麻豆传媒

Dr. Raphael Gottardo is a computational biologist who specializes in applying rapidly evolving ideas in data science to solving problems in cancer and related diseases. As scientific director of the Translational Data Science Integrated Research Center, he is at the center of the busy intersection of biology, data science and technology at Fred Hutchinson Cancer Research Center. His goal is to expand data-driven innovations for patients by cultivating a cross-disciplinary environment in which doctors and laboratory scientists work seamlessly with their colleagues in biostatistics and computational sciences to take advantage of the flood of information made possible by advanced technologies. The aim is to bring scientific discoveries from research labs to the bedside sooner using data-driven approaches. To do so, bench scientists and clinical researchers from many corners of the Hutch work collaboratively with experts in data science. Much of his work is focused on profiling the cellular components of the human immune system – using data science to understand how to make immunotherapies work better for patients. “It’s when you get into the details that it really becomes interesting,” he said. “The immune system is very complex, and it turns out we don’t know a whole lot about it yet. Looking at these single-cell technologies generating massive amounts of data has brought me to really cool statistical and computational challenges.” Dr. Gottardo’s own research involves the development of computational tools for vaccine and immunology studies, including high-throughput experiments that may use flow cytometry or high-speed genome sequencing. His current studies include: • Statistical and computational analysis of flow cytometry data • Development of statistical and computational methods for single-cell genomics • Immune responses to malaria and HIV infection and immunization within the Human Immunology Project Consortium (HIPC) • Development of the HIPC database and research portal (www.immunespace.org) • Contribution to the Bioconductor project, an open computing resource for genomics • Leadership for the Vaccine and Immunology Statistical Center of the Collaboration for AIDS Vaccine Discovery of the Bill and Melinda Gates Foundation • Leadership for the Vaccine Statistical Support (VSS) Global Health Vaccine Accelerating Platform (GH-VAP) of the Bill and Melinda Gates Foundation Dr. Gottardo is the J. Orin Edson Foundation Endowed Chair at Fred Hutch and a member of the Vaccine and Infectious Disease and Public Health Sciences Divisions. He, along with other Fred Hutch researchers, is co-leading a collaboration with the Allen Institute for Immunology to chart the human immune system by harnessing big data and emerging technologies. An affiliate professor of statistics at the University of Washington, he teaches courses in stochastic modeling, bioinformatics and statistical computing and supervises biostatistics and statistics doctoral students on statistical-methods research for high-dimensional omics data analysis

Satish Karra is a computational scientist with the Systems Modeling and Computational Science team. He joined EMSL in 2022 as the lead scientist for the . In this role, he oversees the computational resource needs of EMSL users and advises on simulation and computational strategies to achieve their science objectives. He co-leads road mapping for EMSL's computing and modeling vision.
Satish has 15 years of experience developing scientific software products (high-performance computing, machine learning) for various DOE and private sponsors. Satish’s research is at the interface of engineering, geoscience, applied mathematics, and computing, to solve real-world problems in energy and environmental sciences. He is an expert in building coupled multi-physics and multi-scale models for subsurface applications. His recent works build reduced-order approaches using machine learning and graph-based techniques that emulate physics towards faster decision-making. He also develops methods to link models and experimental data via machine learning. He has 75+ peer-reviewed journal papers and book chapters and is a reviewer for a broad range of journals in computational science, geoscience, flow and transport, and mechanics. He is a developer of the massively parallel code  and led the parallelization effort in the R&D100 award-winning suite . Satish has mentored 20 students and postdocs. Before joining EMSL, Satish was the team leader for the Subsurface Flow and Transport Team at LANL. At LANL, he led and contributed to projects in the areas of energy, global security, and nuclear security.

Research Interests


Porous media modeling
Reduced-order modeling
Multi-physics and multi-scale coupling
Physics-informed machine learning


Education


PhD in Mechanical Engineering, Texas A&M University (2011)
MS in Mechanical Engineering, Texas A&M University (2007)
BTech in Mechanical Engineering, Indian Institute of Technology Madras (2005)


Awards and Recognition


LANL SPOT awards for team leadership and positive influence on co-workers, 2020, 2021.
Distinguished Performance Award (Subsurface Hydrology, Geology, and Geochemistry Science Team), Los Alamos National Laboratory, 2019.
Los Alamos Awards Program award for Publication, Los Alamos National Laboratory, 2017 & 2019.
Distinguished Performance Award (dfnWorks Team), Los Alamos National Laboratory, 2018.
R&D100 Award for dfnWorks, 2017.
Federal Laboratory Consortium for Technology Transfer Notable Technology Development Award for dfnWorks Software Suite, 2017.
Los Alamos Awards Program award for Outstanding Performance in Prototyping Three-dimensional Calculations and Visualizing of Gas Migration in Fractures Following a Subsurface Explosion, Los Alamos National Laboratory, 2016.
Los Alamos LDRD Early Career Award, Los Alamos National Laboratory, 2014.
Outstanding Graduate Student Teaching Award, Texas A&M University, 2010.
Mechanical Engineering Graduate Fellowship, Texas A&M University, 2005-06.
‘Graduate Pool’ Fellowship, Texas A&M University, 2005-06.

Margaret Cheung is a biological physicist and computational scientist with the Systems Modeling and Computational Science team. She holds a joint appointment with the Department of Physics at the University of Washington. She is interested in employing an integrative approach of quantum mechanical calculations, molecular simulations, out-of-equilibrium statistical physics, and network theory to investigate the role of emergent, higher-order protein assemblies in regulating living matter.