Miloje Despic is an Associate Professor in the Department of Linguistics at Cornell University , affiliated with the College of Arts and Sciences . His research spans theoretical syntax , morphology , and semantics , focusing on cross-linguistic variation and the syntax-morphology interface. Research Focus : Cross-linguistic variation in functional elements (e.g., articles), syntax-morphology interactions (e.g., hybrid agreement, clitic-affix distinction). Languages Studied : Slavic (Serbian, BCMS), Turkic, Algonquian (Cheyenne). Publication Trends : Recent work addresses reflexive possessive agreement in Mongolian, nominal ellipsis in Serbian, cyclic and multiple agreement in Cheyenne, markedness in Algonquian theme signs, and non-intersective adjectives. His research intersects syntactic theory , morphological anomalies , and semantic interpretation .
Lior Wolf is a Professor at the School of Computer Science, Tel Aviv University. Previously, he was a postdoctoral researcher at MIT's Center for Biological and Computational Learning (CBCL) under Prof. Tomaso Poggio and earned his PhD from Hebrew University of Jerusalem with Prof. Amnon Shashua. His educational background includes: PhD in Computer Science, Hebrew University of Jerusalem Postdoctoral Research, MIT CBCL Prof. Wolf's research centers on artificial intelligence with seminal contributions to deep learning, computer vision, and natural language processing. His work bridges theoretical foundations (e.g., attention mechanisms, transformer analysis) with practical applications in medical imaging, speech processing, and sign language technology. He pioneered methods for neural network interpretability, efficient sequence modeling, and multimodal fusion. Analysis of his 2023-2025 publications reveals dominant trends in large language model optimization (neuron pruning, attention analysis), efficient video generation, and cross-modal learning. His work increasingly integrates medical applications (fMRI/EEG analysis) while maintaining theoretical rigor in model architecture design. His scientific achievements include: Best paper award at EMNLP 2024 for 'Backward Lens: Projecting Language Model Gradients into the Vocabulary Space' Best paper award at SCIA 2023 for 'Gradient Adjusting Networks for Domain Inversion' Best paper award at FG 2021 for 'Generating Master Faces for Dictionary Attacks' Prof. Wolf mentors graduate students in the School of Computer Science and leads research at the ICRC building laboratory. His team collaborates with 'the friends of TAU' on projects spanning biometric security, medical imaging, and generative AI. Current work focuses on efficient transformers, neural network interpretability, and multimodal medical diagnostics.
Professor Kay O'Halloran serves as Chair Professor and Head of Department of Communication and Media within the School of the Arts at the University of Liverpool since August 2019. She concurrently holds the position of Co-Director for the Digital Media and Society Institute (DMSI), demonstrating significant leadership across academic and research domains. Her career spans prestigious institutions including Curtin University (2013-2019) and National University of Singapore (1998-2013), where she directed the Multimodal Analysis Lab and served as Deputy Director of the Interactive & Digital Media Institute. PhD from Murdoch University (1996) Postdoctoral position at Martin Luther University (1997-1998) Visiting Distinguished Professor at Shanghai Jiaotong University (2017-2020) Professor O'Halloran's research focuses on multimodal discourse analysis, particularly the interaction of language with visual and mathematical resources. Her pioneering work in systemic functional multimodal discourse analysis (SF-MDA) has significantly impacted mathematics education and digital communication studies. Current research emphasizes digital tools for multimodal analysis and mixed methods approaches to big data analytics, addressing contemporary challenges in misinformation and public health communication. Her recent publications reveal strong thematic concentration in pandemic-related communication analysis, misinformation resistance mechanisms, and multimodal argumentation across social and cultural contexts. The 15 most recent articles demonstrate consistent application of multimodal frameworks to urgent societal issues including public health crises, political discourse, and digital literacy challenges. Founding editor of Routledge Studies in Multimodality book series (57+ volumes) Over 150 publications in leading international journals 50+ plenary/keynote presentations globally Competitive funding from National Research Foundation, MOE Singapore, US Air Force, ARC, NHMRC, and others Professor O'Halloran has directed interdisciplinary research teams comprising social scientists, computer scientists, and designers. Her leadership extends to developing commercial multimodal analysis software (Multimodal Analysis Image and Video) adopted internationally. Current projects include Eurovision 2023 wellbeing evaluation and COVID-19 misinformation immunity development, demonstrating continued relevance to contemporary societal challenges.
