Argonne Materials Science Division
Raymond Osborn (Principal Investigator)
BIO: Ray Osborn is a Senior Physicist the Neutron and X-ray Scattering Group in the Materials Science Division of Argonne National Laboratory, where he has worked since 1992. His research is in the field of strongly correlated electron systems probing spin, charge and orbital correlations using neutrons and x-rays. His scientific interests have included quantum critical scaling in non-Fermi liquid compounds, the role of polaron correlations in colossal magnetoresistance, dynamic frustration in rare earth spin glasses, electron-phonon coupling in charge-density-waves, and the competition between magnetism and super conductivity in iron arsenides. He has also been active in developing new instrumental techniques to further his research goals, such as the development of more efficient methods of measuring single crystal diffuse scattering both with neutrons and high-energy x-rays. In collaboration with Stephan Rosenkranz, he was responsible for the proposal and conceptual design of the Corelli diffractometer at the Spallation Neutron Source.
Stephan Rosenkranz (Coinvestigator)
BIO: Stephan Rosenkranz is a Senior Group Leader and Senior Physicist in the Neutron and X-ray Scattering group in the Materials Science Division at Argonne National Laboratory.
BIO: Matthew Krogstad is an assistant physicist in the Magnetic Materials group at the Advanced Photon Source, working mainly at Sector 6-ID-D. He received a B.S. in physics at the California Institute of Technology in 2008 and a Ph. D. in physics at Northern Illinois University in 2018. He then worked as a postdoctoral appointee with the Neutron and X-Ray Scattering Group in the Materials Science Division, winning a Postdoctoral Performance Award in Basic Research in 2020, before joining the Magnetic Materials group in 2021.
His research is focused on detecting and analyzing short-range ordering in crystalline systems. He has practiced and developed high-throughput methods for measuring single crystal diffuse x-ray scattering, including methods for artifact detection and removal. He has also developed methods for analyzing such data, using a combination of 3D-PDF and Monte Carlo modelling techniques to extract the defining structural motifs from complicated solution spaces. Recent systems of interest include relaxor ferroelectrics, high-Tc superconductors, solar perovskites, and insulator-metal transition systems.
BIO: Puspa Upreti is a PhD candidate at Northern Illinois University and a visiting graduate student at Material Science Division, Argonne National Laboratory. His main research centers on the diffuse scattering studies of strongly correlated electron systems probing spin, charge or orbital correlations using neutrons and x-rays scattering.
Anjana M. Samarakoon
BIO: Anjana M. Samarakoon is a postdoctoral research associate in both the Emerging Materials and Neutron and X-ray Scattering groups, Material Science Division, Argonne National Laboratory. He received his B.S. from the University of Colombo, Sri Lanka, and Ph.D. from the University of Virginia in 2010 and 2017, respectively. Subsequently, he worked as a postdoctoral researcher in the Neutron Scattering Division, Oak Ridge National Laboratory. He won the Neutron Scattering Division Award in the category of Postdoc of the Year in 2019. He joined the Materials Science Division at Argonne in 2021.
Anjana’s research interest combines neutron and x-ray scattering experiments, advanced data analysis, and numerical modeling. He pioneered a machine-learning assisted method to address the inverse-scattering problem for diffuse magnetic scattering experiments. Further, he innovates and investigates data science approaches to address bottlenecks in scattering data analysis. Moreover, he has experience in crystal growth and characterization of magnetic and thermodynamic properties. He has worked on topics ranging from frustrated magnets, spin glasses, quantum spin liquids, topological materials to relaxor ferroelectrics.
Cornell Department of Physics
BIO: Eun-Ah Kim is a Korean-American condensed matter physicist interested in high-temperature superconductivity, topological order, strange metals, and the use of neural network based machine learning to recognize patterns in these systems. She is a professor of physics at Cornell University.
BIO: My research is focused on the understanding of strongly interacting quantum matter through machine learning approaches to experimental data and numerical simulations. Topics of interest are High Tc superconductors, out of equilibrium phases, dynamics and thermalization in quantum systems. I am currently a postdoc at Cornell. I obtained a PhD in physics from Penn State University in 2020, and did my undergraduate education at IISER Trivandrum, India.
Argonne Mathematics and Computer Science Division
BIO: Charlotte Haley is an Assistant Computational Statistician in the Division of Mathematics and Computer Science at Argonne National Laboratory. She received her Ph.D. in Statistics from Queen’s University at Kingston. She is interested in statistical signal processing, spectral analysis, bandlimitation, time series analysis, and spatial statistics.
BIO: Mihai Anitescu is a Senior Computational Mathematician in the Mathematics and Computer Science Division at Argonne National Laboratory. He is also a part-time Professor in the Department of Statistics at the University of Chicago and co-teaches in the Computational Math sequence. He is also an adjunct Associate Professor in the Math Department at Pitt, led the M2ACS and now leads the MACSER project.
BIO: Vishwas Rao is currently an Assistant Computational Mathematician in the Mathematics and Computer Science (MCS) Division National Laboratory, Lemont, IL. He received his Ph.D. in computer science from Virginia Tech, Blacksburg, 2015 and had postdoctoral appointments at Oden Institute (previously ICES), University of Texas, Austin and at the MCS division in Argonne. His research interests include uncertainty quantification in weather and climate models and their applications to energy systems.