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.
Professor Anders C. Hansen is a mathematician at the University of Cambridge and University of Oslo, leading the Applied Functional and Harmonic Analysis group. His work bridges functional analysis, artificial intelligence, and computational mathematics, focusing on the Solvability Complexity Index (SCI) hierarchy and stability issues in deep learning. He has held prestigious fellowships, including a Royal Society University Research Fellowship and Peterhouse Bye-Fellowship. Educated at the University of Cambridge, UC Berkeley, and the Norwegian University of Science and Technology Developed groundbreaking theories in compressed sensing and deep learning, revealing algorithmic instability paradoxes Organized workshops on computational mathematics and AI interpretability His research explores the SCI hierarchy , exposing computational barriers in AI, quantum mechanics, and inverse problems. Key projects include Smale’s 18th problem and analyzing neural network stability. His work has transformed understanding of compressed sensing, particularly in medical imaging. Recent scientific awards include the Whitehead Prize (2019), IMA Prize (2018), and Leverhulme Prize (2017). Collaborations span institutions like Caltech, MIT, and the University of Vienna. As an educator, he teaches NST Part IA Mathematical Methods , Part II Numerical Analysis , and a Part III course on Compressed Sensing . His group has mentored 17 PhD and postdoctoral researchers since 2012.
Andreas Vlachos is a Professor of Natural Language Processing and Machine Learning at the Department of Computer Science and Technology, University of Cambridge, and holds the Dinesh Dhamija Fellowship at Fitzwilliam College. His research spans dialogue modeling, automated fact-checking, imitation learning, semantic parsing, biomedical text mining, and trustworthiness in AI systems. PhD in Computer Science, University of Cambridge (supervised by Ted Briscoe and Zoubin Ghahramani) Lecturer at University of Sheffield Postdoctoral roles at UCL, University of Cambridge (NLIP group, Stephen Clark), and University of Wisconsin-Madison (Mark Craven) Current research focuses on evaluating and mitigating biases in language models, advancing fact-checking methodologies, and improving model robustness through interpolation, reinforcement learning, and causal reasoning. His work integrates natural logic, knowledge graphs, and multimodal evidence for verification tasks. Recent publications address uncertainty quantification, temporal planning benchmarks, and ethical framing of NLP artifacts. Grants from ERC, EPSRC, Facebook, Google, and the Alan Turing Institute fund his research team. Collaborations include Sebastian Riedel, Stephen Clark, and Mark Craven. Key projects explore disinformation detection, long-form generation, and confidence calibration in AI systems.
Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.
Sebastian Riedel is a Professor at University College London (UCL) and a Researcher at DeepMind, leading the UCL NLP Lab. His work focuses on teaching machines to read, reason, and write, integrating Natural Language Processing (NLP) with Machine Learning. He holds an Allen Distinguished Investigator award and has held roles at FAIR, UMass Amherst, Tokyo University, and the University of Edinburgh. Education: PhD in Computer Science from the University of Edinburgh (advisor: Ewan Klein), postdoctoral research at UMass Amherst (advisor: Andrew McCallum), and research at Tokyo University (advisor: Tsujii Junichi). Research Interests: NLP, machine learning, information extraction, and multimodal models like Gemini. He develops tools such as UCLEED (BioNLP event extractor), frontlets (Scala map wrappers), and thebibbrag (BibTeX to HTML converter). Awards: Allen Distinguished Investigator. Software contributions include GitHub repositories for NLP, machine learning, and data tools. Contact: s.riedel@ucl.ac.uk | Office: 1st Floor, 90 High Holborn, London WC1V 6LJ | Office Hours: Mondays 11 AM–12 PM.