Martin Willis Monroe is an Assistant Professor in the Department of Classics and Ancient History at the University of New Brunswick. His research focuses on the ancient Middle East, particularly cuneiform cultures, with an emphasis on the history of scholarly knowledge in Babylonian and Assyrian societies. He specializes in Mesopotamian astronomy and astrology, as well as quantitative approaches to historical data. Dr. Monroe holds a PhD in Assyriology from Brown University (2016), an MPhil and BA in Ancient Near Eastern Studies from the School of Oriental and African Studies (2008, 2007). Previously, he served as a postdoctoral fellow and research associate at the University of British Columbia (2016–2023). His current projects include the publication of his Hellenistic astrology research under contract with Brill, titled Celestial Schemata: A Series of Astrological Tables from Seleucid Babylonia , and his role as associate director of the Database of Religious History , a global initiative to quantitatively analyze religious and cultural data. He has contributed to excavations at the Neo-Assyrian site of Tušhan in southeastern Turkey and advocates for responsible qualitative-to-quantitative data conversion in historical scholarship. Dr. Monroe’s research bridges traditional philological approaches with digital humanities, focusing on topics such as cuneiform astral diagrams, the intersection of ‘scientific’ and ‘religious’ texts in antiquity, and the application of computational methods to undeciphered scripts. He teaches courses on ancient civilizations, Near Eastern history, and archaeology.
Pranav Rajpurkar is an Associate Professor at Harvard University, co-founder of a2z Radiology AI, and lead of the Rajpurkar Lab. His work pioneers AI systems that emulate physician-level expertise in medical tasks, with a focus on multi-modal medical AI and radiology. He joined Harvard faculty at age 25 and became Associate Professor at 30 after completing his Stanford PhD at 19. Education: Bachelor of Science (CS), Stanford University, 2015 Master of Science (CS), Stanford University, 2018 PhD (CS), Stanford University, 2021 Rajpurkar's research centers on building AI that thinks and communicates like doctors, with breakthrough work in ECG arrhythmia detection and chest X-ray interpretation. His lab develops foundational datasets (ReXGradient-160K, RadRevise) and benchmarks for medical AI, spanning computer vision, NLP, and multimodal systems. Current work focuses on generative AI for clinical workflows, voice-guided emergency assessment, and rigorous evaluation frameworks for medical LLMs. His 2025 publications reveal three dominant trends: (1) Generative medical AI for radiology report generation and editing, (2) Voice-enabled AI agents for prehospital care, and (3) Multimodal clinical monitoring systems. Key advancements include universal biomedical foundation models (UniBiomed), 3D CT segmentation from text reports, and frameworks for AI-human role separation in radiology. Scientific Awards: Forbes 30 Under 30 in Science (2022) MIT Tech Review Innovator Under 35 (2023) Nature Medicine Early-career Researcher To Watch (2022) Rajpurkar has mentored over 84,000 students through Harvard courses and Coursera's AI for Medicine program. His lab secures major NIH and NSF grants for medical AI development, with recent funding focused on multimodal emergency care systems (MC-MED) and radiologist-AI collaboration. He directs the Harvard-Stanford Medical AI Bootcamp and co-hosts The AI Health Podcast. The Rajpurkar Lab operates as a cross-institutional hub with collaborators at Stanford, MIT, and major hospitals. It drives initiatives like the MAIDA framework for global medical imaging data sharing and develops open-source tools including RadGraph for radiology report analysis. Current projects focus on voice AI for stroke assessment and generative models for non-invasive cancer management.
