Simulating massive star formation
My scientific research in astrophysics is focussed on understanding the processes that influence the formation of the largest stars in the recent universe. These beasts, sometimes 10-100x more massive than our sun, exert an outsize influence on the evolution of galaxies, produce most of the UV radiation in the universe, and are responsible for the creation of many of the heavy elements through stellar burning and supernova explosions. They form in only the most extreme environments, which makes them tricky to model.
We use supercomputer simulations to recreate these extreme environments, including supersonic turbulence, interstellar dust, magnetic fields, and radiation feedback. My PhD research has been in correctly modeling the radiation feedback from these extreme stars on their surrounding gas envelopes. This involved combining various methods for processing radiation on adaptive grids and creating a hybrid radiation code. You can see our methods paper on the arXiv.
Data-driven adaptive training for aerospace
Around 2013, I was approached by Adolfo Klassen to consider applications of data science in the aerospace simulation and training sector, a $4B/annum industry. We believed that traditional methods of training flight crews, which are based on the number of hours spent in the air and in simulators, could be dramatically improved.
The future of education is personalization: machine learning algorithms that tailor training to the individual needs of the student. We are seeing the beginnings of this in MOOCs. By contrast, proficiency-based training is an ideal in aerospace that is still far from being realized.
We incorporated and began a pilot project with Seneca College Flight School in Toronto to begin developing data-driven adaptive training for online platforms.
To learn more, visit our website.
For several years during graduate school, I maintained a blog where I explored ideas relating to expert performance, personal development, and academic culture.
One of the major issues in higher education today is that universities produce far more PhDs than can reasonably expect to find employment within academia. Does that mean we have too many PhDs? I wager the answer is ‘no’. There is no shortage of important problems in need of smart, qualified people to tackle them: energy, climate change, disease, poverty, and agriculture.
The project was an interesting way for me to explore ideas related to deep work, personal development, academic life, and finding balance. I’ve now shut down the blog, but I’ve migrated some of my favourite posts over to Medium, and I may continue to contribute under that heading.