The Austin Python Meetup Monthly Meetup

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: Greg Freeman - An overview of ROS: Robot Operating System

Talk 2: Ryan Henning - Simulated Autonomous Cars with Python

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Talk 1: Greg Freeman - An overview of ROS: Robot Operating System

Description: ROS is a framework, set of tools and packages in python and c++ for robotic applications with state of the art algorithms. I will be covering an overview of the ROS ecosystem, important concepts, getting started, major packages and applications that ROS is tailored for.

Bio: I lead a mechatronics and robotics team at Applied Materials. I use python and other languages to develop algorithms, control systems and vision systems. I work in the robotics space, but am learning ROS with side projects and would like to share what I've learned.

Talk 2: Ryan Henning - Simulated Autonomous Cars with Python

Description: This will be a hands-on presentation where we'll use Python to program and race simulated autonomous cars. We'll begin with a quick presentation about the AutoAuto learning platform (and its simulator), then we'll move on to the hands-on portion.

The simulation platform is a real-time physics engine in a multiplayer world. Your autonomous car will interact and collide with other attendees' cars as it races through the track. The same learning platform is used in K-12 schools and universities for teaching Python.

Attendees with little to no Python experience can do some introductory lessons, while the more advanced Python users can compete in an autonomous race at the end of the event. Attendees are invited to try and fail at this event, as failure is more celebrated than success. We can all learn and laugh together!

Bio: Ryan Henning is a Software Engineer turned Data Scientist turned Educator. Ryan worked in industry on a large C++ data acquisition framework as a Software Engineer before returning to academia. In graduate school, Ryan researched deep neural networks for image processing and classification, specifically using neural networks to detect a symptom of pediatric cancer. Now Ryan leads the development of the AutoAuto learning platform and its hardware products.