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. At Berkeley, Prof. Slaybaugh's research program is based in computational methods and applied to existing and advanced nuclear reactors, nuclear non-proliferation and security, and shielding applications. She received a BS in nuclear engineering from Penn State in 2006, where she served as a licensed nuclear reactor operator. Dr. Slaybaugh went on to the University of Wisconsin–Madison to earn an MS in 2008 and a PhD in 2011 in nuclear engineering and engineering physics along with a certificate in energy analysis and policy. For her PhD, she researched acceleration methods for massively parallel deterministic neutron transport codes. Dr. Slaybaugh then worked with hybrid (deterministic-Monte Carlo) methods for shielding applications at Bettis Laboratory while teaching at the University of Pittsburgh as an adjunct faculty member. Throughout her career, Dr. Slaybaugh has been engaged in Software Carpentry education and training; she also contributes to the open source project PyNE. Prof. Slaybaugh was awarded the 2014 American Nuclear Society Young Member Excellence Award.