The Department of Physics is proud to announce that two graduate students have been recognized with a 2018 Center for Research Computing (CRC) Award for Computational Sciences and Visualization, James Kapaldo and Anna McCoy. This award recognizes outstanding contributions in the areas of computational sciences and visualization. Such contributions may include, but are not limited to: 1) applications of high performance computation and/or visualization technology; 2) development of algorithms, codes, software environments or other tools for better using high performance computing and/or visualization.

**James Kapaldo** is advised by Prof. Sylwia Ptasinska. He is a January 2018 PhD graduate. James has been able to develop a single method, based on fundamental physics, that can be applied to locating cell nuclei in biophysical and biomedical areas working with cell images, by allowing larger scale experiments and more accurate results, and this can allow researchers to get their work to the public more quickly. His method also directly applies to any application or research that uses unsupervised machine learning or data clustering; and, as these two fields are extensively used in artificial intelligence, natural language processing, network security, and in daily life in general, his work could have far reaching impacts in society. Moreover, his work uses fundamental physics to solve applied problems in image processing and machine learning, and this can give strong motivation for studying physics, and the base sciences in general, to young scientists and high school students who want to solve applied problems.

**Anna McCoy** is advised by Prof. Mark Caprio. She is a January 2018 PhD graduate. Anna has the unusual distinction of having seen the development of an innovative ab initio computational scheme, for the nuclear quantum many-body problem, through from conception to implementation. Ab initio nuclear calculations suffer from an explosion in dimension as either the size of the nucleus (number of protons and neutrons) or the included single-particle space (and thus the accuracy of the calculation) increases. Anna developed a configuration interaction framework for ab initio nuclear structure calculations based on highly-correlated many-body basis functions which encode an approximate symplectic, or Sp(3,R), symmetry of the nucleus. The symplectic scheme potentially provides a means of restricting the many-body space for nuclear calculations to include only those highly-excited configurations which dominantly contribute to the nuclear wave function. Anna’s initial thesis results reveal a much greater role for symplectic symmetry in nuclear wave functions that had previously been demonstrated.

Anna is now continuing her work on this project and on ab initio computational nuclear theory as a postdoc at TRIUMF, Canada’s national laboratory for subatomic physics, at the University of British Columbia.

Originally published by physics.nd.edu on May 04, 2018.

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