Tim Van de Cruys is a Senior Lecturer at the Faculty of Arts, KU Leuven, serving as Head of the Centre for Computational Linguistics (CCL). He maintains significant affiliations with LECTIO (KU Leuven Institute for the Study of the Transmission of Texts, Ideas and Images), Leuven.AI (KU Leuven Institute for Artificial Intelligence), and LILI (KU Leuven Interdisciplinary Language Institute). His work bridges computational linguistics, artificial intelligence, and humanities research with practical applications across multiple disciplines. Dr. Van de Cruys specializes in computational semantics and creative language generation, with particular expertise in applying NLP techniques to historical and classical texts. His research spans multiple domains including: Natural Language Processing for ancient languages (Latin, Ancient Greek) Computational approaches to lexical and compositional semantics Large language models and their applications in humanities research Creative language generation and human-AI collaboration Named entity recognition and disambiguation in historical contexts Non-autoregressive modeling for sequential generation tasks His recent publications demonstrate a strong focus on applying cutting-edge NLP techniques to humanities challenges, particularly in processing ancient languages. He frequently employs transformer models to address named entity recognition, word sense discrimination, and semantic analysis in low-resource language contexts. His work consistently bridges formal linguistic theory with practical computational applications, creating valuable tools for digital humanities scholars. As promotor and co-promotor on numerous research projects extending through 2029, Dr. Van de Cruys supervises PhD students working at the AI-humanities intersection. His current major projects include "Living Corpora" (exploring human-AI collaboration in digital humanities), "Stochastic processes and non-autoregressive models for sequential generation," and "NIKAW" (exploring knowledge networks from classical antiquity). These projects demonstrate his commitment to advancing both theoretical understanding and practical applications of computational linguistics. He teaches various courses including Computational Linguistics, Scripting Languages, Programming for Humanities, Computational Creativity, and AI for Humanities, training students to work at this critical interdisciplinary crossroads. His leadership of the Centre for Computational Linguistics positions him at the forefront of computational linguistics research in Belgium, where he continues to expand the boundaries of what's possible at the intersection of language, computation, and humanistic inquiry.
Wink Smith, PhD, is an Instructor in the Department of Interpretation and Translation at Gallaudet University, part of the School of Language, Education, and Culture. He holds a PhD in Linguistics (2023) and has over 16 years of professional experience as an interpreter, educator, and researcher. His work bridges theory and practice in interpretation, with a focus on cognitive linguistics frameworks, depiction in signed languages, and message analysis. He has presented nationally and internationally, published in RID Views, and developed CEU training programs. Research interests include embodied imagery in depiction, skill development methodologies, and the role of depiction in interpretation. He actively serves on professional committees such as the Certification Council for RID and the Conference of Interpreter Trainers (CIT). Awards include the 2016 NAD Outstanding Service Award and a 2021 Certificate of Appreciation for service in interpretation. Teaching responsibilities span undergraduate and graduate courses like INT-595G Special Topics, INT-736 Professional Practice I, and INT-223 Interactive Discourse Analysis. His recent scholarly contributions explore linguistic structures in signed languages and cognitive frameworks for interpreting.
Christiane Fellbaum serves as Lecturer with Rank of Professor in Princeton University's Program in Linguistics and Department of Computer Science, where she has been a senior research scholar since returning in 1987 after postdoctoral work at the University of Paris. Her foundational contributions to computational linguistics include co-developing WordNet and co-founding the Global WordNet Association. Her educational background features: Ph.D. in Linguistics, Princeton University (1980) Postdoctoral Fellowship, University of Paris Fellbaum's research integrates theoretical linguistics with computational applications, specializing in lexical semantics, corpus analysis, and semantic network construction. Her work bridges computational linguistics and lexicography through projects like WordNet and Medical WordNet, with recent emphasis on multilingual resources, bias analysis in embeddings, and African language technology development. She examines semantic phenomena including idioms, verb alternations, and emotion scales through both corpus linguistics and formal ontological frameworks. Analysis of her publication trajectory reveals sustained innovation in lexical resource development since the 2000s, evolving from foundational WordNet studies to contemporary work on large language model adaptation and social bias mitigation. Current research demonstrates increasing interdisciplinary collaboration across NLP, cognitive science, and social justice applications. Her scientific recognition includes: Wolfgang Paul Prize from the German Humboldt Foundation (2001) Antonio Zampolli Prize (2006) Fellbaum has secured continuous research funding from the U.S. National Science Foundation, European Union Seventh Framework, Frank Moss Foundation, and Tim Gill Foundation. She actively mentors junior researchers through Princeton's Independent Work seminars and hosts the North American Computational Linguistics Olympiad (NACLO), while leading major international collaborations including the KYOTO and SIERA European projects. As director of the WordNet project and permanent fellow at the Berlin-Brandenburg Academy of Sciences, she maintains leadership in global lexical resource initiatives through the Princeton Language and Intelligence initiative and Natural and Artificial Minds research group.
