Name: The Austin Python Meetup Monthly Meetup
Start: 2021-10-13T19:00:00-05:00
End: 2021-10-13T22:00:00-05:00

We typically have a main presentation or a series of lightning talks, followed by discussion and Q&A. There is a diversity of domains and experience levels represented, so come with your questions and be prepared to talk about how you use Python!

Talk 1: Gunnar Kleemann, MIDS PhD. - Using Python and a TypeDB knowledge graph to understand virus biology

Talk 2: Dillon Niederhut, PhD - The eight rules of fast code : performance in Python

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Talk 1: Gunnar Kleemann, MIDS PhD. - Using Python and a TypeDB knowledge graph to understand virus biology

Description:
Biological data has always been nuanced and deep. A scientist may spend their whole career probing just a small subset of this information. However, the utility of understanding biological data has become more imperative. The stakes are higher with the recent epidemic and rapid developments in biological technology. In this talk we will demonstrate how we use Python and the TypeDB open-source knowledge graph to better understand viral biology.

Takeaways:
- Biology is complicated and rich
- Deep scientific literature is accessible with the right tools
- Public data, Python and Knowledge graphs are key to lowering the barrier to navigating complex domains

Speaker Bio:
Gunnar is the Principal Data Scientist and owner of the Capital Data Corp of Austin (ACD). He holds a PhD in Molecular Genetics from Albert Einstein College of Medicine, as well as Master’s degrees in evolutionary biology (UNL) and Data Science (UC Berkeley). Most recently he did, a Post-doc on the genomics of reproduction and aging (Princeton University). He is interested in how data science facilitates biological discovery and lowers the barrier to understanding nuanced and complicated scientific data. One of his passions is enabling scientific research through teaching, mentorship, and corporate engagements. In line with this goal, he offers consulting services to local businesses, and teaches data science for the UC Berkeley School of Information and in the community.

Talk 2: Dillon Niederhut, PhD - The eight rules of fast code : performance in Python

Description:
At some point in our lives, most of us have written code that took longer to run than we wanted. This might have been a CLI tool that took so long you could make a cup of coffee while you were waiting for it to finish, or a computational simulation that took so long you missed a paper deadline. In this talk, we'll cover eight simple rules to help you write code in Python that executes in reasonable amount of time.

Speaker Bio:
Dillon is a data scientist at Novi Labs, where he works on forecasting and optimization for the energy sector. Previously, he helped companies with their digital transformation and innovation initiatives at Enthought. He is the founder of adversarial designs, a firm that uses adversarial machine learning to develop objects that fool computer vision systems. He is also a co-organizer of the Austin Python Meetup, and has been an editor for SciPy since 2017. In his spare time, he writes bad poems.

Peter Wang

Dillon Niederhut

Florin Matei

The Austin Python Meetup

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Python

Software Development

Open Source

Computer Programming

Machine Learning

The Austin Python Meetup Monthly Meetup

Rich Internet Applications

Extreme Programming

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