Haruko Murakami Wainwright
Mitsui Career Development Professor in Contemporary Technology
Assistant Professor of Nuclear Science and Engineering, and
Assistant Professor of Civil and Environmental Engineering
Haruko Wainwright joined the Department of Nuclear Science and Engineering as an assistant professor in January 2022. She received her BEng in Engineering Physics from Kyoto University, Japan in 2003; her MS in nuclear engineering in 2006, MA in statistics in 2010 and PhD in nuclear engineering in 2010 from University of California, Berkeley. Before joining MIT, she was a Staff Scientist in the Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory, and an adjunct professor in Nuclear Engineering at University of California, Berkeley. Her research focuses on environmental modeling and monitoring technologies, with a particular emphasis on nuclear waste and nuclear-related contamination. She has been developing Bayesian methods for multi-type multiscale data integration and model-data integration. She leads and co-leads multiple interdisciplinary projects, including the US Department of Energy’s Advanced Long-term Environmental Monitoring Systems (ALTEMIS) project, and theArtificial Intelligence for Earth System Predictability (AI4ESP) initiative.
At nuclear contaminated sites, long-term environmental monitoring is critical to ensuring public safety and combatting misinformation. Climate change poses additional concerns since extreme weather could re-mobilize residual contaminants. We are developing a new paradigm of environmental monitoring by integrating state-of-the-art technologies: in situ sensors, geophysics, remote sensing, contaminant transport simulations and machine learning.
Geological disposal is required to isolate nuclear waste for thousands of years. Models need to address the large uncertainty associated with geological heterogeneity and future climate. At the same time, the existing contamination from waste disposal in the 1940s–1980s provides significant insights on radionuclide mobility and datasets for model validation. We are developing uncertainty quantification (UQ) methods, including global sensitivity analysis, Bayesian parameter estimation, surrogate modeling, and experiment-to-model UQ pipelines. In parallel, we have been developing comparative analysis methodologies for quantifying the environmental impacts of nuclear waste and other energy waste.
Emergency responses during accidents and the recovery from contamination have been challenging in nuclear energy. We are developing science-based methodologies to prepare for, respond to, and recover from environmental disasters. Risk analysis allows us to quantify the consequence and probability of such accidents, and to identify vulnerabilities. Integrated monitoring technologies enable the rapid assessment of accident consequences and planning of the recovery. Remediation technologies have been evolving towards sustainable methods, considering net environmental impacts. Our goal is to build a comprehensive environmental resilience framework in nuclear energy.