The nuclear energy industry is at a crossroads: existing nuclear reactors are struggling to operate economically in some tough markets, and construction of new designs in the U.S. is slow and over budget. At the same time, interest in and development of the next generation of nuclear reactors is growing at an unprecedented rate, and some other nations are building new reactors efficiently. Can the current fleet reduce costs? Will the next generation of designs be “walkaway safe” and cost-competitive? What about safeguards and recycling of nuclear fuel? Many new technologies, including Data Analytics and Machine Learning, can be impactful in answering these questions. This talk will frame some of the big challenges in nuclear energy and how new technologies are starting to be used. We’ll also look to the future in terms of where the biggest impacts are likely to be and what we can do to move quickly.
The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features Berkeley faculty doing visionary research that illustrates the character of the ongoing data revolution. This lecture series is offered to engage our diverse campus community and enrich active connections among colleagues. All campus community members are welcome and encouraged to attend. Arrive at 3:30 PM for light refreshments and discussion prior to the formal presentation.
Rachel Slaybaugh is an Assistant Professor of Nuclear Engineering at the University of California, Berkeley. Slaybaugh researches computational methods applied to nuclear reactors, nuclear non-proliferation and security, and shielding. She recently served as a Program Director at the Advanced Research Projects Agency-Energy (ARPA-E) and has served on the Nuclear Energy Advisory Committee (NEAC). She is the founding Board Chair for Good Energy Collective, and a Senior Fellow at the Breakthrough Institute. Prof. Slaybaugh is developing programs to train and inspire the next generation to innovate in clean energy, including the Nuclear Innovation Bootcamp, and also focuses on improving transparency and reproducibility in computational science and scientific publication.