Immerse Meeting 2017

The Immerse Meeting 2017 Conference took place on June 7 - 9 at Carleton University. Updates can be found via Twitter @GamesInstitute

Π ∩ ϟ presented the results of an experiment done at the University of Waterloo for a first year honors math class. Our work would not have been possible without Epic Games, HTC Vive, NVIDIA & Wellcome Trust. On behalf of all of us at Π ∩ ϟ, we humbly thank you for support :)

Gamifying Education

First year math student trying a linear algebra demo.

First year math student trying a linear algebra demo.

Learning STEM subjects can be challenging, especially if the material or problem of interest is difficult to visualize. We firmly believe that it is time to embrace the gamification of education because it enables people to explore with the freedom to fail and experiment.

Our team is working on creating bite size post-secondary STEM VR education demos - if you have an idea for a VR lesson, or want to get involved please contact us!

Virtual Genome Project

Team Π ∩ ϟ participated in an international experiment called the Big Data VR Challenge, hosted by Wellcome Trust and Epic Games this past April-July 2015. The purpose of the Challenge was to explore if the video games industry could help scientists interrogate big data sets in virtual reality (VR). Our group was selected to work in collaboration with Martin Hemberg, a computational genomics researcher at Sanger Institute. The task was develop next generation VR environments to interrogate and visualize genomics data sets, to be presented in July at Develop Brighton 2015.  

Team Π ∩ ϟ "presented an intuitive way to interrogate genomics data through the use of head tracking, using motion to produce colourful patterns that correspond to variations of genetic parameters."  -UE4 Blog

Our group is continuing collaborative efforts with Martin Hemberg post challenge, and we have extended our efforts by including Canadian genomics researcher Andrew Doxey from the University of Waterloo. The platform and tools being developed will be extended beyond genomics and applied to modern big data problems.

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