People
Faculty
Maria del Rio-Chanona
Dr Maria del Rio-Chanona is a Lecturer (Assistant Professor) at University College London in the Computer Science department. Her research draws from network science, Large Language Models (LLM), as well as Agent-Based Models (ABM) to study the economic and employment impacts of the net-zero transition, the Covid-19 pandemic, and (Gen)AI. Maria completed her PhD in mathematics at the University of Oxford, where she was part of the complexity economics group of the Institute for New Economic Thinking, Oxford Martin School. She was a JSMF research fellow at the Complexity Science Hub, Vienna and a visiting scholar at the Harvard Kennedy School. Maria has worked alongside international policy organisations, including the International Monetary Fund, the World Bank, and the International Labour Organization.
Silvia Bartolucci
Dr Silvia Bartolucci is an Associate Professor in the Department of Computer Science at University College London (UCL), where she is part of the Financial Computing and Analytics Group. Her research leverages network, statistical physics and data-driven modelling tools to investigate critical behaviour in socio-economic systems, assessing protocols’ design and applications of blockchain technologies, monitoring trends and issues in traditional and decentralized financial markets. Before joining UCL, Dr. Bartolucci was a Research Associate in the Department of Finance at Imperial College Business School, working within the Centre for Financial Technology. She holds a Ph.D. in Applied Mathematics from King's College London and a background in Theoretical Physics from Sapienza University in Rome.
Paolo Barucca
Dr Paolo Barucca is Associate Professor at UCL Computer Science. He obtained his PhD in Theoretical Physics from Sapienza University of Rome. He works on Statistical Physics of Complex Systems, Statistical and Machine Learning in Finance and Economics. He collaborates with private and public institutions for teaching and research activities on machine learning and statistical data science in finance and economics applied to risk assessment and predictive modeling.
Alejandro Hermida Carrillo
Dr Alejandro Hermida Carrillo is an Imperial College Research Fellow (ICRF) at Imperial College Business School. Alejandro is passionate about understanding how different components of the self – such as personality, attitudes and beliefs – simultaneously shape and respond to socio-economic structures. In his fellowship, he studies the rise of pathological leadership styles using digital trace data and Natural Language Processing. Before joining Imperial, Alejandro completed a PhD in Management at the LMU Munich, Germany, and degrees in Psychology (MSc, LMU Munich and Licenciatura, UNAM). He was also a visiting researcher at the Computational Culture Lab of Stanford Graduate School of Business.Speakers
Doyne Farmer
J. Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking and Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment, University of Oxford. He is also External Professor at the Santa Fe Institute and CEO and Chief Scientist at Macrocosm. His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology. His book, Making Sense of Chaos: A Better Economics for a Better World, was published in 2024. During the 1980s he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 1970s he built the first wearable digital computer, which was successfully used to predict the game of roulette.
Sanaz Talaifar
Sanaz Talaifar is an Assistant Professor in Organisational Behaviour at Imperial College London. She studies identity and its intersections with politics and technology using a multi-method approach that combines traditional methods (surveys, experiments) with novel methods (mobile sensing, experience sampling). She is particularly interested in theory-method synergy, wherein novel methods inform the development of theory and theoretical insights inform the development of novel methods. Previously, Sanaz was a Postdoctoral Scholar in Organizational Behavior at Stanford University and received her PhD in Social and Personality Psychology from the University of Texas at Austin. Outside of academia, she has worked in people analytics at Google and in executive search at Egon Zehnder.