Keynote Speakers



Claire Monteleoni is an Associate Professor, and the Associate Chair for Inclusive Excellence, in the Department of Computer Science at the University of Colorado Boulder, and the founding Editor in Chief of Environmental Data Science, a Cambridge University Press journal, launched in December 2020. She joined CU Boulder in 2018, following positions at University of Paris-Saclay, CNRS, George Washington University, and Columbia University. She completed her PhD and Masters in Computer Science at MIT and was a postdoc at UC San Diego. She holds a Bachelor’s in Earth and Planetary Sciences from Harvard. Her research on machine learning for the study of climate change helped launch the interdisciplinary field of Climate Informatics. In 2011, she co-founded the International Conference on Climate Informatics, which turned 10 years old in 2020, and has attracted climate scientists and data scientists from over 20 countries and 30 U.S. states. She gave an invited tutorial: Climate Change: Challenges for Machine Learning, at NeurIPS 2014.
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Despite the scientific consensus on climate change, drastic uncertainties remain. Crucial questions about regional climate trends, changes in extreme events, such as heat waves and mega-storms, and understanding how climate varied in the distant past, must be answered in order to improve predictions, assess impacts and vulnerability, and inform mitigation and sustainable adaptation strategies. Machine learning can help answer such questions and shed light on climate change. I will give an overview of our climate informatics research, focusing on challenges in learning from spatiotemporal data, along with semi- and unsupervised deep learning approaches to studying rare and extreme events, and precipitation and temperature downscaling.
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Tutorial Speakers



Sequential Learning
Tutorial 4B
Sequential learning addresses the problem of allocating resources under cost constraints, and sometimes lack of information. This class of problems is ubiquitous for instance in machine learning, operations research or econometrics. In particular, it is at the heart of Reinforcement Learning (RL), a learning paradigm where the agent learns via trial-and-error. In fact, a key difficulty for the learner is to decide how to explore the space of actions while trying to maximize a certain reward metric, thus facing an exploration-versus-exploitation trade-off. 
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Human AI interaction
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