We are very happy to have two exciting keynotes, both with experience in the academic world and in collaboration with practitioners.
Assistant Professor at the University of Amsterdam
She received her PhD at the VU Amsterdam on logics for causal inference under uncertainty in 2017, focusing on learning causal relations jointly from different experimental settings, especially in the case of latent confounders and small samples. After a year in IBM Research NY as a postdoc, she joined the MIT-IBM Watson AI Lab in 2019 as a Research Scientist, where she has been working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. Her current focus is on causality-inspired machine learning, i.e. applications of causal inference to machine learning and especially transfer learning, and formally safe reinforcement learning.
Professor at Stanford University
He has held tenured positions at UCLA, UC Berkeley, and Harvard University before joining Stanford in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences from experimental and observational data. He has published extensively in the leading economics and statistics journals. Together with Donald Rubin he has published a book, ”Causal Inference in Statistics, Social and Biomedical Sciences”. Guido Imbens is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, and the American Statistical Association. He holds an honorary doctorate from the University of St. Gallen. In 2017 he received the Horace Mann medal at Brown University, in 2021 he was awarded the Nobel Memorial Prize in Economic Sciences, jointly with David Card and Joshua Angrist. Currently Imbens is Editor of Econometrica.