Ping Yang is the Deputy Director of the G.T. Seaborg Institute for Transactinium Science and a Staff Scientist in the Physics and Chemistry of Materials Group of the Theoretical Division at Los Alamos National Laboratory. She has extensive experience in computational approaches to modeling electronic structure and reactivity of actinides, separation, surface chemistry, and nanomaterials in solution environments. Her research focuses on understanding fundamental electronic structures, spectroscopic properties, reactivity, and dynamical behaviors of f-element chemistry and materials that are crucial for energy security. She has broad interests in both applications of current high-performance computing frameworks and development of new computational methods for long time-scale simulations of complex f-element systems.
Dr. Kozimor is a staff scientist in the Inorganic, Isotope, and Actinide Chemistry group (C-IIAC), where he is heavily involved in the isotope production team. He focus on the study of the unique properties and problems associated with radioactive elements, including synthetic expertise and spectroscopic expertise for f-elements. He leads a laboratory-based team in conducting X-ray spectroscopy measurements on radioactive samples at synchrotron facilities since 2006.In addition, Stosh serves as the Los Alamos National Laboratory BES HEC project PI and maintains a fully functional transuranic laboratory (the "Alpha Wing"). Stosh has a wide background in inorganic synthesis, spectroscopy, and radiochemistry with his work at the Lab branching out into studies of actinides (in particular transuranics). Of particular relevance, Stosh spent four years working on the C-NR radiochemistry, where his technical focus was center on nuclear forensics. He was additional the P.I. for the DTRA supported Nuclear Forensic Undergraduate Summer School.
Joshua Schrier is a physical chemist interested in using computers to accelerate the discovery of new materials, by using a combination of physics-based simulations, cheminformatics, machine learning, and automated experimentation. He is the Kim B. and Stephen E. Bepler Professor of Chemistry at Fordham University in New York City. Prior to joining Fordham in 2018, he was on the faculty at Haverford College, and a Luis W. Alvarez computational sciences postdoctoral fellow at Lawrence Berkeley National Laboratory. As a faculty member, he has received awards include the Dreyfus Teacher-Scholar, U.S. Department of Energy Visiting Faculty, and Fulbright scholar awards.
Adelman's diverse educational background, including a PhD in Chemistry from Michigan State University under Professor Jim McCusker, and her professional experiences have led her to a career at Los Alamos National Laboratory. There, she focuses on complex scientific questions vital for national security. During her time at MSU, she worked on developing earth-abundant dyes for solar cells and analyzed their electronic and physical properties. As a post-doc at the Laboratory, under Drs. Stosh Kozimor, Veronika Mocko, and Benjamin Stein, she developed a robotic system for automated chemical processes in a radiological lab. Now, as a scientist, she enhances automated systems for pharmaceutical isotope production and nuclear energy applications, collaborating with data scientists to improve separations and train machine learning algorithms.
Enrique R. Batista, received a B.S. in physics, with honors from the University of Buenos Aires, Argentina, M.S. and Ph.D. in physics from the U. of Washington. Post Ph.D. he worked as a postdoctoral fellow at Columbia University, in the Environmental Molecular Sciences Institute, funded by the NSF. He joined Los Alamos National Laboratory in 2003 as a post-doctoral fellow at the Theoretical Chemistry and Molecular Physics group and a staff scientist in 2005. Since 2016 Batista is Deputy Center Director of the Center for Nonlinear Studies (CNLS). His research areas of interest in the computational study of actinide chemistry in gas phase and in solution, spectroscopy, coordination, and reactivity, and on electronic structure studies of transition metal complexes with application to homogeneous catalytic processes. His studies also extend to solid-state systems and surface chemistry problems. Batista works with, and towards the development of, efficient computational methodologies for atomistic simulations and electronic structure approaches.
Nicholas Lubbers is a computation scientist with Los Alamos National Laboratory’s Information Sciences group. His background is in statistical physics, with a Ph.D. from Boston University under Professor William Klein. He had a postdoctoral appointment with the Center for Nonlinear Studies and Theoretical Division at the Lab, and in 2018 the Information Sciences group in the Computer, Computational, and Statistical Sciences division as a staff scientist. He is an organizer and mentor for the Laboratory’s Applied Machine Learning Summer Program, which he lead in the years of 2021-2023.
His research focus is on Machine Learning for Science, with diverse applications to Materials Science, Seismology, Fluid Mechanics, Microbiome analysis, Nanoscale phenomena, and Computational Chemistry. He has particular interest in physics-informed algorithm design, uncertainty quantification and active learning, and meta-algorithms. His mission is to contribute to science and technology through innovation, mentoring, and open-source software development.
Danny is a Scientist IV at Los Alamos National Laboratory. He obtained a Ph.D. in Physics from Université de Montréal in 2006. He then joined the Laboratory as a Director’s postdoctoral fellow in 2007. His research centers around the development of long-time atomistic simulation methods that are designed to explore systems that evolve through sequences of rare events. He then implements these methods in high performance simulation codes, and applied them to a range of problems of relevance to energy applications, such as nuclear fusion or fission. Over the last few years, Danny has been especially interested in developing strategies for the optimal use of exascale computing. He is currently the PI of the EXAALT project funded by DOE’s Exascale Computing Project where his team develops novel ultra-scalable approaches for materials simulations, including long-timescale molecular dynamics, optimization for GPUs, and high-throughput large-scale workflows.
