Lenny Smith

Lenny Smith is a distinguished Professor of Statistics at the London School of Economics and Political Science (LSE), where he also directs the Centre for the Analysis of Time Series (CATS). Additionally, he serves as a Senior Research Fellow at Pembroke College, Oxford. With a PhD in Physics from Columbia University (1987), his career spans prestigious institutions including École Normale Supérieure, Warwick, and Potsdam University.

• Academic Roles: LSE Professor of Statistics, CATS Director, Pembroke College Senior Research Fellow
• Education: PhD in Physics (Columbia University, 1987); Undergraduate in Physics, Mathematics, and Computer Science (University of Florida)
• Research Focus: Climate modeling, ensemble forecasting, uncertainty quantification, chaos theory, and applications to disaster risk reduction
• Grants: Funded by ONR, NOAA, EPSRC, NERC, European Commission, and UK Research Councils
• Key Projects: NAPSTER (NERC), DIME and REMIND (EPSRC), THORPEX strategic planning
• Scientific Recognition: Royal Meteorological Society Fitzroy Prize (2003), Selby Fellowship (Australian Academy of Sciences)
• Students Supervised: Roman Frigg, David Stainforth, Emma Suckling, Falk Niehörster, H. Du, T. Maynard, S. Higgins, A. Jarman
• Media Presence: Quoted in Nature, New Scientist, BBC, Financial Times, and The Daily Telegraph

Smith's work bridges rigorous mathematical analysis with practical applications, notably in climate change economics and weather risk management. His research on ensemble forecasting and probabilistic skill has shaped methodologies for evaluating climate models. He actively contributes to public science through his book A Very Short Introduction to Chaos and media appearances, emphasizing the importance of scientific uncertainty in policy decisions.

His publications reveal a consistent focus on dynamical coherence, model error analysis, and weather derivatives, with recent work exploring multi-model cross-pollination and predictability limits. Smith's interdisciplinary approach combines nonlinear dynamics, statistical physics, and hydrological modeling to address real-world climate challenges.