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Ryan Heuser is an Assistant Professor in Digital Humanities at the University of Cambridge, specializing in computational approaches to literary and intellectual history, prosody, and artificial intelligence's impact on language. His work bridges data science, machine learning, and literary studies through digital methodologies. Doctoral training in Eighteenth-Century British Literature, Stanford University (2019) Founding member & Associate Research Director, Stanford Literary Lab Junior Research Fellow, King’s College Cambridge (2019-2022) Research interests span computational modeling of semantic revolutions, large-scale literary field analysis, and the intersection of digital methods with historical and intellectual studies. His book Explorations in the Digital History of Ideas (2023) co-edited with Peter de Bolla exemplifies this approach. Recent publications focus on historical semantics, metrical analysis, and digital mapping of emotions in literature, reflecting his interdisciplinary expertise in natural language processing, network theory, and literary data visualization. Currently leads teaching initiatives at Cambridge Digital Humanities and contributes to computational projects exploring textual rhythms and large language models.

• Digital Humanities
• Computational Prosody
• Literary and Intellectual History
• +5 more

Yarin Gal is an Associate Professor of Machine Learning at the University of Oxford's Department of Computer Science and a Tutorial Fellow at Christ Church College. He is also a Turing AI Fellow at the Alan Turing Institute and Director of Research at the UK Government’s AI Safety Institute. He leads the Oxford Applied and Theoretical Machine Learning (OATML) Research Group, focusing on Bayesian deep learning, AI safety, and uncertainty quantification. Education PhD in Uncertainty in Deep Learning (2016) Research Interests His research integrates Bayesian methods with deep learning to address challenges in AI safety , uncertainty quantification , and robustness . Key areas include: Bayesian neural networks and approximate inference Uncertainty estimation in deep learning AI safety and interpretability Applications in autonomous driving, medical imaging, and NLP Publications & Trends His recent work spans Bayesian optimization , adversarial robustness , and continual learning . Notable contributions include Targeted Dropout for model pruning, Uncertainty in Autonomous Driving , and theoretical studies on adversarial examples in Bayesian networks. Awards & Honors Turing AI Fellow Teaching & Supervision He has taught Advanced Machine Learning , Uncertainty in Deep Learning , and contributed to NASA's Frontier Development Lab. His current students include Kelsey Doerksen, Gunshi Gupta, and Shreshth Malik. Labs & Teams He leads the OATML Group , a multidisciplinary team advancing theoretical and applied machine learning, with a focus on safety and interpretability in AI systems.

• Bayesian Deep Learning
• Machine Learning
• AI Safety
• +4 more

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +4 more

Walid Magdy is a Professor at the School of Informatics, The University of Edinburgh, where he is a faculty member at the Institute for Language, Cognition and Computation (ILCC). He holds a PhD from Dublin City University's School of Computing and has been a faculty fellow at The Alan Turing Institute. With substantial industry experience, he has worked for Qatar Computing Research Institute (QCRI), Microsoft, and IBM, where he filed nine patents. His research spans computational social science and natural language processing. In computational social science, he focuses on social content analysis, political bias detection, polarization, and users' behavior analysis/prediction, with particular attention to understudied communities. His natural language processing work centers on social applications including sentiment analysis, sarcasm detection, and hate-speech identification, with specialized expertise in Arabic and its dialects as well as Persian. Professor Magdy's recent publications demonstrate a consistent focus on Arabic language processing and social media analysis, with growing attention to cultural factors in NLP systems. His work bridges computational methods with social science questions, particularly regarding misinformation, cultural differences in online behavior, and the impact of platform policies on diverse linguistic communities. He has expanded his research to include Persian language processing and continues to explore social dynamics across different cultural contexts. His significant contributions have been recognized with multiple awards: Best Demo Award for TwiXplorer at CSCW 2024 Outstanding Paper Award at ACL 2024 Best Paper Award Runner-up at SocInfo 2022 Best Paper Award at WANLP-EMNLP 2022 Best Paper Award at AIRS 2015 Best Dataset Award at ICWSM 2013 Professor Magdy serves as an editor for Springer's Social Network Analysis and Mining journal and has held leadership roles in major conferences including ACL 2023, ArabicNLP 2023, ICWSM 2022, and ASONAM 2022. He has co-organized numerous shared tasks focused on Arabic language processing and social media analysis, contributing significantly to community resources and benchmarks. He founded and directs the Social Media Analysis and Support for Humanity (SMASH) research group at the University of Edinburgh, which has organized the Summer School in Computational Social Science (SICSS-Edinburgh) in 2022, 2023, and 2025, training researchers in computational social science methodologies.

