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Los Alamos Distinguished Postdoc Fellows

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Meet the Lab's Current Distinguished Postdoctoral Fellows

Los Alamos National Laboratory Distinguished Fellows (pdf)

Lauren J. Beesley

Feynman Distinguished Postdoctoral Fellow
Analytics, Intelligence and Technology Division: Information Systems and Modeling (A-1)
Computer, Computational and Statistical Sciences: Statistical Sciences (CCS-6)

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Education: Ph.D. in Biostatistics – University of Michigan; B.S. in Mathematics – University of Kansas

Mentors: Sara Del Valle and David Osthus

Research: Lauren’s research addresses statistical challenges that arise during analysis of complex observational data, including handling of missing data, variable selection, measurement error, and selection bias. Lauren has collaborated with researchers across a variety of scientific domains, including oncologists and medical doctors, epidemiologists, human geneticists, computer scientists, biologists, and mathematicians. At LANL, Lauren’s research will focus on the common setting where researchers want to develop prediction models based on multiple different data streams (e.g., social media, satellite imagery, demographics, etc.). Data quality can vary substantially between different data sources, and issues such as missing data, measurement error, and massive data size all pose unique and consequential challenges. Lauren’s research will work to address some of these challenges, with the ultimate goal of giving better model predictions. Lauren’s work will center on two key applications, including forecasting of political instability across various spatial resolutions and addressing problems with data quality and backfill in prediction modeling of influenza outbreaks.

Bio: Lauren received her PhD in Biostatistics under the mentorship of Dr. Jeremy M G Taylor. Her dissertation research focused on handling of missing data for complicated event time outcomes, with a particular emphasis on risk prediction for survival and recurrence outcomes for patients diagnosed with cancer. After her PhD, Lauren worked with Dr. Bhramar Mukherjee as a Postdoctoral Research Fellow at The University of Michigan, where she developed statistical strategies for reducing bias due to selection and measurement error in health research using electronic health record data. In her free time, Lauren enjoys cooking/baking and crafting.


Joshua Burby

Feynman Distinguished Postdoctoral Fellow
Theoretical Division: Applied Mathematics and Plasma Physics (T-5)

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Education: Ph.D. in Plasma Physics – Princeton University; B.S. in Engineering Physics – Cornell University

Mentors: Xianzhu Tang and Luis Chacon

Research: Josh's research aims to efficiently model the intricate multiscale properties of plasmas using tools from dynamical systems theory and differential geometry. He focuses on developing broadly-applicable analytic and computational techniques for coping with the time- and space-scale separation associated with stiff plasma dynamics. Through his research, Josh recognized that phase-space-geometric objects known as slow manifolds appear and play a foundational role in a variety of reduced models for multiscale plasma behavior. Notably, he used this observation to explain how those models inherit Hamiltonian structure from more-complete descriptions of the plasma state. As he continues to explore the ramifications of slow manifolds in plasma physics, Josh is developing "slow manifold integrators" at LANL. These are simulation algorithms that use a system's slow manifold to solve the preconditioning problem inherent to implicit simulations of temporally-stiff systems.

Bio: Josh earned his PHD under the mentorship of Professor Hong Qin at the Princeton Plasma Physics Laboratory. Being more of a mathematical physicist than a traditional theoretical physicist, Josh spent his first postdoc at the Courant Institute of Mathematical Sciences as a DOE Fusion Energy Sciences (FES) Fellow. After leaving Courant and before coming to LANL, he joined the Mathematical Sciences Research Institute (MSRI) as a Viterbi Fellow during the program "Hamiltonian systems, from topology to applications through analysis."


