Some talks and papers for 2016

It's been a really busy end quarter of the year, and in the last few months I got around to publishing one paper, and doing a number of presentations.

Back in September I was lucky enough to travel to beautiful Kraków in Poland to talk about data science and IoT-ish stuff at DevDay16.
My talk was titled 'A Homespun Decentralised DIY Data Science Research Pipeline for the Internet of *Your* Things' (quite a mouthful!), and it basically relates my adventures in the last year, trying to move from a software development background into a more data science oriented view of the world. I also rant about privacy for quite a while.

These are the slides and recording of the talk:





The conference was fantastic, flawlessly organised, and they treat speakers incredible well. You should definitely consider their CFP next year!

In October I got the chance to present at the Mobile!16 workshop at SPLASH in Amsterdam. My paper is about requirements for mobile health applications, with an End User Development flavour to it, provided by App Inventor. The paper can be found here, and the slides are:




Finally in November I gave another talk at the PyCon Ireland conference, this time about image classification in Python. During the summer a group of people helped me collect pictures of food, with a view to figure out some way of classifying the contents. There are a few companies out there such as Clarifai and Indico that do a pretty good job at classification and tagging of images (Google and Microsoft also have very good vision products, but I didn't want to go down that path). Their APIs are pretty nice and easy to use, but I wanted to figure out if I could locally reproduce at least some of the results they provide. My first attempt was to do classification with a technique called 'bag of features', which I must say, didn't work incredible well in complicated pictures such as food. After that I followed today's trend of using deep learning (everybody is at it!), more concretely using TensorFlow to retrain the Inception model that Google made available at some point last year. Results, as you can imagine, are much better, even if I can't really explain any of the math behind it!

The title of the talk was 'Introduction to Image Classification in Python: from APIs to Neural Networks', and here you can find my slides (recordings are not available yet):



Oh, and I also got a chance to finally go to the Mozilla Festival in October. We run a session about Paper Badger, but I'll tell you all about it in a different post!