People 2025
Meet Oxford LLMs 2025 Team!
Here, you will find information about workshop organisers and lecturers who are leading this year’s sessions. Beyond the core sessions, we are also inviting researchers and research teams to come and share with you their current research ideas and projects at the intersection of NLP and social sciences. Stay tuned, as we will be updating this page with additional speakers as we receive their confirmations!
Organisers
Rachel Bernhard (Co-Organiser)
DPIR, Nuffield College, Oxford University
Rachel Bernhard is Associate Professor of Quantitative Political Science Research Methods at Nuffield College and the University of Oxford. Before joining Nuffield, she served as an Assistant Professor of Political Science at the University of California, Davis. She holds a Ph.D. in political science from the University of California, Berkeley, and previously was a Postdoctoral Prize Fellow in Politics at Nuffield.
Her current research focuses on appearance-based discrimination in politics. She has recently taught courses on political psychology and public policy, identity politics, statistics and research design, women in politics, and computational methods. To learn more about Rachel’s research and teaching, visit her website.
Ilya Boytsov (Lecturer, Co-Organiser)
NLP Lead, Wayfair
Ilya is an applied Deep Learning Scientist with a focus on Natural Language Processing (NLP). He currently works as the NLP lead at Wayfair in Berlin. His main professional interests include information retrieval, aspect-based sentiment analysis, and generative AI. In addition to his applied research work, Ilya has extensive experience in teaching and public speaking. He designed lectures and machine-learning bootcamps for various audiences, including students, managers, and individuals without prior coding experience. Ilya was a speaker at several ML conferences in Europe, including World Data Summit and DSC Europe. He is also a co-founder of the Street Smart AI community in Berlin where AI practitioners share their knowledge of making ML&DS happen for real-life applications. You can connect with Ilya and read more about his research on his personal website.
Maksim Zubok (Co-Organiser)
DPIR Oxford University
Maksim is a doctoral candidate in Politics at Oxford University, Nuffield College. In his dissertation, Maksim explores various ways of harnessing LLMs for social science research. Those range from classic data labelling to using the models as condensed snapshots of the internet from which we can learn how people organise relationships between concepts and thus form beliefs about the world they live in. Maksim has a longstanding interest in teaching and facilitating intellectual exchange. He has helped organise several academic events, including previous sessions of Oxford LLM workshop and the Oxford Summer Institute for Computational Social Science in 2022.
Confirmed Speakers
Mikhail Burtsev (Lecturer)
Arnold & Landau AI Fellow at the London Institute for Mathematical Sciences
Dr Mikhail Burtsev is a Landau AI Fellow at the London Institute. He studied microelectronics at the Moscow Power Engineering Institute, before doing his PhD in computer science at the Keldysh Institute of Applied Mathematics. He held senior research positions at the Anokhin Institute of Normal Physiology and later the Kurchatov Institute, and visiting research positions at Cambridge. He was Scientific Director of the Artificial Intelligence Research Institute in Moscow, and set up and ran the Neural Nets and Deep Learning Laboratory at the Moscow Institute of Physics and Technology. Under his leadership, it developed the award-winning open-source conversational AI framework, DeepPavlov. Dr Burtsev researches the mathematics behind more intelligent AI, including continual learning and memory augmented neural networks, as well as AI assisted maths. To learn more about Mikhail’s research, visit his website.
Emeli Dral (Lecturer)
Co-founder and CTO at Evidently AI
Emeli Dral is a Co-founder and CTO at Evidently AI, a startup developing open-source tools to evaluate, test, and monitor the performance of machine learning models. Earlier, she co-founded an industrial AI startup and served as the Chief Data Scientist at Yandex Data Factory. She led over 50 applied ML projects for various industries - from banking to manufacturing. Emeli is a data science lecturer at Harbour.Space University, and a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students.
Grigory Sapunov (Lecturer)
CTO and co-founder of Intento
Prior to Intento, Grigory worked in industry at Yandex and in academia at the Higher School of Economics in Russia. He has over 20 years of experience in software engineering, including about 15 years in data analysis, artificial intelligence, and machine learning. Since 2011, he has been engaged in deep learning. Grigory is a Google Developer Expert in Machine Learning and holds a Ph.D. in Artificial Intelligence.
You can connect with Grigory on LinkedIn.
Tatiana Shavrina (Lecturer)
Research Scientist Manager, Meta, Llama team
Tatiana is passionate about open source and multilingualism in LLMs, under-resourced languages, and bringing them to rich resource environments. As an enthusiast of various benchmarking methods, she has contributed to BLOOM as the lead for interpretability, led the mGPT model development, and contributed to low-resource NLP methods. Her main projects include mGPT, Russian SuperGLUE, and BLOOM. To learn more about Tatiana’s research, visit her Google Scholar profile.
