Shikha Prasad, Assistant professor, Department of Nuclear Engineering
I am interested in neutron sensing and imaging to solve nuclear and international security, nuclear fuel cycle safety, and industrial applications. I am leading works in the areas of both neutron measurement and neutron response computation. These works include machine algorithms for source identification using neutron response, probing integrity of materials with neutron response, new neutron detector development for easier more accurate source characterization.
Wei (Ang) Eng, Ph.D. researcher
Wei Eng is a graduate student in the Department of Nuclear Engineering. He is currently working with Dr Prasad on neutron and neutrino detection for nuclear nonproliferation application. He is currently focusing on neutrino detection using cryogenic germanium detector and looking for neutrino energy spectrum. He’s also working with Department of Physics and Astronomy on background shielding project for phonon detection at Nuclear Science Center.
Xiaodong Tang, M.S researcher
Xiaodong is working on measurement techniques for securing advanced reactors such as the pebble-bed reactors. He is working on gamma-ray methods and neutron multiplicity methods. He is also working on MCNP6 ad MCNPX-PoliMi simulations of the same.
Benjamin Wellons, M.S researcher
Benjamin is working on multiplicity measurement for spontaneous fission sources.
Manan Dhir, Undergraduate researcher
Manan is working on setting-up excore neutron measurements at the Texas A&M Nuclear Science Center 1-MW reactor. He is working with the liquid organic scintillation detectors.
Sanghun Lee, Undergraduate researcher
Lee is working on simulations with MCNP and MCNPX-PoliMi. He works to improve detection methods and nuclear data discrepancies.
Patrick Maedgen, Undergraduate researcher
Patrick working on data-science methods to to improve neutron detection methods. He is working on neutron and gamma-ray pulse shape discrimination methods using machine learning. He primarily uses Python libraries for his calculations.
Ian Halvic, Ph.D. student
Ian’s research involved the application of machine learning techniques for neutron spectra reconstruction for use with scintillation detectors. Detector output data was leveraged to perform classification of the data by the physics of the neutron production. From this classification the appropriate unfolding model was selected and applied to give the operator an approximation of the neutron flux spectra experienced by the detector allowing the operator to make quicker and more informed decisions based on the spectra observed.
Sournav worked on quantum algorithms to better simulate Monte Carlo transport of neutrons in a detection problem.