Research Expertise and Interest

neutronics, criticality safety, nuclear data validation, machine learning, neutron noise, reactor dosimetry

Research Description

Daniel Siefman is an assistant professor in the Department of Nuclear Engineering. His research interests include critical and subcritical experiments and methods, nuclear data validation and adjustment, computational methods in radiation transport, neutron noise, reactor dosimetry, design optimization and safety analysis of nuclear reactors with machine learning, and nuclear power plant decommissioning.  Daniel received a bachelor’s degree in Nuclear Engineering from the University of Florida in 2013, masters degrees in Nuclear Engineering from the École polytechnique fédérale de Lausanne (EPFL) and from ETH Zurich in 2015, and a PhD in Nuclear Engineering from EPFL in 2019. From 2019 to 2023, he was a staff scientist in the Nuclear Criticality Safety Division at Lawrence Livermore Laboratory supporting R&D efforts in integral experiments, nuclear data validation, radiation transport, neutron noise, and diagnostics for nuclear emergency response.