Speaker |
Qingyuan Zhao |
|---|---|
Affiliation |
University of Cambridge |
Date |
Friday, 08 March 2024 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Experimental design was introduced nearly a century ago and embodied the dawn of modern statistics. Today, design is more broadly understood as the process of data collection/preparation and is intimately related to the concept of causal identification. Strikingly, design is largely missing in the current development and discussion of AI. I will share some stories based on research from my group and collaborations, in hope that they will help to promote the awareness of design in the interdisciplinary field of data science. The research works I will discuss range from applied analyses of COVID-19, policing, and biodiversity conservation to theoretical foundations of randomized experiments and causal graphical models, but they share one common theme: design trumps analysis. |
Biography |
Qingyuan Zhao is an Associate Professor of Statistics in the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics (DPMMS) at University of Cambridge, a Fellow of the Corpus Christi College, and an Associate Faculty of the Cambridge Centre for AI in Medicine (CCAIM). He is interested in improving the quality and appraisal of statistical research, including new methodology and a better understanding of causal inference, novel study designs, sensitivity analysis, multiple testing, and selective inference. |