• Computational Social Science
• Natural Language Processing
• Social Content Analysis
• +8 more

Professor Janet B. Pierrehumbert is a leading academic in computational linguistics and natural language processing, holding the position of Professor of Language Modelling at the Oxford e-Research Centre, University of Oxford. She is also a Senior Research Fellow at Trinity College and affiliated with the Faculty of Linguistics, Philology and Phonetics. Her work bridges interdisciplinary research in phonology, sociolinguistics, and computational models of language dynamics. Education: PhD in Linguistics from MIT (1980), A.B. in Linguistics from Harvard University (1975). Research Interests: Focuses on computational linguistics, dialect variation, language dynamics, and the societal impacts of NLP. Her group develops algorithms for analyzing social media discourse, forecasting trends, and modeling language evolution. Recent work includes studies on dialect fairness in LLMs and semantic shifts in political discourse. Key Projects: EPSRC-funded research on online forum dynamics, the Wordovators project on lexical innovation, and collaborations with institutions like the Oxford Man Institute. Her work emphasizes robust NLP systems and theoretical linguistics. Awards: ISCA Medal (2020), National Academy of Sciences membership (2019), Fellowships from the American Academy of Arts and Sciences and Cognitive Science Society. Grants & Advising: Over £6M in research funding, including EPSRC and Templeton grants. Supervised over 30 PhD students and postdocs, many now leading academics and industry researchers in NLP and linguistics. Labs/Teams: Leads the Pierrehumbert Language Modelling Group, collaborating with the Oxford e-Research Centre and international partners like Stanford and the University of Canterbury.

• Computational Linguistics
• Natural Language Processing
• Phonology
• +6 more

Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk

• Machine Learning
• Bayesian Optimization
• Theoretical Physics
• +3 more

Dr Helen Gittos is a Senior Lecturer and Fellow in Medieval History at Balliol College and Brasenose College, University of Oxford. She holds a PhD in early medieval history from Oxford, following an undergraduate degree in English Literature at Newcastle University. Her research focuses on medieval church history, liturgy, and the role of language in religious practices, with expertise in archaeological evidence and textual analysis. She teaches British and European medieval history at both undergraduate and graduate levels, including the MSt and MPhil programs in Medieval History. Her major publications include Liturgy, Architecture and Sacred Places in Anglo-Saxon England (2013) and co-edited volumes on medieval liturgy. Current projects explore the use of English in medieval liturgy and collaborative research on Cerne Abbas’s early medieval history with archaeologists. She leads the Oxford Centre for Early Medieval Britain and Ireland and participates in interdisciplinary initiatives like the Worked in Stone project. Dr Gittos actively supervises DPhil students in areas related to her research, with recent advisees focusing on topics like shrines, liturgical charms, and Anglo-Saxon burial practices. She is also a media commentator, appearing in BBC News and podcasts discussing Anglo-Saxon history and archaeological discoveries.

• Medieval Church History
• Medieval Liturgy
• Old English Language
• +3 more

Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +4 more

Henriëtte Hendriks is a Professor in Language Acquisition and Cognition at the University of Cambridge, affiliated with the Faculty of Modern and Medieval Languages and Linguistics and the Theoretical and Applied Linguistics (TAL) department. She leads the Cambridge Processing and Acquisition of Language lab (CAMPAL) and serves as Deputy Director of the Centre for Lifelong Learning and Individualised Cognition (CLIC). Education: Sinology at Leiden University Early Career: Coordinator at Max-Planck Institute for Psycholinguistics (DFG/ESF projects) Her research explores how languages encode concepts (person, time, space, causality) and their impact on language acquisition. Key themes include: Cognitive Linguistics and Language-Cognition Interfaces First and Second Language Acquisition Mechanisms Multilingualism and Cognitive Flexibility Typological Variation in Semantic Structures Deictic Terms and Dynamic Space Representation Recent publications focus on motion event typology across English, German, French, and Uyghur, examining cross-linguistic influences in acquisition processes. Her work combines experimental methods with syntactic analysis to study semantic mapping and grammaticalization in multilingual contexts. Current projects include: Centre for Lifelong Learning and Individualised Cognition (CLIC) - Principal Investigator MEITS Project (Strand 5) - Co-Investigator Cambridge Language Sciences Incubator projects on cognitive pacing and parental linguistic contributions International DeicTeS project on deictic meaning processes Labs & Teams: Cambridge Processing and Acquisition of Language lab (CAMPAL) Collaborations with Zoe Kourtzi, Vicky Leong, and Clare Hughes