Sergei Skvortsov (Lecturer)
Lead MLE at Nebius
Sergei is a Lead Machine Learning Engineer at Nebius with a passion for the efficient training and inference of Large Language Models. Before joining Nebius, he led the Inference team at Yandex Self Driving Group. In that role, his team developed the main inference engine that powered all neural network models on the company’s autonomous cars and robots. Sergei enjoys sharing his expertise, having lectured on efficient inference at the Nebius Academy.
Social Science Researchers
Raymond Duch
Director of the Centre for Experimental Social Sciences, Nuffield College, Oxford University
Ray Duch is the co-founder and Director of the Centre for Experimental Social Sciences (CESS) at Nuffield College University of Oxford. He established and directed similar CESS centres in Chile, China, and India. He is also co-Director of the Candour Project and a co-PI for the Nuffield Department of Population Health REAL Demand Centre. Ray Duch directs the Talking to Machine project that explores how to leverage advances in Large Language Models to enhance social science research including the design and implementation of experiments and how we conduct public-opinion research. His research focuses on the application of experimental methods to understanding individual decision making related to politics, finance, health, and economics. His publications have appeared in leading social science journals including, American Political Science Review, Proceedings of the National Academy of Sciences, Journal of Economic Behavior and Organization, American Journal of Political Science, Political Analysis, Applied Economics, Journal of Politics, and Nature Medicine. Ray Duch holds a visiting appointment at IAST-Toulouse School of Economics and has had visiting appointments at Stanford Graduate School of Business, WZB-Berlin, Université de Montréal, and Pompeo-Fabra University, Barcelona. His research is funded by leading social science funding agencies including the NSF, ESRC, FONDECYT, Swiss National Science Foundation, the Health Foundation and SSHRC. He frequently advises governments, international organizations, law firms and corporations.
You can read more about Ray’s work on his website.
Kosuke Imai
Professor in the Department of Government and the Department of Statistics at Harvard University
Kosuke Imai (pronounced Kō´·skā) is Professor in the Department of Government and the Department of Statistics at Harvard University. He is also an affiliate of the Institute for Quantitative Social Science. Before moving to Harvard in 2018, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. Imai specializes in the development of statistical methods and machine learning algorithms and their applications to social science research. His areas of expertise include causal inference, computational social science, and survey methodology. Imai leads the Algorithm-Assisted Redistricting Methodology Project (ALARM) and served as an expert witness for several high-profile legislative redistricting cases. In addition, he is the author of Quantitative Social Science: An Introduction (Princeton University Press, 2017). Outside of Harvard, Imai served as the President of the Society for Political Methodology from 2017 to 2019.
His current research interests include: data-driven policy learning and evaluation, causal inference with high-dimensional and unstructured treatments (e.g., texts, images, videos, and maps), GenAI and causal inference, human and algorithmic decision-making, fairness and racial disparity analysis, algorithmic redistricting analysis, data fusion and record linkage, census and privacy.
Charles Rahal
Associate Professor in Computational Social Science at the University of Oxford
Charles is an associate professor in computational social science at the University of Oxford, where he works with colleagues at the Demographic Science Unit and Leverhulme Centre for Demographic Science (where he is part of the Senior Management Board). He was previously a British Academy Postdoctoral Fellow. Charles’s research focuses on methodological innovations which uncover patterns in large-scale observational data with a focus on equality and equity. It is usually motivated by a desire to improve policies and public administration. This most recently includes but is not limited to population-wide scientometric analysis, model evaluation in machine learning, and computational approaches to the life course (broadly defined). Charles has recently been involved in several successful funding applications (totalling around £12m) and has published in many of the world’s leading journals. He predominantly works in Python, Bash and TeX, and takes great pride in being able to generate policy impact - having won awards and commendations for contributions to the UK government Covid-19 policy response - all through open and reproducible research. Charles recently launched a Metrics and Models lab, which has an open seminar series component to it and is open for everyone to attend!
Joan Timoneda
Assistant Professor of Political Science at Purdue University
Joan Timoneda is an Assistant Professor of Political Science at Purdue University. He received his Ph.D. from the Department of Government and Politics at the University of Maryland, College Park, in 2019, specializing in comparative politics and data science. Prior to joining Purdue, he was a Postdoctoral Associate at Duke University. His substantive research focuses on authoritarian regimes and democratic backsliding, with particular attention to the dynamics of personalism in dictatorship and democracy in the digital age. His methods work applies big data techniques and novel natural language processing algorithms to core questions in comparative politics, such as the evolution of Rafael Trujillo’s personalist rule in the Dominican Republic or the ways in which democratically elected leaders subvert democracy using social media. Joan’s current methods work focuses heavily on technical and applied aspects of large language models (LLMs) in the social sciences. You can read more about Joan’s work on his personal website.