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Professor Alexandra Birch serves as Chair of Multilingual Natural Language Processing at the Institute for Language, Cognition and Computation (ILCC) within the School of Informatics at the University of Edinburgh. She is a leading member of the StatMT group and Edinburgh Natural Language Processing group, while also co-founding and serving as Chief Scientist of Aveni.ai, a fintech company delivering GenAI solutions to the UK finance sector. Her research centers on advancing multilingual natural language processing with applications that improve people's lives. Professor Birch has made significant contributions to the paradigm shift toward neural networks and large language models in NLP, with expertise spanning multilingual and multimodal processing, ethical considerations, model explainability, and computational efficiency. Her work addresses critical challenges in making language technologies accessible across diverse linguistic contexts. Professor Birch's publication record includes over 100 peer-reviewed papers that demonstrate strong trends in multilingual machine translation, low-resource language processing, and ethical AI frameworks. Her research bridges theoretical advances with practical implementations, particularly evident in her current leadership of the FinLLM project for the financial sector. As a research leader, she has secured and directed major EU-funded projects including EuroLLM (developing a large language model for European languages), UTTER (creating a multilingual meeting assistant), and GoURMET (focusing on low-resource machine translation for media content). Her industry collaboration through Aveni.ai demonstrates successful translation of academic research into commercial applications, particularly in the financial services domain. Professor Birch maintains active leadership in both academic and industry communities, with her current work on domain-specific language models representing an important direction in adapting general-purpose LLMs to specialized professional contexts while addressing multilingual challenges.
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.
Marianna Patrick is a Research Fellow at the School of Education, Learning and Communication Sciences (SELCS), University of Warwick. She holds a PhD in Intercultural Communication (2025), MA in Global Communication from the Chinese University of Hong Kong (2013), and BA (Hons) in Politics and International Studies from Warwick University (2012). Her research focuses on generative AI in academic writing, migration narratives, and intercultural communication. She leads a Leverhulme Trust-funded project on AI chatbots' impact on academic writing practices, collaborating with the University of Edinburgh and Sophia University Tokyo. Research & Education: Her doctoral work explored language and place construction in serial migration narratives, supervised by Prof. Stephanie Schnurr and Prof. Steve Mann. She also contributed to Monash-Warwick Alliance projects on multicultural cricket team inclusivity. She teaches professional skills and research methods across Computer Science and Applied Linguistics departments, supervising MSc dissertations in intercultural communication. Awards & Engagement: Received the People's Choice Prize at the Warwick Research Festival. Actively promotes interdisciplinary collaboration through initiatives like the Social Sciences Research Exchange Group (co-founder) and chairs the Applied Linguistics PGR Student-Staff Liaison Committee. She also contributed to EDI efforts as part of the Athena Swan team and served as Assistant Director of Student Experience and Progression for the Faculty of Arts.
Professor Eric Atwell is a Professor of Artificial Intelligence for Language at the University of Leeds' School of Computer Science, part of the Faculty of Engineering and Physical Sciences. He holds additional roles as a LITE Fellow at the Leeds Institute for Teaching Excellence (40% part-time), Turing Fellow at the Alan Turing Institute, and member of the Leeds Institute for Data Analytics (LIDA) and Language at Leeds (LATL). His research focuses on AI applications in corpus linguistics, text analytics, and computational analysis of religious and medical texts, with a strong emphasis on Arabic and Islamic studies. He leads the AI4L research group and has supervised over 60 research students and fellows, many of whom have pursued careers in academia, tech, and AI-driven fields. Education: PhD in Corpus Linguistics and Language Learning (University of Leeds, 2008), BA (First Class) in Computing and Linguistics (Lancaster University, 1981) Grants: Includes EPSRC-funded projects like Natural Language Processing with Arabic and Islamic Studies (£337K, 2013-2015) and EDUBOTS chatbots for education (£94K, 2019-2022) Teaching: Leads modules in Data Mining, Text Analytics, and AI across multiple programs, including online Masters and PhDs. Known for innovative teaching approaches and student support, receiving positive feedback for engagement and course design. Research Interests: AI applied to corpus linguistics, Quranic text analysis, Arabic NLP, chatbots for education, and decolonizing curricula. Notable projects include Quranic semantic search tools, Hadith corpus analysis, and AI-driven fact-checking systems. Publications: Over 277 publications, with recent work focusing on Quranic QA systems, Arabic dialect identification, and generative AI applications in education. His research is widely cited, earning recognition from ResearchGate for high readership. Awards: Recognized for his most-read research items on ResearchGate (June 2021). Gallup StrengthsFinder highlights his top traits as Learner, Achiever, Ideation, Intellection, and Maximizer. Labs/Teams: Leads the AI4L research group and collaborates with international institutions including SUSTECH Sudan, King Saud University, and SWJT University (China). Active in research networks like LIDA and LATL.
