Speaker: Prof. Yang Chen, University of Michigan, Ann Arbor, USA
Wednesday June 29, 2022, at 11.30 am Chilean time
• Title of the seminar: Statistical and machine learning approaches to solar flare forecasting
• Abstract: Solar flare prediction is an important task, which impacts national power grid robustness, satellite signal stability, and safety of astronauts’ exploration of space. In recent years, the solar flare prediction problem has caught more attention among the machine learning and statistics community. In this talk, I will give an introduction on our efforts on solar flare forecasting with existing and innovative machine learning and statistical models. We obtain highly competitive results for flare forecasting with highly interpretable models, which contributes both to operational use and the science itself.
Yang Chen received her Ph.D. (2017) in Statistics from Harvard University and joined the University of Michigan as an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute of Data Science (MIDAS). She received her B.A. in Mathematics and Applied Mathematics from the University of Science and Technology of China. Research interests include computational algorithms in statistical inference and applied statistics in the field of biology and astronomy.
Speaker: Eunice Kim, Data Scientist at Microsoft, USA
Wednesday July 13, 2022, at 11.30 am Chilean time
• Title of the seminar: Signal and Noise – Hotspots Jump Out of the Norm
• Abstract: Humans easily detect something that is out of the ordinary. That presupposes that we establish a norm, have some measurements and/or a detectable (visual, aural, or olfactory) aid to understand what we experience and observe. How do statistics and data science come into play when there is a need to detect anomalies and provide a response to the anomalies? What are the conditions given? How do we set up the problem when we have little knowledge of the domain? I draw examples from my work and share what I have found to be necessary for analysis and collaboration.
About the speaker: Eunice Kim is a data scientist with Microsoft. She has a passion for teaching and solving complex problems with an investigative, statistical lens. Previously, she taught undergraduate students in engineering at Iowa State University and liberal arts majors at Amherst College. Besides teaching introductory statistics courses and research methods, she recalls the challenge and joy of introducing spatial statistics to undergraduates. Her research application is in the environmental and economic context where she brings her specialty in spatial statistics to draw out patterns. She has a Bachelor’s Degree in Mathematics and Economics, and Master’s and PhD in Statistics. She volunteers on the new client acquisition team of Statistics Without Borders and serves as chair of the Committee on Women in Statistics of the American Statistical Association.
Local Organizing Committee
Alba Martínez-Ruiz – UDP
Paula Fariña – UDP
Raúl Pezoa-Zamorano – UDP
Francisco Jara-Moroni – UDP
Supporting Institutions – Endorsements