• Cognitive Linguistics
• First Language Acquisition
• Second Language Acquisition
• +3 more

Dr Aaron D. Hornkohl is an Associate Professor in Hebrew and Bye-Fellow at Girton College, University of Cambridge. He serves as Director of Studies in Asian and Middle Eastern Studies at Girton and Homerton Colleges. His academic background includes a BA from Biola University, MA from The Hebrew University of Jerusalem, and PhD (2012) on the linguistic analysis of the Book of Jeremiah. His research focuses on ancient Hebrew philology, linguistic periodization, and exegesis, with a particular interest in the diachronic development of Hebrew and textual criticism of biblical manuscripts. Teaching responsibilities include courses on ancient and modern Hebrew literature, language history, and Middle Eastern culture. He co-organizes initiatives like the Sutton Trust Summer School and leads AHRC-funded research on critical editions of Hebrew texts, including the Kaufmann MS of the Mishna. Recent publications explore Tiberian reading traditions, Masoretic vocalization, and linguistic features in Qumran manuscripts. Research projects include Grammar of Biblical Hebrew Books (2024) and Studies in the Masoretic Tradition (2022). His work bridges linguistic analysis with historical context, addressing questions of language evolution and textual authenticity in Hebrew biblical and rabbinic corpora.

• Hebrew linguistics
• Semitic linguistics
• Historical linguistics
• +6 more

Professor Carl Edward Rasmussen is affiliated with the University of Cambridge , where he focuses on Machine Learning , Probabilistic Inference , Decision Making , and Reasoning Under Uncertainty . His work bridges theoretical advancements with practical applications in robotics, control systems, and computational biology. Academic Affiliation: University of Cambridge Academic Role: Professor His research emphasizes scalable Gaussian process methods, Bayesian system identification, and reinforcement learning. Recent projects include transfer learning for antibacterial discovery , graph neural processes for molecular functions , and efficient variational inference techniques . Key themes in his publications highlight uncertainty quantification , model generalization , and nonparametric approaches . Notable Scientific Contributions Advancements in sparse Gaussian process hyperparameter estimation Framework for Bayesian system identification in dynamic systems Hybrid models combining transformers and Gaussian processes

• Machine Learning
• Probabilistic Inference
• Decision Making
• +1 more

Professor Helena Sanson is a Full Professor of Italian, History of Linguistics, and Women's Studies at the University of Cambridge , where she is also a Fellow of Clare College and serves as Postgraduate Teaching and Examining Officer for the Italian Section. She joined Cambridge in 2005 after teaching at University College London (1998-2003) following her British Academy Postdoctoral Fellowship. PhD from University of Reading (2002) Her interdisciplinary research focuses on the intersection of Italian linguistic history , literary culture , and gender studies from the Renaissance to post-Unification period. Particular emphasis includes women's contributions to linguistic thought , conduct literature analysis , and female roles in translation networks . Her work examines how women navigated language standardization, educational access, and literary production across social strata. Recent projects include Leverhulme-funded research on women's linguistic experiences in 18th-19th century Italy and co-edited volumes on transnational travelers' accounts and early modern female education . She founded the Women and Gender in Italy (1500-1900) book series and the journal Women Language Literature in Italy . Scientific awards include: British Academy Postdoctoral Fellowship Leverhulme Research Fellowship Philip Leverhulme Prize CRASSH Early Career Fellowship Isaac Newton Trust grant Senior Visiting Fellowship at University of Bologna She supervises graduate research in topics spanning women's writing , linguistic thought , translation history , and cultural marginalization in 16th-20th century Italy. Her work bridges philology, literary criticism, and social history through rigorous textual analysis.

