His research focuses on computational linguistics, particularly in semantic analysis, multilingual systems, and language technologies for Galician and Portuguese. He leads projects like the Nós Project, advancing Galician's integration into AI and NLP tools. His work includes developing resources like the Parallel Universal Dependencies Treebank and tools such as Bertinho (Galician BERT models). Key areas include idiomaticity detection in word representations, vector models for semantic analysis, and syntactic parsing. Education: PhD in Computational Linguistics from USC (2014), Master in Linguistics (University of Lisbon), and Degree in Portuguese Philology (USC). Postdoctoral research under the Juan de la Cierva fellowship (2016-2020). Awards include Ramón y Cajal and Juan de la Cierva fellowships. Research interests span vector models, multilingual NLP, and semantic compositionality. Projects include LINNA (linguistic-based neural models) and DeepR3 (green language tech). His publications explore topics like homonymy/synonymy representation and Galician language preservation through open-source tools. Key contributions include annotated corpora, coreference resolution systems, and cross-lingual parsing methods. He collaborates on hybrid intelligence systems for education (iRead4Skills) and Responsible AI (DeepR3.gal). Current efforts emphasize leveraging linguistic knowledge to enhance NLP models' interpretability and multilingual support.

Oiwi Parker JonesView profile

Research Fellow

• Neural Engineering
• Machine Learning
• Robotics
• +3 more

Dr. Oiwi Parker Jones is a Hugh Price Fellow in Computer Science at Jesus College, University of Oxford, and Principal Investigator leading the Parker Jones Neural Processing Lab (PNPL) at the Oxford Robotics Institute. Their research focuses on neural speech prosthetics, combining machine learning with neuroscience to develop technologies for speech restoration. They hold an honorary fellowship in the Nuffield Department of Clinical Neurosciences and teach in Engineering Science, Computer Science, and Neuroscience, including roles as a stipendiary lecturer in neuroscience and medical teaching at St Peter’s and Oriel Colleges. Jones’ work spans robotics, neural decoding, and endangered language preservation, with a particular emphasis on Hawaiian linguistics. Their lab develops large-scale machine learning methods for neural data analysis and collaborates on interdisciplinary projects involving clinical neuroimaging and AI ethics. Jones is also engaged in cultural preservation efforts through computational linguistics and has published widely on language contact, phonology, and indigenous protocols in technology. Education: Doctoral research in NLP and machine learning at Oxford Neuroscience training at UCL and Oxford Research Interests: Neural prosthetics, speech decoding, small-data machine learning, robotics, and endangered language preservation. Current projects include non-invasive brain-to-text systems, clinical fMRI applications, and computational tools for Hawaiian linguistic analysis. Teaching Contributions: Undergraduate lectures on generative deep learning and robotics, postgraduate supervision in DPhil projects across Engineering, Computer Science, and Neuroscience. Known for interdisciplinary teaching methods integrating neuroscience and AI. Awards & Recognition: No explicit awards listed, but recognized for innovative interdisciplinary research and leadership in neural engineering. Labs & Teams: Leads PNPL, collaborates with Oxford Robotics Institute and Applied Artificial Intelligence Lab. Active in global networks for clinical neuroimaging and indigenous AI ethics.

Mitch MarcusView profile

Professor

• Statistical natural language processing
• Cognitively plausible models
• Linguistic structure acquisition

Mitch Marcus is a Professor Emeritus in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. He earned his Ph.D. from MIT in 1978 and joined Penn in 1987 after working at AT&T Bell Laboratories. His research focuses on statistical natural language processing and cognitively plausible models for automatic acquisition of linguistic structure. Dr. Marcus created and led the Penn Treebank Project, which revolutionized natural language parsing accuracy. He has served as PI on multiple major projects including DARPA LORELEI and GAILA AIX, focusing on unsupervised morphology acquisition and child language acquisition simulations. Awards: Fellow of American Association for Artificial Intelligence (1992) Founding Fellow of Association for Computational Linguistics (2011) Advising: His former PhD students hold positions at institutions including MIT, Johns Hopkins, Google, and Microsoft Research. Research groups: Directed the Penn Treebank Project and co-led the OntoNotes Project. Currently collaborates on natural language understanding for human-robot interaction.

Aaron WhiteView profile

Professor

• Computational Linguistics
• Syntax
• Semantics
• +3 more

Prof. Aaron White is a Professor of Linguistics at the University of Rochester since 2017. He holds a PhD in Linguistics from the University of Maryland, College Park (2015), advised by Valentine Hacquard and Jeffrey Lidz. Prior to Rochester, he was a postdoctoral fellow at Johns Hopkins University's Science of Learning Institute, affiliated with the Department of Cognitive Science and the Center for Language and Speech Processing. His research focuses on computational linguistics, syntax, semantics, and natural language processing, particularly in syntactic bootstrapping, propositional attitude verbs, and event structure decomposition. Key research projects include leadership of the MegaAttitude Project and contributions to the JHU Decompositional Semantics Initiative. He teaches courses on statistical methods in linguistics, computational linguistics, and deep learning applications. His work bridges formal semantics, syntax, and computational models, emphasizing cross-linguistic analysis and parser development. Publications span document-level information extraction, neg-raising inferences, and semantic typology. He has developed tools like the Decomp Toolkit for decompositional semantics. Current research explores lexicalization processes in parsers and temporal reasoning in NLI tasks. Labs/Teams: JHU Decompositional Semantics Initiative, MegaAttitude Project.

Wei TangView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Visual Compositionality
• +3 more

Wei Tang is an Assistant Professor at the Department of Computer Science , University of Illinois Chicago , focusing on computer vision and machine learning with emphasis on visual compositionality and data-efficient approaches. PhD from Northwestern University (2019) Research Interests : Computer Vision Machine Learning Visual Compositionality Data-Efficient Vision 3D Reconstruction Human Pose Estimation Recent Publication Trends : Pioneering part-whole structure modeling for 3D objects (CVPR'25, ECCV'24) Advancing visual dependency modeling in semantic segmentation (CVPR'21, PR'22) Innovating graph networks for pose estimation (ICCV'21, BMVC'20) Developing compositional networks integrating stochastic grammars (ECCV'18, ICCV'17) Academic Service : Associate Editor for Pattern Recognition (2024-present) Area Chair for ICCV'25 , BMVC'25 , ICME'25 , etc. NSF Panelist (2023-2024) Teaching : CS 515 Advanced Computer Vision (Spring 2025) CS 415 Computer Vision I (Fall 2024) CS 412 Introduction to Machine Learning (Spring 2024)