Rogério de Lemos is a Senior Lecturer in Computing Science and Director of Postgraduate Research (PGR) at the School of Computing, University of Kent. He previously served as an invited assistant professor at the University of Coimbra, Portugal, and as a Senior Research Associate at the Centre for Software Reliability (CSR) at the University of Newcastle upon Tyne, UK. Dr. de Lemos' research focuses on architecting resilient systems, particularly in resilient AI, self-adaptive software systems, and authorization infrastructures. He belongs to both the Programming Languages and Systems Group and the Cyber Security Group at the University of Kent. His specific research interests include: Software engineering for self-adaptive systems assurances and resilience evaluation Dynamic generation of processes Handling insider threats using self-adaptive authorization Architectural abstractions for fault tolerance Verification and validation of dependable software architectures Software development for safety-critical systems Dependability and bioinspired computing His publication trends show increasing emphasis on practical applications of self-adaptive systems in cyber security contexts, with recent work spanning network traffic analysis, cryptographic function detection, and cloud-edge security architectures. His research bridges theoretical foundations with practical implementations, particularly in cyber security and resilient systems architecture. Dr. de Lemos currently leads the "Collaborative and Confidential Information Sharing and Analysis for Cyber Protection" project funded by the European Union's Horizon 2020 Programme. His past projects include "ADAAS: Assuring Dependability in Architecture-based Adaptive Systems" and multiple collaborations with NCR on sensor fusion and fault tolerance. As Director of Postgraduate Research, he oversees the School of Computing's research degree programs and likely supervises PhD students in resilient systems and cyber security, though specific student names are not listed in the available information.
Dr Anandadeep Mandal is an Associate Professor in Finance and the Scotcoin Distinguished Chair of Digital Finance at the University of Birmingham , within the Birmingham Business School and the Department of Finance . He is the founding director of the MSc Financial Technology programme and the Programme Director for the MBA (Distance Learning), demonstrating significant leadership in academic program development. Education: PhD in Probability Distribution Fitting, Cranfield University (2016) MRes in Management Science, Cranfield University (2012) MSc in Finance and Investments, Durham University (2008) Bachelor’s in Electronics Engineering Research Interests: Dr Mandal’s interdisciplinary research lies at the intersection of mathematical modelling, artificial intelligence, finance, and digital innovation . His work focuses on AI-enabled investment strategies , blockchain for financial transparency , ESG performance measurement , and the development of the Sustainable Efficiency Index (SEI) . He also pioneers AI applications in digital education , including a patent-pending platform for automated grading of multi-modal student submissions using ensemble AI methods. Publication Trends: His recent scholarly output spans high-impact journals and conferences, reflecting a strong focus on digital finance , climate and social media analytics , cryptocurrency regulation , and AI in financial forecasting . His work combines advanced data science techniques with real-world policy and financial applications, particularly in sustainability and public health. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: Dr Mandal has secured over £2 million in research funding from sources including UKRI, UoB QR Funding, and industry partners. While specific students are not listed, his role as programme director and research leader suggests active mentorship. His research has direct policy impact through collaborations with the NHS Trusts , NIHR , and the UK Government . Labs, Teams, and Impact: Dr Mandal leads a research agenda that bridges academia and public policy. His work extends beyond the university through public engagement at science festivals, outreach for young learners, and expert contributions to UK Parliamentary consultations on AI, sustainability, and financial innovation. He is a key figure in advancing digital finance education and research at the University of Birmingham.
