Haruko Wainwright is an Affiliate Scientist at Lawrence Berkeley National Laboratory in Environmental Geophysics Group. She is also an Assistant Professor at Massachusetts Institute of Technology. Her research focuses on environmental informatics, aiming to improve understanding and predictions in Earth and environmental systems through mechanistic modeling and machine learning. She has been working on Bayesian geostatical methods and zonation-based data integration methods to integrate multi-type and multiscale datasets (e.g., point measurements, geophysical data, and drone/airborne/satellite remote sensing data) for estimating spatially heterogeneous subsurface and ecosystem properties. In addition, she has been developing real-time model-data integration approaches to improve environmental monitoring, including radiation, groundwater contamination and soil moisture. In parallel, she has been developing and applying global sensitivity analysis methods and reduced-order modeling (i.e., emulators) to efficiently predict the environmental impacts of environmental contamination, nuclear waste disposal and geological CO2 storage.
She works on broader topics, including watershed science, Arctic ecosystem science, agricultural ecosystem science, environmental monitoring and remediation, radiation monitoring and restoration after the Fukushima accident, nuclear waste disposal and CO2 storage. Her recent focus is the use of AI and machine learning for environmental monitoring; particularly for real-time spatiotemporal estimation and monitoring network optimization. In addition, her research aims for establishing sustainable remediation methodology, and exploring sustainable solutions of nuclear waste.
She is the Environmental Resilience program lead in EESA. She leads multiple projects; Watershed Function Scientific Focus Area (Thrust lead), Advanced Long-term Monitoring Systems (Co-PI), AR1K (Co-PI) and others. She has served in the International Atomic Energy Agency Modelling and Data for Radiological Impact Assessments (MODARIA II) working group as well as the Federal Remediation Roundtable (FRTR) committee. She is also a co-lead of the “AI for Earth Systems Predictability” (AI4ESP) working group in the Department of Energy, Office of Science, Biological Environmental Science, Earth and Environmental Systems Sciences Division. She was featured in Women Killin’ it in STEM Fields.
She earned her PhD in nuclear engineering, MS in nuclear engineering and MA in statistics from University of California, Berkeley. She earned her B. Eng. in engineering physics from Kyoto University in Japan. Her expertise includes stochastic and computational hydrology, spatial statistics, machine learning, data integration, and uncertainty quantification.