Gabrielle Hodge is a Senior Lecturer in Sign Language Linguistics at the University of Edinburgh's School of Philosophy, Psychology and Language Sciences. A deaf researcher, she specializes in sociolinguistics related to deaf communities, sign languages, and multimodal communication. Her work incorporates corpus linguistics, applied linguistics, and semiotics methodologies. Education: PhD in Linguistics (Macquarie University, 2014), BA (Hons) in Linguistics (La Trobe University, 2008). Teaching includes undergraduate and postgraduate courses in sign language linguistics and sociolinguistics. She chairs the University BSL Plan Implementation Group (2024-2030) and actively supervises PhD and MSc students in deaf community research. Research focuses on accessibility, sign language corpus methods, and inclusive language theory. Notable projects include the Accessibility & Inclusion Toolkit for deaf Australians and Signing to Know & Survive , exploring deaf communication resilience. She develops training for educators and interpreters, emphasizing direct access without relay systems. Key contributions include advancing corpus-driven sign language research, analyzing deaf professionals' trust in interpreters, and creating climate change resilience resources in Auslan. Her work challenges traditional linguistic frameworks to better incorporate deaf perspectives and embodied communication practices.
Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.
Dr Ellen Adams is a Reader in Classical Archaeology and Liberal Arts at King’s College London, affiliated with the Departments of Classics and Interdisciplinary Humanities. She holds a PhD from the University of Cambridge (2004) and has conducted archaeological fieldwork across Europe. Her research focuses on Minoan Crete, disability studies in classical contexts, and museum accessibility for sensory-impaired audiences. She co-organizes the ICS Mycenaean Seminars and founded the Museum Access Network for Sensory Impairments (MANSIL) in 2018. Her work bridges archaeology and modern accessibility, emphasizing audio description, touch tours, and British Sign Language in museums. Recent projects include Disability Studies and the Classical Body (Routledge, 2021) and collaborations with institutions like the British Museum and the Courtauld Gallery. She teaches Greek archaeology, museum studies, and interdisciplinary modules in global cultures. Adams has appeared on BBC Radio discussing Minoan civilization and classical reception. Her research projects include Making Sense of Visual Art Through a Visual Language (BSL) and Anosmia in Culture and History . She actively curates public engagement events, such as BSL storytelling in Holyrood Park and creative writing competitions for blind/visually impaired audiences.
Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.
