Panel: Python in Science

Josh Bloom - moderator

Chris Holdgraf (UC Berkeley - Knight Lab)
graduate researcher in the Knight Lab at UC Berkeley. He studies the way that the auditory cortex of the brain understands sounds. To do this, he builds predictive models of brain activity. He uses these models to understand what aspects of sound the brain “cares” about, and to understand how higher-levels of the brain can influence this process.

David Shapiro (LBNL - ALS)
Received his PhD in physics from Stony Brook University in 2004 studying diffractive x-ray imaging
under Janos Kirz and David Sayre.  He was then a postdoctoral scholar with the Center for Biophotonics at UC Davis 
under Prof. John Spence and Henry Chapman working on the development of Serial Nano-Crystallography, 
now widely in use at X-ray Free Electron Lasers and he continued this research as a LBL Seaborg Fellow from 
2006-2009 (during which time he learned Python!).  He worked on the design of coherent soft x-ray sources at 
NSLS2 (Brookhaven Lab) before joining the ALS as staff in 2012.  He now leads the ALS Microscopy Group and works 
primarily on the development of instrumentation and computational methods for ptychographic x-ray imaging.  
ALS x-ray microscopes generate more than 100 peer reviewed papers per year and currently hold the world 
record for spatial resolution.

Juliane Mueller (LBNL - CRD)
2003-2008: MSc in Applied Mathematics from TU Bergakademie Freiberg, Germany (focus on operations research, thesis work on vehicle routing problems)
2008-2012: PhD in Applied Mathematics from Tampere University of Technology, Finland (focus: development of surrogate model algorithms for computationally expensive black-box optimization problems; applications areas environmental engineering, structural optimization, renewable energy)
2013-07/2014: Postdoc at Cornell University, Ithaca, NY, surrogate optimization of global climate models
08/2014-now: Alvarez Fellow in CCSE group, surrogate model algorithm development, applications in transportation, combustion, ALS lattice optimization

Kyle Barbary (UCB and LBNL)
Cosmology Data Science Fellow at the Berkeley Center for Cosmological Physics. As a cosmologist, he study the "Universe at large": how the Universe has expanded over time and the properties of dark energy, its largest component. He does this using Type Ia supernovae, a type of stellar explosion that can be used as an indicator of distance. These distance indicators allow us to measure how the universe has expanded over the past 10 billion years, looking back to the first third of the Universe's existence!
Recent research focus has been on writing reusable open-source scientific software for astrophysics research. He's a core contributor to the AstroPy project, a community-developed astronomy library for Python. He also develops a Python package specifically for supernova cosmology research called SNCosmo. I've also started developing packages for the Julia programming language, including both astronomy packages and general purpose scientific packages. He's particularly interested in making it as easy as possible for scientists to find, use, and understand the software they need to accomplish their research.