Rachel Bernhard is Associate Professor of Quantitative Political Science Research Methods at Nuffield College and the University of Oxford. Before joining Nuffield, she was an Assistant Professor at the University of California, Davis, and a Postdoctoral Prize Fellow in Politics at Nuffield. She holds a PhD from UC Berkeley.
Her research focuses on appearance-based discrimination in politics — how candidate appearance shapes voter preferences and how parties select candidates. She is also a methodologist working on survey experiments and text-as-data approaches.
Ilya Boytsov is an applied Deep Learning Scientist and NLP Lead at Wayfair in Berlin. His main professional interests include information retrieval, aspect-based sentiment analysis, and generative AI. He has extensive experience designing machine-learning bootcamps and lectures for diverse audiences.
Ilya has spoken at conferences including the World Data Summit and DSC Europe. He is also co-founder of the Street Smart AI community in Berlin.
Maksim Zubok is a doctoral candidate in Politics at Oxford University, Nuffield College. His dissertation explores ways of harnessing LLMs for social science research, from classic data labelling to using models as condensed snapshots of the internet to study how people organise relationships between concepts and form beliefs about the world.
He has helped organise several academic events, including previous sessions of the Oxford LLM workshop and the Oxford Summer Institute for Computational Social Science.
Dr Mikhail Burtsev is a Landau AI Fellow at the London Institute for Mathematical Sciences. He studied microelectronics before completing his PhD in computer science at the Keldysh Institute of Applied Mathematics, and has held visiting positions at Cambridge.
As Scientific Director of the AI Research Institute in Moscow, he led development of the DeepPavlov conversational AI framework. His research focuses on continual learning, memory-augmented neural networks, and AI-assisted mathematics.
Emeli Dral is Co-founder and CTO at Evidently AI, a startup developing open-source tools to evaluate, test, and monitor machine learning models and LLM-based systems.
She previously co-founded an industrial AI startup and served as Chief Data Scientist at Yandex Data Factory, leading over 50 applied ML projects across industries. Emeli co-authored a Coursera machine learning and data analysis curriculum with over 100,000 students enrolled.
Grigory Sapunov is CTO and co-founder of Intento. He has over 20 years of software engineering experience, including around 15 years in data analysis, AI, and machine learning. Since 2011 he has focused on deep learning.
Grigory is a Google Developer Expert in Machine Learning and holds a PhD in Artificial Intelligence. He is a frequent speaker at ML conferences and community events.
Tatiana Shavrina works on the Llama team at Meta and has previously worked at Snap and AIRI. She is passionate about open source and multilingualism in LLMs, especially for under-resourced languages.
She contributed to BLOOM as lead for interpretability, led development of the mGPT multilingual model, and worked on Russian SuperGLUE and low-resource NLP methods. See her Google Scholar profile for publications.
Sergei Skvortsov is Lead Machine Learning Engineer at Nebius, focused on efficient training and inference for large language models. He regularly lectures on efficient inference at the Nebius Academy.
Previously, he led the inference team at Yandex Self Driving Group, where his team built the main inference engine powering neural models for autonomous cars and robots.
Ray Duch is the co-founder and Director of the Centre for Experimental Social Sciences (CESS) at Nuffield College. He has established CESS centres in Chile, China, and India, and serves as co-Director of the Candour Project and co-PI for the REAL Demand Centre.
His work uses experimental methods to study decision making in politics, finance, health, and economics, and has appeared in the American Political Science Review, Journal of Politics, and Proceedings of the National Academy of Sciences.
Charles Rahal is Associate Professor in Computational Social Science at the University of Oxford, affiliated with the Demographic Science Unit and the Leverhulme Centre for Demographic Science.
His research develops methods to uncover patterns in large-scale observational data with a focus on equality and equity. He contributed to the UK government's Covid-19 policy response and leads the Metrics and Models lab.
Joan Timoneda is Assistant Professor of Political Science at Purdue University. He received his PhD from the University of Maryland and previously held a postdoctoral position at Duke University.
His research focuses on authoritarian regimes, democratic backsliding, and how leaders use digital tools and social media. His methods work applies large language models to core questions in comparative politics. More details on his personal website.
Brandon Stewart is Professor of Sociology at Princeton University. His research develops quantitative methods for the analysis of text as data, with a focus on making these tools accessible to social scientists.
He is the creator of the Structural Topic Model (STM), a widely-used method for analysing document collections that allows researchers to incorporate metadata into topic models. He joined the 2025 school as part of a joint session with the Metrics and Models seminar series.