Changjian Li is an Assistant Professor in the School of Informatics at the University of Edinburgh. He leads the GraphViX Group (Graphics, Vision and X) and is a member of the Institute of Perception, Action and Behaviour (IPAB). His research spans computer graphics, computer vision, and human-computer interaction with a focus on 3D generation and analysis. Education: Bachelor's Degree from Shandong University (2014) Ph.D. from the University of Hong Kong (2019) under Prof. Wenping Wang Postdoc at University College London (UCL) with Prof. Niloy Mitra Starting Researcher position at Inria with Dr. Adrien Bousseau Research Interests: Changjian's research focuses on sketch-based 3D modeling, CAD modeling, point cloud processing, and medical imaging applications. He develops systems that bridge intuitive sketching with precise CAD workflows, enhances 3D animation pipelines, and applies neural methods to sparse medical data reconstruction. Scientific Recognition: Best Paper Honorable Mention Award (MICCAI 2021) CADTalk selected as Highlight (CVPR 2024 top 10%) ACM SIGGRAPH Asia 2018 cover image selection ACM SIGGRAPH Asia 2015 technical paper highlight CVPR 2019 poster highlighted in 'Computer Vision News' Advising & Collaborations: He mentors postdocs and PhD students including Duolikun Danier, Haocheng Yuan, Ankan Bhunia, and Lei Zhong. Former advisees include Salvatore Esposito (now at Edinburgh), Guangshun Wei (Shandong University), and Mingjun Yang (University of Melbourne). Collaborates with Oisin Mac Aodha, Hakan Bilen, and Niloy Mitra. Professional Service: Currently serves as Associate Editor for IEEE TVCG and participates in program committees for SIGGRAPH Asia, SIGGRAPH, EuroGraphics, and Geometry Design and Computing (GDC) conferences.
Professor Peter Watkinson serves as Professor of Intensive Care Medicine at the University of Oxford and is an NHS consultant in intensive care at the Oxford University Hospitals NHS Foundation Trust. He leads the Critical Care Research Group based at the Kadoorie Centre for Critical Care Research & Education at the John Radcliffe Hospital, Oxford. His work bridges clinical practice with academic research in the field of critical care medicine through the Nuffield Department of Clinical Neurosciences. Professor Watkinson's research primarily focuses on the identification of deteriorating patients in hospital settings. His work encompasses: Design and implementation of studies on wearable monitoring devices Exploration of non-contact monitoring technologies Analysis of standard electronically-recorded patient descriptors Pattern recognition in vital signs data to predict clinical deterioration Development of electronic monitoring systems Application of human factors techniques for technology integration in healthcare Assessment of long-term effects of critical illnesses on patient quality of life The Critical Care Research Group maintains a strong collaborative link with the University of Oxford Institute of Biomedical Engineering. Using data collected from thousands of patients' vital signs both in Oxford and elsewhere, the multi-disciplinary team investigates patterns that precede and predict clinical deterioration in hospitalized patients. Recent publications indicate a strong focus on early warning scores, patient monitoring technologies, and the application of machine learning approaches to critical care data. Professor Watkinson's research output demonstrates consistent productivity with numerous 2024-2025 publications spanning systematic reviews of early warning systems, development of novel monitoring technologies, and analytical approaches to predicting patient deterioration. His work frequently employs rigorous methodology including systematic reviews, meta-analyses, and innovative study designs to address critical questions in intensive care medicine. As leader of the Critical Care Research Group, Professor Watkinson oversees a multi-disciplinary team investigating vital sign patterns and developing predictive algorithms that have direct clinical applications. The group's research has significant implications for improving patient safety through earlier recognition of clinical deterioration and more effective resource allocation in hospital settings.
Eliese-Sophia Lincke is a Junior Professor at the Department of History and Cultural Studies, Freie Universität Berlin, since May 2022. Her work bridges computational methods with Egyptology, focusing on digital tools for studying ancient texts. Bachelor's and Master's in Egyptology, Humboldt-Universität zu Berlin (2007) PhD in "The Conception of Spaces in Language" (TOPOI Cluster, 2012) Research interests include: Digital Humanities : Developing machine learning models for Hieroglyphic, Demotic, and Coptic text processing Linguistic Typology : Analyzing classifier systems in Ancient Egyptian and Sign Languages Spatial Linguistics : Investigating prepositions and spatial adverbs in Egyptian-Coptic Recent publications focus on Neural Lemmatization , OCR for Coptic , and Classifier Semantics , demonstrating her commitment to computational Egyptology. Scientific awards include the Humboldt-Preis 2008 for best Master's thesis and the Prize for Good Teaching 2014 . She has co-organized workshops like "Wege zum Ägyptischen" and served as Co-Editor for Lingua Aegyptia . Her teaching contributes to the Digital Studies of Ancient Texts Master's program.
