Designing, building, running, and maintaining supercomputers can be a daunting task. Thankfully, the US has XSede, an NSF-funded array of supercomputers, staff, and support that makes supercomputing available to all academic researchers in the country. In this talk I’ll tell you about XSede and the resources it provides, along with who may apply (probably not you, but definitely some people you know!), how much time you can get (quite a bit!) and how much work it takes to get started (1 page! Really, just 1 page!).

In this workshop, I will give an introduction to vectorized programming in Python. While Python is not typically the most efficient language, with the right libraries installed you can speed things up immensely. I will show you how to turn make your scientific Python code into highly efficient CPU code with the Numpy library, and how to translate that into efficient GPU code with the Cupy library.

First meeting of the Fall 2019 semester. This will serve as an introduction for new members, including a quick recap of what the group has done in the past. Then we will talk about what people would like to see done this semester, such as subjects they’d like to learn or speak about, and what we can do to make our meetings even better.

Statistical Machine Learning plays a central role in the emerging field of Data Science, with supervised learning as one of its main pillars. When it comes to supervised learning methods, one of the most formidable paradigms from a point of view of optimal prediction is the ensemble learning paradigm according to which instead of seeking to select a single model out of many, one combines/aggregates many good candidate models in some ways through model averaging (regression) or majority voting (classification).