• History of Linguistics
• Italian Language and Literature
• Gender Studies
• +6 more

Professor Aditi Lahiri is a Research Professor of Linguistics at the University of Oxford's Faculty of Linguistics, Philology and Phonetics. She holds positions in the Centre for Linguistics and Philology and directs the Language and Brain Laboratory. Her research focuses on phonology, phonetics, historical linguistics, psycholinguistics, and neurolinguistics, with special expertise in metrical theory, feature theory, and morphological complexity. Education : BA (Mathematics, Economics) from University of Calcutta (1971); MA (Comparative Philology) from University of Calcutta (1973); DPhil (Comparative Philology and Linguistics) from University of Calcutta (1979); PhD (Linguistics) from Brown University (1982). Research Interests : Her work spans experimental and theoretical linguistics, including prosodic phrasing, lexical representation, language change, and neural correlates of phonological processing. Key areas include: Metrical phonology and historical sound change Cognitive representation of speech sounds Morphological complexity in the mental lexicon Speech perception and production mechanisms Recent Research Trends : Over the past decade, her publications emphasize cross-disciplinary approaches, integrating experimental psycholinguistics with computational modeling (e.g., ERC-funded projects on morphophonological alternations). Recent work explores language processing in multilingual speakers and the neural basis of phonological features. Awards : Recipient of prestigious awards including the Leibniz Prize (2000), Max Planck Research Prize (1994), and Fellow of the British Academy (2010). She serves as Vice President of the British Academy and President of the Philological Society. Grants & Leadership : Secured over €8 million in research funding, including ERC Advanced Grants (e.g., MORPHON, WORDS). Directed the DFG-funded Sonderforschungsbereich 471 (2006-2008). Established key partnerships in India through endowed scholarships and academic outreach initiatives. Labs & Collaborations : Leads the Language & Brain Laboratory, collaborating internationally on projects combining neurolinguistic methods with historical and experimental linguistics. Supervised numerous PhD students who have become leading academics globally.

• Phonology
• Phonetics
• Historical Linguistics
• +3 more

Wim A Van der Stede is a **Professor of Accounting and Financial Management** at the **London School of Economics (LSE)**, specifically within the **Department of Accounting**. He holds the prestigious **CIMA Professorship** and serves in leadership roles such as LSE Council Member and Houghton Street Ventures Advisory Board member. His research focuses on **management control systems**, **corporate governance**, **executive compensation**, and **performance measurement**, with notable contributions to understanding incentive systems and organizational design. He has authored influential books like *Management Control Systems: Performance Measurement, Evaluation, and Incentives*, translated into multiple languages. Van der Stede’s academic leadership includes editorial roles in top journals like *Management Accounting Research* (Editor-in-Chief 2015–2024), and he has received awards such as the LSE Teaching Excellence Award (2016/17) and the AAA Outstanding International Educator Award (2019). His work integrates empirical studies with practical insights, addressing topics like budgeting, innovation selection, and the impact of information technology on organizations. He actively contributes to global accounting discourse through research, teaching, and advisory roles.

• Accounting
• Management Control Systems
• Corporate Governance
• +5 more

Alis Oancea is Professor of Philosophy of Education and Research Policy at the University of Oxford's Department of Education, part of the Social Sciences Division. She holds additional roles including ESRC Centre for Global Higher Education Deputy Director and Social Sciences Division Advocate for Responsible Engagement. With dual doctoral degrees (DPhil from Oxford and PhD from Bucharest), she also received a Doctor Honoris Causa from University of the West Timisoara. Her research focuses on meta-research, research policy, and higher education governance. Key areas include research assessment frameworks, impact measurement, ethics, open knowledge practices, and teacher education. She co-edited the 2024 Handbook of Meta-Research and leads international studies on research cultures and policies. Education: DPhil (Oxford), PhD (Buc), DipLATHE (Oxford), Dhc (UWT) Roles: Director of Research (2016-20), REF2021 Coordinator, ESRC CGHE Deputy Director Leadership: Over 30 funded projects including the RKEEI initiative and BERA Observatory Her 150+ publications span research policy, impact evaluation, and education systems. Awards include FAcSS Fellowship and numerous international advisory roles across Europe and Norway. Advises on global education reforms and chairs panels for EU, UK, and Nordic research councils. Current doctoral supervision focuses on research policy, higher education systems, and teacher education innovations. Active in editorial roles for Oxford Review of Education and Review of Education .

• Philosophy of Education
• Research Policy
• Research Assessment
• +7 more