Adding second factor authentication with FIDO U2F

This is the first semester since the Fall of 2015 that I have not taught a course with the Foundation for Advanced Education in the Sciences. It was a pleasure teaching and I was lucky enough to spend most of my time on a course I had designed. For the Spring 2016 semester I designed the syllabus and began teaching a course on machine learning and object oriented python. I chose to include a web application as I felt it exposed the students to some unfamiliar ideas.

Most of the students were fellow scientists. Many only had previous experience writing scripts for use in their own research. Not trusting user input was often a novel concept. During the course I only had a couple of hours to introduce web applications. This meant I skipped over many important topics. I intend this post to be the first in a collection moving beyond the basics for anyone still new to these concepts. I will start with a basic background but the actual implementation will hopefully be new for most. If implementing web application authentication is familiar to you then skip ahead to the implementation.

Let me know if there are topics you think I should cover moving forward.

In this post I will cover authentication, specifically adding a second authentication factor for additional security.

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PyDataLondon January talk

I'm just back from the UK where I spent a couple weeks catching up with family and friends. My visit happened to conincide with one of the monthly PyData London events so I attended and gave a lightning talk on image segmentation in medical applications.

They have built a really vibrant community and it was great meeting over 200 data enthusiasts.

The slide deck is available here. Unfortunately there was no video. The content is a more technical version of the presentation I gave at the NIH Pi Day 2017 event where there was video.

Technique and review articles on exosomes

I've studied exosomes since the Summer of 2007 when I did my MSc dissertation project and then PhD in the laboratory of James Dear. When it came time to move on I was fortunate in finding my current position where I could explore new areas without moving away entirely from the exosome field where I had so much experience.

An opportunity to revisit the exosomal field came at the beginning of 2016 when we were invited to write three separate book chapters and review articles on exosomes. The third manuscript, a chapter in the book "Drug Safety Evaluation", has just been published online.

Inevitably the manuscripts have some overlap but they each focus on different aspects of exosomes and their study. The highlight was having a figure from our article in the journal of cellular physiology on the front cover of the issue.

The three manuscripts are:

Urine Exosome Isolation and Characterization is focused on the methods we use to collect, process, and characterize exosomes.

Urine Exosomes: An Emerging Trove of Biomarkers is a review of the potential and challenges in bringing exosome based biomarkers into clinical use.

Quantification of Exosomes is a review of the options available for determining the concentration of exosomes.

NIH Pi Day 2017

At the NIH Pi Day celebration I gave a lightning talk on applying deep learning to histology images. A video of the event is now available at NIH Videocast.

During the one hour event there were presentations on ten different projects. I was the second speaker and began at 8:48.

I am using deep learning to identify glomeruli in kidney biopsies. When we are unsure about the specific type of kidney disease a patient has we take a small biopsy to look at the kidney. It is often differences in the glomeruli that define the type of disease. Pathologists study the biopsy to define the type of kidney disease. These skilled pathologists spend significant time locating the glomeruli. A machine can do this simple step. The pathologist can then focus on the harder disease identification task.

Transportation Techies: Capital Bikeshare TSP

The theme for the Transportation Techies event this month was Capital Bikeshare. This is the bike sharing service in Washington DC. Information is available on every trip and every station. Lots of analyses are possible with all this data. This event was the seventh on this theme.

I had not worked with geographical or transportation data before this so I learned a lot. I treated the stations as cities in the traveling salesperson problem. I then calculated the shortest path visiting all the stations.

I was able to do this using open data and open source software. This included customizing the calculation of distances for cycling.

The slides I presented include links to all the data and software used. The code I wrote is available on github. I include a Dockerfile for running the routing software with data for the Washington DC region.