Carl Fields

Feynman Distinguished Postdoctoral Fellow
Computer, Computational and Statistical Sciences Division: Computational Physics and Methods (CCS-2) and Computational Physics Division: Eulerian Codes (XCP-2)

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Education: Ph.D. in Astronomy and Astrophysics - Michigan State University; B.S. in Physics and Astrophysics from Arizona State University

Mentor: Wesley Even, Joshua Dolence and Samuel Jones

Research: Carl's research focuses core-collapse supernova explosions and their massive star progenitors, stellar evolution and nucleosynthesis, and astrophysical sources of gravitational waves. He works on developing state-of-the-art computational and numerical methods for hydrodynamic simulations. These hydrodynamic models can directly impact the qualitative properties of massive stars and their stellar explosions. Stellar explosions can produce multi-messenger signals capable of being detected by current and next-generation astrophysical observations. Groundbreaking observational efforts, such as the LIGO observatory and the Super-K neutrino detector, benefit directly from our work. At LANL, Carl will be involved in ongoing development of next-generation large-scale computational tools capable of utilizing exa-scale machines to answer some of the largest questions of our Universe.

Bio: Carl earned his PhD at Michigan State University working with Professor Sean Couch. Carl was supported by a National Science Foundation Graduate Research Fellowship and a FORD Foundation Predoctoral Fellowship during his time at MSU. In December 2020, Carl was named to the prestigious Forbes 30 Under 30 for Science list. In the fall of 2020, Carl was awarded the Dr. Pliny A. and Margaret H. Price Prize recognizing research excellence and exceptional promise in areas related to Ohio State University Center for Cosmology and Astroparticle Physics initiatives. Fields was also named to the Inaugural class of the Edward A. Bouchet Graduate Honor Society chapter at MSU in 2019. He earned undergraduate degrees in Physics and Astrophysics from Arizona State University where he worked with Professor Frank Timmes on stellar evolution models.  


Christopher Johnson

Feynman Distinguished Postdoctoral Fellow
Earth and Environmental Sciences Division: Geophysics (EES-17)
and Science Program Office/Applied Energy Program (SPO-AE)  

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Education: Ph.D. and M.S. in Earth, Atmospheric and Ocean Sciences - University of California Berkeley
B.S. in Geophysics - Georgia Tech

Mentors: Paul Johnson, George Guthrie and Andrew Delorey

Research: Chris studies the crustal response of earthquake activity from transient forces to decompose the mechanical processes of active faulting. Earthquake triggering is the occurrence of an event during a quantifiable stress transient. Studying these events advances our knowledge of what makes earthquakes nucleate. His research uses seismic measurements to detect earthquakes and non-tectonic sources of ground motion. He incorporates geodetic measurements, or how the surface of Earth deforms, to characterize stress perturbations that promote earthquake activity. The enhancement of earthquake catalogs and detecting new observations of weak ground motions using dense seismic arrays is his latest research focus. Applying data driven machine learning techniques is allowing more information to be extracted from seismic waveforms that can be analyzed in conjunction with existing geodetic networks. Current research at Los Alamos National Laboratory is to utilize machine learning algorithms for seismic noise analysis to characterize signals related to earthquake nucleation. Analyzing these observations with new techniques that enhance the resolution of the observations will advance our understanding of what makes an earthquake start rupturing.

Bio: Chris completed his Ph.D. with a NSF graduate research fellowship at the Berkeley Seismological Laboratory at UC Berkeley. His dissertation work was advised by Roland Bürgmann with a focus on transient stress changes in the crust and the dynamics of earthquake triggering. He was awarded a NSF postdoctoral fellowship and spent 2.5 years at UC San Diego and Uni. of Southern California utilizing machine learning techniques to identity weak signals in dense seismic array data to enhance earthquake detection. Prior to entering a 4 year university as a non-traditional student, Chris worked as a diesel technician on heavy duty trucks before attending community college and transferring to Georgia Tech to study Geophysics. During his undergraduate studies he was involved in research exploring the dynamics of crustal strain that led to pursuing a graduate degree.


