Events

Matplotlib: The Hard Way

The Hacker Within

Hacking

November 8, 2016
4:00pm to 5:30pm
190 Doe Library
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This Week's The Hacker Within

  • Topic: matplotlib
  • Speaker: Yu Feng

http://thehackerwithin.github.io/berkeley/

This is a weekly meeting for sharing skills and best practices for scientific computation. Based on The Hacker Within Scientific Computing Group from the University of Wisconsin–Madison, the UC Berkeley chapter uses this as a structured set of skill-sharing sessions for scientific software development (e.g., testing, data management, version control, literate programming, etc. ) The goal is to learn cool skills and incorporate these practices into our workflows. People from all scientific disciplines are welcome. This meeting would be a great venue for describing neat tips and tricks for efficiency, introducing new libraries, showing off useful features of a scientific code you're using, or bringing up a computational problem you're having.

Participating is really easy: 

  • First, you'll show up. 
  • At 4:00 p.m., there will be tutorial/discussion about a scientific computation topic. 
  • Next, there will be a time for a couple of lightning talks, which are five- to ten-minute blasts of information about a particular topic or question of interest to the group.
  • Feel free to hang around and discuss your needs and current projects with other attendees.

To volunteer to give a talk, just let the listhost know by email at ucb-hacker-within@lists.berkeley.edu.

Speaker(s)

Yu Feng

BIDS Alum – Data Science Fellow

My study in cosmology focuses on the formation of galaxies in the large-scale structure of the Universe. Cosmology is a data-driven science. I develop the necessary software and tools that can efficiently handle these data on platforms from laptops to supercomputers, including (1) massively parallel software to solve gravity and hydrodynamics on tens of thousands of computing nodes, (2) tools to visualize density estimation of large particle datasets with hundreds of billions of particles, and (3) algorithms to understand the clustering of galaxies. I also contribute to open source data science software projects as a user developer. I strongly believe in the power of adequate tools in data-driven science research.