Haruko Murakami Wainwright

I am from Aomori, Japan, where many nuclear facilities – all of Japan’s back-end facilities – are located. I grew up worrying about the environmental impacts of these facilities. At the same time, I was fascinated by nuclear physics and its history (e.g., Marie Curie). I studied engineering physics and nuclear engineering at Kyoto University, Japan. During my PhD at University of California-Berkeley, I worked on modeling radionuclide transport in a nuclear waste repository, and developed a Bayesian hydrogeological parameter estimation method at the Hanford 300 Area. I have MA in statistics as well.

After earning my PhD degree, I worked in Berkeley Lab’s Earth and Environmental Sciences Area for 11 years. I developed modeling and monitoring technologies for nuclear contaminated sites in the US and in the Fukushima region. I also worked on various Earth and climate science topics, including hydrogeophysics, watershed science, remote sensing, permafrost, geological CO₂ sequestration, precision agriculture and biodiversity. It was an eye opening experience to learn the significant impacts of climate change on our environment, and also innovative technologies such as geophysics, remote sensing and drones. At the same time, I’ve developed expertise in uncertainty quantification and parameter estimation coupled with large-scale simulations, and geospatial machine learning and geostatistics, including multi-scale data integration.

My current research aims to establish environmental resilience in nuclear energy to better prepare for, respond to, and recover from nuclear accidents and associated environmental contamination. Observations from nuclear contaminated sites – at more than 100 nuclear weapon-related sites in the US and in the Fukushima region – provide valuable insights into the mobility of radionuclides as well as validate model/monitoring performance. At the same time, I continue developing and improving environmental monitoring and modeling strategies for existing/potential contaminated sites or for general general earth science applications, using innovative sensor technologies, contaminant transport simulations and machine learning.