How I Ended Up in Geophysical Fluid Dynamics

How I Ended Up in Geophysical Fluid Dynamics

Lately, I’ve been disclosing the various biases I bring to practicing and enabling Data Science. Motivated by my decision to (finally) self-curate an online, multimedia portfolio, I felt such biases to be material in providing the context that frames this effort. Elsewhere, I’ve shared my inherently scientific bias. In this post, I want to provide additional details. These details I’ve been able to extract verbatim from a blog post I wrote for Bright Computing in January 2015; once I’d settled on geophysics (see below), I aspired to be a seismologist … but, as you’ll soon find out, things didn’t pan out quite the way I’d expected:

I always wanted to be a seismologist.

Scratch that: I always wanted to be an astronaut. How could I help it? I grew up in suburban London (UK, not Ontario) watching James Burke cover the Apollo missions. (Guess I’m also revealing my age here!)

Although I never gave my childhood dream of becoming an astronaut more than a fleeting consideration, I did pursue a career in science.

As my high-school education drew to a close, I had my choices narrowed down to being an astronomer, geophysicist or a nuclear physicist. In grade 12 at Laurier Collegiate in Scarboro (Ontario, not UK … or elsewhere), I took an optional physics course that introduced me to astronomy and nuclear physics. And although I was taken by both subjects, and influenced by wonderful teachers, I dismissed both of these as areas of focus in university. As I recall, I had concerns that I wouldn’t be employable if I had a degree in astronomy, and I wasn’t ready to confront the ethical/moral/etc. dilemmas I expected would accompany a choice of nuclear physics. Go figure!

And so it was to geophysics I was drawn, again influenced significantly by courses in physical geography taught by a wonderful teacher at this same high school. My desire to be a seismologist persisted throughout my undergraduate degree at Montreal’s McGill Universitywhere I ultimately graduated with a B.Sc. in solid Earth geophysics. Armed with my McGill degree, I was in a position to make seismology a point of focus.

But I didn’t. Instead, at Toronto’s York University, I applied Geophysical Fluid Dynamics (GFD) to Earth’s deep interior – mostly Earth’s fluid outer core. Nothing superficial here (literally), as the core only begins some 3,000 km below where we stand on the surface!

Full disclosure: In graduate school, the emphasis was GFD. However, seismology crept in from time to time. For example, I made use of results from deep-Earth seismology in estimating the viscosity of Earth’s fluid outer core. Since this is such a deeply remote region of our planet, geophysicists need to content themselves with observations accessible via seismic and other methods.

From making use of Apache Spark to improve the performance of seismic processing (search for “Reverse-Time Seismic Migration” or “RTM” in my Portfolio), to the analysis of ‘seismic data’ extracted from Twitter (search for “Twitter”in my Portfolio), seismology has taken center stage in a number of my projects as a practitioner of Data Science. However, so has the geophysical fluid dynamics of Earth’s mantle and outer core. Clearly, you can have your geeky cake and eat it too!

Data Science: Celebrating Academic Personal Bias

Data Science: Celebrating My Academic Bias

In a recent post, I introduced my Data Science Portfolio. After describing the high-level organization of the Portfolio, I noted:

At the end, and for now, there is a section on my academic background – a background that has shaped so much of those intersections between science and technology that have been captured in the preceding sections of the portfolio.

Even in this earliest of drafts, I knew that I was somewhat uncomfortable with a section dedicated to academics in my Portfolio. After all shouldn’t a portfolio place more emphasis on how my knowledge and skills, academic or otherwise, have been applied to produce some tangible artifact?

Upon further reflection, I currently believe what’s material in the context of a portfolio is some indication of the bias inherent in the resulting curated showcase of one’s work. Of course to some degree the works presented, and the curation process itself, will make self-evident such personal bias.

Whereas it may make sense for an artist not to overtly disclose any bias with respect to their craft, or a curated collection their work, I currently perceive absolutely no downside in sharing my personal bias – a bias that in my own case, I believe reflects only in positive ways on the Portfolio as well as the individual items included in it.

To this end, and in the spirit of such a positive self-disclosure, my personal bias reflects my formative years in science – a background to which I well recall significant contributions from high school, that were subsequently broadened and deepened as an undergraduate and then graduate student. Even more specifically in terms of personal bias was my emphasis on the physical sciences; a bias that remains active today.

As I’ve started to share, through such posts as the one on the mathematical credentials I bring to Data Science, my choice to pursue the physical sciences was an excellent one – even through the self-critical lens of personal hindsight. An excellent choice, but albeit a biased one.

The very nature of Data Science is such that each of us carries with us our own, wonderfully unique personal bias. As we necessarily collaborate in team, project and organizational settings, I believe it’s important to not only ensure each of us preserves their personal bias, but that we leverage this perspective as fully and appropriately as possible. As a consequence it is much more likely that everyone we work with, and everything we work on, will derive maximal value.