is a data visualization researcher
based in Montreal, Canada.
I like to give talks about my personal and professional interests, and I try to record them and post them here, whenever possible. If you would like me to give a talk at an event, please reach out!
I gave a talk at Montreal Python where I showed a diagram I’ve been working on to capture and explain how the various pieces of the Python data visualization landscape fit together. My presentation is first, starting about 7 minutes into the video.
I was on a panel at PyData Global 2021 where folks representing various Python “dashboarding” frameworks compared and contrasted their work with Dash, which I represented.
I gave a talk at PyData Global 2021 that pulls together some ideas about why interactive data visualization matters into what I hope is an interesting and useful framework.
I was very proud to introduce Dashboard Engine to the world, as part of the Dash Enterprise 5.0 announcement webinar. I’ve been the product manager and team leader for this project for 18 months and it’s really gratifying to see it come to fruition.
I was recently interviewed on the IQT Podcast about Visualizing Data During a Pandemic, and how Plotly is contributing to COVID-19 response.
I gave a talk at the Data Science, Design and Technology Montreal meetup which was a lot of fun, especially when other members of the community presented the apps that they'd created with Dash!
I recently did a guest talk at the Arup Montreal office regarding the differences between Software Product Organizations and Professional Services Organizations.
My friend Mark Weiss recently started a podcast called Using Reflection and I was pleased to be interviewed as a guest on his 6th episode. We had a great chat about datavis and engineering ethics, among other topics.
I recently did a guest lecture (in French!) at the Université de Montréal in the context of the École d’été en Architecture de l’information (Summer program for Information Architecture).
Visualizing datasets as circle-and-arrow networks or graphs is a popular and easy way to make attention-grabbing graphics. As the number of data points grows, however, these graphics become crowded and marginally useful. Dimensionality-reduction algorithms such as t-SNE represent a different approach to visualizing the relationships between large numbers of data points, which in certain cases can produce graphics which do not suffer from the same types of problems as graph-visualization approaches. In this talk I compare and contrast the two approaches and give pointers to those who wish to try them out.
I presented MLDB today at the BigData Innovators Gathering (BIG) 2016 conference.
I was recently invited to give a talk about auction theory and online advertising at Concordia University for a course entitled Social and Information Networks, which uses a really interesting textbook called Networks, Crowds, and Markets.
As you can see in the video above, during the talk I just scrolled through an R file in RStudio. What you see below is the result of slightly modifying that file and running it through the RMarkdown process to capture the output.
Earlier this year, I collaborated with a reporter from the Montreal Gazette to analyze a dataset containing information about 1.4 million service requests received by the City of Montreal from its citizens. The resulting article was entitled "Montreal's 311 records shed light on residents' concerns — to a point" and credits me at the bottom. I have also published my own interactive analysis of the dataset here: Montreal 311 Service Requests, an Analysis. The dataset, obtained from the city's Gestion des demandes clients (GDC) system via an Access to Information request, covered the five years from 2008 to 2012 and contained the date and a very short description for each request, and in most cases, an address. The service requests were received by the city through its 311 phone line or at service counters throughout the city.
I recently organized and MC’ed the fifth Visualization Montreal meetup, and I think it was a great success! The concept was to have a series of 7-minutes-max flash presentations from Montrealers where each one would show off a single visualization project. The rules were: no slides, no tools, just one publicly available data visualization. We had 12 presenters including me, with a good mix of types of data and visualizations. Below is the list of visualizations that were presented, and you can find photos of the event here.
The visualization I presented at VisMtl 5 was entitled “Canadian Members of Parliament in 2012 by Province, Party, Age & Gender” and is shown above.
I gave a talk at in Barcelona at the PAPIs.io 2014 Predictive APIs conference last November.
Video and slides from my talk at the kickoff of Big Data Week Montreal 2014.
I was invited to speak on a panel at a Rubicon Project product launch, and this is the video of the event.