I. Introduction 1. Overview of Data Science and Sustainability
Analysis and State of their Co-Application
II. Enironmental Health and Sustainability 2. Applying AI for
Conservation 3. Water balance characterization 4. Machine Learning
in the Australian Critical Zone
III. Energy and Water 5. A Clustering Analysis of Energy and Water
Consumption in U.S. States from 1985 to 2015 6. Energy footprint of
big data evaluated with data science 7. Solar PV rooftop
disaprities by race and ethnicity in US 8. Screening materials for
solar pv
IV. Sustainable Systems Analysis 9. Machine Learning in life cycle
analysis 10. Industry sustainable supply chain management with data
science
V. Society and Policy 11. Machine Learning to Inform Enhance
Environmental Enforcement 12. Sociologically informed use of remote
sensing data to predict rural household poverty 13. Trade-offs
Between Environmental and Social Indicators of Sustainability
VI. Conclusion 14. Research and Development for Increased
Application of Data Science in Sustainability analysis
Jennifer B. Dunn is the Director of Research of the Northwestern-Argonne Institute of Science and a Research Associate Professor at Northwestern University in Chemical and Biological Engineering. She holds a joint appointment in the Energy Systems Division of Argonne National Laboratory, where she led the Biofuels Analysis Team before taking on her current role. In her research, Jennifer investigates life cycle energy consumption and environmental impacts of advanced transportation and fuel technologies, including biofuels and battery-powered electric drive vehicles. She is also interested in carbon capture and utilization (CCU), automotive lithium-ion battery impacts and recycling, and fit-for-purpose water treatment. She holds a PhD in Chemical Engineering from the University of Michigan. Prasanna Balaprakash is a computer scientist in the Mathematics and Computer Science Division with the joint appointment in the Leadership Computing Facility at Argonne National Laboratory. He is also a Fellow in the Northwestern-Argonne Institute of Science and Engineering of the Northwestern University. His research interests span the areas of artificial intelligence, machine learning, optimization, and high-performance computing. Currently, his research focus is on the automated design and development of scalable algorithms for solving large-scale problems that arise in scientific data analysis and in automating application performance modeling and tuning. He holds a Ph.D. in engineering sciences from CoDE-IRIDIA (AI Lab), Université libre de Bruxelles, Brussels, Belgium, where he was a Marie Curie fellow and later an FNRS Aspirant.
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