Dr. James Ahrens of Los Alamos National Laboratory (LANL) is the founder and design lead of ParaView, a widely adopted visualization and data analysis package for large-scale scientific simulation data. ParaView has had an extremely positive impact on the large-scale data analytic capabilities available to simulation scientists around the world. Dr. Ahrens graduated in 1996 with a Ph.D. in computer science from the University of Washington. Following his graduate studies, he joined LANL as a technical staff member. At Los Alamos, he is the leader of an awesome data analysis and visualization team of twenty staff, postdocs and students, as well as a national leader of programmatic initiatives important to the Department of Energy’s (DOE) National Nuclear Security Administration (NNSA) Advanced Simulation and Computing (ASC) program and the Office of Science (SC) Advanced Scientific Computing Research (ASCR) programs.
Ayan joined the Data Science at Scale team as a postdoctoral researcher January 2017. He received his Ph.D. in Computer Graphics and Visualization from The Ohio State University in December 2016 and has been a summer intern since 2013. He has worked with flow field data and particle tracing using streamlines and stream surfaces and on time-varying multivariate data exploration and using information theory to provide some insights into data. He is also working with turbulent flow structures and vortex visualization for the unstable time-varying complex flows.
Dr. Roxana Bujack is a staff scientist in the Data Science at Scale Team at Los Alamos National Laboratory since July 2016. She graduated in mathematics and computer science and received her PhD in the Image and Signal Processing Group at Leipzig University. Then, Roxana worked as a postdoctoral researcher at IDAV at the University of California, Davis and at the Computer Graphics and HCI Group at the Technical University Kaiserslautern. Her research interests include visualization, pattern recognition, vector fields, moment invariants, high performance computing, massive data analysis, Lagrangian flow representations, and Clifford analysis.
Curt joined the Los Alamos National Lab in 1994 as a systems scientist. He has over 30 years of experience as a Systems Scientist and Manager in information technologies.
Pat received an M.S. in Computer Science from Purdue University in 1975 when there was a whole lot less to learn. She just celebrated her 35th anniversary as a staff member at Los Alamos. As a software engineer Pat has enjoyed tackling such diverse topics as graph theory, hydrology, cosmology, physics, fraud detection, nuclear reactors, discrete event simulation, military evidence marshalling, and finally large scale visualization along with the wonderful domain experts at the laboratory.
Pascal joined the Data Science at Scale team as a post doc in August 2016. He received his Ph.D. in Computing from the University of Utah working on visualization on High-Performance Computing (HPC) Systems. His research interest is in large-scale data visualization and analysis.
Qiang joined the Data Science at Scale team in November 2015. He is also a member of the Ultra-scale System Research Center at the New Mexico Consortium. His interests include cloud performance modeling and optimization, cloud dependability and reliability analysis, cloud failure detection and prediction, virtualization, power management and green computing in cloud infrastructures, resilience analysis In HPC, resource management in cloud system, data mining and machine learning, signal processing and image processing in Biometrics.
Li-Ta (Ollie) Lo
Li-Ta Lo a.k.a Ollie received a B.S. in Physics from National Chung-Hsing University in 1995 and a M.S. in Applied Mechanics from National Taiwan University in 1997. He joined Los Alamos National Laboratory in 2003, after working in the semiconductor industry for 4 years. As a multi-disciplined, multi-cultured person, he has enjoyed working with several teams and a diverse set of projects during his career at LANL. His current research interest includes data science, large-scale visualization and analysis, data-parallel programming and software engineering for scientific computing.
John received a B.A. in Anthropology in 1995 and a M.S. in Computer Science in 2011, both from the University of New Mexico. His experience includes data science at scale, large-scale visualization and analysis, data-parallelism, in-situ visualization and analysis.
Scientist and Team Leader
David Honegger Rogers joined LANL in 2013, after a decade of leading the Scalable Analysis and Visualization Team at Sandia National Labs, where he was instrumental in bringing in-situ analysis and visualization into production. He now focuses on interactive web-based analysis tools that integrate design, scalable analytics and principles of cognitive science to promote scientific discovery. Prior to working on large scale data analysis, David worked at DreamWorks Feature animation, writing and managing production software. He has degrees in Computer Science, Architecture (buildings, not computers), and an MFA in Writing for Children.
Scientist and Artist
Francesca’s work balances on the fulcrum between art and science. Activity immersed in collaborations with scientists and computer visualization specialists, she has seeks to meld scientific factual understanding with artistic distillation and metaphor. Using the combined language, she seeks to present the environmental issues of our time in an approachable digestible form to a wide range of audiences. The bulk of her work day is spent with the Research Visualization Team at Los Alamos National Labs. Using her expertise in color and communication, the non-numerical kind, they are involved in a long-term collaboration to improve the visualization tools to help scientists gain deeper understanding from data and research. See the SciVisColor.org website or more information.
Jon joined the Los Alamos National Lab as a staff member in 2009. He recieved B.S., M.S. and Ph.D.’s in Computer Science from The Ohio State University. His experience includes visualization, data science, scientific computing, and high-performance computing.