### Open Source Software in the Sciences — D. Joe Anderson

RIT’s Joe Anderson is going to talk about open source software, and its use in the sciences. He will bust some prominent myths and misunderstandings about open source, and dig into a few key fundamentals about free and open source software that align with some key goals in scientific work.

### 3D printing for scientific visualizations — Daniel Wysocki

3D printing has recently become affordable and quite reliable. It is now being used to help visualize scientific data and mathematical functions – basically anything that can be represented as $f(x, y)$ can trivially be printed.

### Taking the measure of mutation in the light of molecular dynamics — Greg Babbitt

Dr. Gregory A. Babbitt (GAB) T.H. Gosnell School of Life Sciences, Rochester Institute of Technology. Rochester, NY USA

### Tutorial on Vectorized Programming Languages — Daniel Wysocki

Most of today’s data analysis – scientific or otherwise – is performed in a vectorized programming language (e.g., Python+numpy, R, MATLAB). These languages are distinguished from more traditional “scalar” programming languages, in that one performs operations on entire arrays, instead of looping over each individual element.This talk will give a basic overview of the differences between scalar and vector languages, with side-by-side examples (in Python and C). I will explain both the convenience and performance motivations behind using such a language, as well as some downsides.If there is time at the end, I will show off more vectorization tricks in Python. Laptops with Python and IPython are recommended for this part.

### An Introduction to Smoothed Particle (Magneto-)Hydrodynamics — Benjamin Lewis

Smoothed particle hydrodynamics (SPH) is a mesh-free Lagrangian method for solving fluid dynamics problems. SPH has been applied to a wide array of problems, ranging from astrophysical and cosmological simulations to modelling the flow of oil in an engine or waves breaking on a shore. In this I will provide an introduction to SPH, including the underlying mathematical basis. In addition I will cover various potential implementation issues and known pitfalls.