Arshan Nasir

Oppenheimer Distinguished Postdoctoral Fellow
Theoretical Division: Theoretical Biology & Biophysics (T-6)

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Education: Ph.D in Informatics and M.S. in Bioinformatics -University of Illinois at Urbana-Champaign; B.S. in Bioinformatics – COMSATS University

Mentors: Thomas Leitner and Ethan Romero-Severson

Research: Arshan has developed and utilized protein structure-based phylogenomic methods to advance research on viral origins and evolution. His research has shown that viruses originated multiple times in evolution from ancient cells that once co-existed with the ancestors of modern organisms, Archaea, Bacteria, and Eukarya. He has also shown that virus-to-cell gene transfer may transcend known host boundaries and the concept of virus host is rather ill-defined. In addition, he has associated the presence and absence of viral lineages in host organisms to major evolutionary transitions that shaped the diversification of life. His research is highly collaborative in nature with teams in USA, France, and Korea, and has been featured in popular science magazines and websites such as BBC Earth, Popular Science, Scientific American, among many others. Recently, he completed editing a special issue on ‘Viruses, genetic exchange, and the tree of life’ on the invitation of Frontiers in Microbiology (https://www.frontiersin.org/research-topics/7867/viruses-genetic-exchange-and-the-tree-of-life) alongside Professors Gustavo Caetano-Anollés (University of Illinois at Urbana-Champaign) and Jean-Michel Claverie (Aix-Marseille Université, France). He also co-led the development of HGTree, a novel database to detect horizontally transferred genes in prokaryotes in collaboration with Seoul National University in Korea. The database is available online (http://hgtree.snu.ac.kr/) and has been frequently used by microbiologists worldwide to monitor the spread of genes within bacteria. At LANL, he will be focusing on studying HIV evolution and spread in human populations and developing resources to combat its threat along with advancing his earlier work on reconstructing the deep evolutionary history of organisms and viruses.

Bio: During his undergraduate education, he developed a strong interest in utilizing computational approaches to solve biological problems. He completed an undergraduate thesis studying the possible transmission routes of mouse mammary tumor virus into human populations. This increased his research interest and enthusiasm in studying and combating viruses and he continued with virus evolution research during his MS and PhD education and training under the guidance of Prof. Gustavo Caetano-Anollés. At Illinois, he was part of an international evolutionary genomics collaborative between USA, France, and Korea to study virus origins and evolution using a novel approach based on protein structures. After the completion of his PhD, he returned to his home country of Pakistan and served at the COMSATS University in Islamabad.


Johanna Palmstrom

Reines Distinguished Postdoctoral Fellow
Materials Physics & Applications Division: National High Magnetic Field Laboratory (MPA-MAGLAB)

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Education: Ph.D. and M.S. in Applied Physics – Stanford University
B.S. in Physics – University of California Santa Barbara

Mentors: Ross McDonald

Research: Johanna’s research is focused on understanding unconventional superconductivity and the phase diagrams in which this exotic phase occurs. Her research leverages the relationship in solids between the electronic symmetries of a material and the symmetry of the underlying crystal lattice by using strain as a powerful tool to both probe and tune emergent phenomena and phase transitions. In her graduate work, Johanna investigated the large electronic nematic instability found in one family of unconventional superconductor, the Fe-based superconductors, via measurements of the elastoresistivity (the induced resistivity response to strain). At LANL she brings symmetry resolved measurements and strain to the extreme environment of pulsed magnetic fields to study broken symmetry phases and symmetry breaking fluctuations in strongly correlated electron systems.

Bio: Johanna’s interest in science and research began at a young age when her father, a materials scientist, took her into lab for a “Bring Your Child to Work Day.” She assisted with a thin-film single crystal growth and assembling toy model crystal structures. She was a Regents Scholar at the University of California, Santa Barbara where she pursued a B. S. degree in the College of Creative Studies Physics program. She was actively involved in research throughout her undergraduate studies where she worked with Prof. James Allen and Prof. Omar Saleh and was awarded the Arnold Nordsieck Award as a graduating senior for her promise in scientific research. She completed her Ph. D. at Stanford University under the advisement of Prof. Ian Fisher. For her dissertation research she investigated the role of electronically driven rotational symmetry breaking in iron-based superconductors. During her graduate studies she was honored to receive the G. J. Lieberman Fellowship, the Gabilan Stanford Graduate Fellowship, the National Science Foundation Graduate Research Fellowship, and to attend the 66th Lindau Nobel Laureate Meeting. Johanna joined Los Alamos National Laboratory as a Frederick Reines Postdoctoral Fellow in 2020.