Webinar Series

Anders Bredahl Kock is currently an Associate Professor at the University of Oxford, Aarhus University and CREATES. He is also a Fellow at St. Hilda's College. His research interests are econometrics, statistics and time series analysis with emphasis on high-dimensional models.

He is a Professor of Politics and Data Science at New York University. He is the Deputy Director and the Director of Graduate Studies (MSDS) at the Center for Data Science, and Chair of the Executive Committee of the Moore-Sloan Data Science Environment. He specialize in political methodology and legislative behavior, with an interest in the application of text-as-data/NLP, Bayesian statistics, machine learning, item response theory and generalized linear models in political science.

Baylis is Full Professor, Agricultural and Consumer Economics, University of Illinois . She helps stakeholders design agricultural, conservation, and trade policy to promote ecosystem preservation and international food security. In 2001/02, she was the staff economist in charge of agriculture and forestry for the Council of Economic Advisors in the White House, and in the mid-1990s, she worked as Executive Secretary with the National Farmers Union in Canada. She has helped bring in over $28 million in grants and has successfully advised and graduated over 20 graduate students. She has published over 40 journal articles and book chapters on agriculture, forestry, trade and environmental policy. She has also coauthored a textbook on Canadian and U.S. agricultural policy.

He is an assistant professor and environmental economist at the Vancouver School of Economics and a member of the Centre for Food, Resource and Environmental Economics (CFREE). He study how people respond to environmental threats like climate change, air pollution, and wildfires. Some of his recent work includes estimating the impact of temperature on tweets to understand preferences for climate change, assessing the implicit subsidy of federal wildfire suppression, projecting how climate change will alter peak electricity demand, and considering how consumers respond to defaults in electricity consumption. His favorite projects use large datasets, natural language processing, spatial information, or all three.

Stan Matwin is a Canada Research Chair (Tier 1) at the Faculty of Computer Science, Dalhousie University, and the Director of the Institute for Big Data Analytics. He is also Emeritus Distinguished Professor of Computer Science at the University of Ottawa, and a Professor in the Institute of Computer Science of the Polish Academy of Sciences. His research focuses areas of Machine Learning, text mining, big data and data privacy.

He is an assistant professor of Operations, Information, and Technology at the Stanford Graduate School of Business, and an assistant professor of Statistics (by courtesy). His research focuses on adapting ideas from machine learning to statistical problems that arise in scientific applications. He is particularly interested in causal inference, non-parametric statistics, uses of subsampling for data analysis, and empirical Bayes methods. He got support from a Facebook Faculty Award, the Global Climate and Energy Project, and Stanford Human-Centered AI. He is currently serving as an associate editor for Biometrika and the Journal of the American Statistical Association (Theory and Methods).