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Statistics and Data Science Seminar Series

The Department of Statistics hosts the Statistics and Data Science Seminar Series (SDSS) throughout the year and usually taking place on Friday afternoons at 2pm. Topics include statistics, machine learning, computer science and their interface, both from a theoretical and applied point of view. We invite both internal and external speakers to present their latest cutting edge research. All are welcome to attend our seminars!

Autumn Term 2024 

Friday 4 October 2024, 2-3pm - Tom Everitt (Google DeepMind)

Tom Everitt

This event will take place in COL.1.06.

Title: Robust Agents Learn Causal World Models

Abstract: It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. However, it is not known if agents must learn causal models in order to generalise to new domains, or if other inductive biases are sufficient. We answer this question, showing that any agent capable of satisfying a regret bound under a large set of distributional shifts must have learned an approximate causal model of the data generating process, which converges to the true causal model for optimal agents. We discuss the implications of this result for several research areas including transfer learning and causal inference. 

Biography: Tom Everitt is a Staff Research Scientist at Google DeepMind leading the Causal Incentives Working Group. His work is on AGI Safety, i.e. how we can safely build and use highly intelligent AI. His PhD thesis, Towards Safe Artificial General Intelligence, is the first PhD thesis specifically devoted to this topic. Since then, he has been building towards a theory of alignment based on Pearlian causality.

Friday 25 October 2024, 2-3pm - Dennis Lin (Purdue University)

Dennis Lin

This event will take place in (TBA).

Title: TBA

Abstract: TBA

Biography: Dr. Dennis K. J. Lin is a University Distinguished Professor and Head of the Statistics Department at Purdue University. His research interests are quality assurance, industrial statistics, data mining, and data science. He has published near 250 SCI/SSCI papers in a wide variety of journals. He currently serves or has served as associate editor for more than 10 professional journals and was co-editor for Applied Stochastic Models for Business and Industry. Dr. Lin is an elected fellow of ASA, IMS, RSS and ASQ, an elected member of ISI, and a lifetime member of ICSA. He is an honorary chair professor for various universities, including Renmin University of China (as a Chang-Jiang Scholar), Fudan University, and National Chengchi University (Taiwan). His recent awards include the 2004 Faculty Scholar Medal Award (Penn State), the Youden Address (ASQ, 2010), the Shewell Award (ASQ, 2010), the Don Owen Award (ASA, 2011), the Loutit Address (SSC, 2011), the Hunter Award (ASQ, 2014), the Shewhart Medal (2015), and the SPES Award at the Joint Statistical Meeting (2018).  He will be the 2020 Deming Lecturer at JSM at Philidelphia.