How much evidence do you need? Data Science to Inform Environmental and Climate Change Policy During the COVID-19 Pandemic
In this talk, I will provide an overview of data science methods, including methods for Bayesian analysis, causal inference, and machine learning, to inform environmental policy. This is based on my work analyzing a data platform of unprecedented size and representativeness. The platform includes more than 500 million observations on the health experience of over 95% of the US population older than 65 years old linked to air pollution exposure and several confounders. I will provide an overview of studies on air pollution exposure, environmental racism, wildfires, and how they also can exacerbate the vulnerability to COVID-19.
Francesca Dominici, PhD is the co-Director of the Harvard Data Science Initiative, at Harvard University and the Clarence James Gamble Professor of Biostatistics, Population and Data Science at the Harvard T.H. Chan School of Public Health and Co-Editor in Chief of the Harvard Data Science Review. She is an elected member of the National Academy of Medicine and of the International Society of Mathematical Statistics. She leads an interdisciplinary group of scientists to address important questions in environmental health science, climate change, and health policy. Her contributions to the field have been remarkable including more than 250 peer-reviewed published articles, and has provided her knowledge on the topics on joint panels with New Jersey Senator Cory Booker, and European Commission). Dr. Dominici has provided the scientific community and policy makers with comprehensive and compelling evidence on the adverse health effects of air pollution, noise pollution, and climate change. Her studies have directly and routinely impacted air quality policy. Dr. Dominici was recognized in Thomson Reuter’s 2019 list of the most highly cited researchers–ranking in the top 1% of cited scientists in her field. Her work has been covered by the New York Times, the Los Angeles Times, BBC, the Guardian, CNN, and NPR. In April 2020 she has been awarded the Karl E. Peace Award for Outstanding Statistical Contributions for the Betterment of Society by the American Statistical Association. She is an advocate for the career advancement of women faculty, and her work on the Johns Hopkins University Committee on the Status of Women earned her the campus Diversity Recognition Award in 2009. At the Harvard T.H. Chan School of Public Health, she has led the Committee for the Advancement of Women Faculty.
Continual Learning: the New Agile for Machine Learning Operations
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning, constantly and efficiently updating our biased understanding of the external world. On the contrary, current AI systems are usually trained offline on huge datasets and later deployed with frozen learning capabilities as they have been shown to suffer from catastrophic forgetting if trained continuously on changing data distributions. A common, practical solution to the problem is to re-train the underlying prediction model from scratch and re-deploy it as a new batch of data becomes available. However, this naive approach is incredibly wasteful in terms of memory and computation other than impossible to sustain over longer timescales and frequent updates. In this talk, we will discuss recent Continual Learning approaches which can significantly reduce the amount of computation and memory overhead (e.g. of more than 45% w.r.t. the standard re-train & re-deploy approach), further exploring their real-world applications in the Cloud or running on the edge on highly-constrained hardware platforms such as widely adopted smartphone devices
Vincenzo Lomonaco is an Assistant Professor at the University of Pisa, Italy and Co-Founding President of ContinualAI, a non-profit research organization and the largest open community on Continual Learning for AI. Currently, He is also a Co-founder and Board Member of AI for People, Director of the ContinualAI Lab and a proud member of the European Lab for Learning and Intelligent Systems (ELLIS). In Pisa, he works within the Pervasive AI Lab and the Computational Intelligence and Machine Learning Group, which is also part of the and the Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE). Previously, he was a Post-Doc @ University of Bologna (with: Davide Maltoni) where he also obtained his PhD in early 2019 with a dissertation titled “Continual Learning with Deep Architectures” (on a topic he’s been working on for more than 8 years now) which was recognized as one of the top-5 AI dissertation of 2019 by the Italian Association for Artificial Intelligence. For more than 5 years he worked as a teaching assistant for the Machine Learning and Computer Architectures courses in the Department of Computer Science of Engineering (DISI) at UniBo. In the past Vincenzo have been a Visiting Research Scientist at AI Labs in 2020, at Numenta (with: Jeff Hawkins, Subutai Ahmad) in 2019, at ENSTA ParisTech (with: David Filliat) in 2018 and at Purdue University (with: Eugenio Culurciello) in 2017. Even before, he was a Machine Learning Software Engineer @ iDL in-line Devices and a Master Student @ UniBo. His main research interest and passion is about Continual Learning in all its facets. In particular, he loves to study Continual Learning under four main lights: Deep Learning, Distributed Learning and Practical Applications, all within an AI Sustainability developmental framework.