Dr. Zheng Yuan is an Associate Professor (Senior Lecturer) in the School of Computer Science at the University of Sheffield. Previously, they held roles as an Assistant Professor at King's College London and a Research Associate at the University of Cambridge's Department of Computer Science and Technology. Their primary research focuses on machine learning and deep learning applications in natural language processing (NLP), particularly in educational technology, healthcare, creativity, and multilingual contexts. Key projects include computer-assisted language learning (CALL), human-centered NLP in education, computational code-switching, and creative AI. Education includes a PhD and MPhil in Natural Language Processing from the University of Cambridge, and a BSc(Eng) from Queen Mary University of London. They hold affiliated positions at the University of Cambridge, King's College London, and are a Fellow of Trinity College, Cambridge. They contribute to The Alan Turing Institute's Data-Centric Engineering Programme and hold FHEA status (2024-). Research interests span educational NLP, multilingual systems, transfer learning, and explainable AI. They actively organize workshops and serve on editorial boards (e.g., PeerJ Computer Science) and conference committees (ACL/EMNLP). Recent activities include co-organizing NLP workshops at ACL 2025 and NAACL 2024, alongside roles in professional societies like the ACL Professional Conduct Committee. Awards include Fellowship of the Higher Education Academy (2024-) and ASEFClassNet18 Faculty Collaboration (2025-). They welcome PhD applications in NLP and machine learning, emphasizing interdisciplinary applications.
Zaiqiao Meng is a Lecturer (Assistant Professor) at the University of Glasgow's School of Computing Science, affiliated with the Information Retrieval Group and IDA section. He also holds an Affiliated Lecturer position at the University of Cambridge's Language Technology Lab. His research focuses on the intersection of machine learning, knowledge graphs, and NLP, particularly in biomedical applications. Key areas include AI agents, large language models, and healthcare informatics. Current roles include co-leading the Glasgow AI4BioMed Lab, which develops AI solutions for biomedical knowledge extraction. He has extensive postdoctoral and visiting research experience, including at KAUST's MINE lab. Meng has published widely in top conferences like ACL and EMNLP, with over 40 publications since 2019. His work spans topics such as drug-target interaction prediction, clinical summarization, and knowledge graph construction. Teaching includes courses on Recommender Systems and Data Science at both undergraduate and graduate levels. He advises multiple PhD students on projects involving LLMs, biomedical entity representation, and conversational agents.
Oya Celiktutan is a Reader (Associate Professor) at King’s College London in the Department of Engineering, leading the Social AI & Robotics (SAIR) Lab within the Centre for Robotics Research. She holds a BSc in Electronics Engineering from Uludag University, and an MSc and PhD in Electrical and Electronics Engineering from Bogazici University, Turkey. Her doctoral work included a visiting research period at the National Institute of Applied Sciences in Lyon, France. She has held postdoctoral positions at Queen Mary University London, the University of Cambridge, and Imperial College London before joining King’s College in 2018. Her research focuses on multimodal machine learning for autonomous agents, addressing challenges in human-robot interaction, social awareness, and navigation. Key areas include human behavior analysis, generative models, and continual learning. Her work is supported by EPSRC, The Royal Society, EU Horizon, and industry partners like Toyota and NVIDIA. Notable awards include the EPSRC New Investigator Award (2021) and a Best Paper Award at IEEE Ro-Man 2022. Dr. Celiktutan’s lab explores socially assistive robotics, extended reality systems, and ethical AI. Recent projects include the LISI initiative to enable robots to learn social interactions from human-human data and the CL-HRI project advancing continual learning in human-centric robotics. Her research has led to datasets like MHHRI and RICA, and collaborations with companies like SoftBank Robotics. She teaches modules on Sensing and Perception, and Sensors and Actuators, emphasizing practical applications in robotics and mechatronics. Current research interests span socially-aware navigation, algorithmic fairness, and multimodal data fusion for robotics in healthcare and urban environments.
