Category: Skill Transfer

From Set Theory To Self-Driving Cars

Zachariah Tyree, PhD (Research Scientist at General Motors R&D) I have a PhD in Pure Mathematics. A common sentiment within this field is that Pure Mathematics sits somewhere above Applied Mathematics and quite a distance above other scientific disciplines. The ranking is even implicit in the name “Pure Mathematics”, as opposed to “Adulterated Mathematics” or perhaps “Contaminated Mathematics.” Most of my peers in graduate school were targeting positions in academia. Initially, so was I. Over time I began to reevaluate my goals, as I found I did not care for the teaching aspect of the profession. I decided instead to target industries known for hiring mathematicians, and so shifted my research area from Logic and Set Theory to Probability and Statistics, with the aim at going to work in finance.

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A Winding Path from Complex Analysis to Computational Biology

Robert Thurman, Principal Computational Biologist,  Seattle Genetics, Bothell, WA Computational biologists come in two types: those who were originally trained mathematically or computationally and then gravitated towards biological problems, and those who were formally trained on the biological side but couldn’t stay away from computers. That generalization is slightly dated, because many colleges now offer interdisciplinary degree programs in computational biology and bioinformatics, but those programs tend to be small and it is safe to say most practioners currently in the field started out as one of the two types. Both perspectives are important.

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How I became a Data Scientist

Bolor Turmunkh, PhD, Data Scientist at Uptake Technologies Inc., Chicago, IL At the beginning of my fifth year of graduate school at the University of Illinois, with thoughts of impending graduation, I started thinking for perhaps the first time in my life about who I wanted to be. I had lived happily as an information hermit for four years. I had spared little thought for anything other than academic research. It would have been handy if I had kept up with career trends, sought-after skills, or internship opportunities. But as they say, the secret of getting ahead is getting started. So, I buckled down and got started.

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What I know now that I wish I had known then!

Kristine Jones, PhD – Senior Data and Applied Scientist, Microsoft When I was first approached about writing this post, I was asked to try to convey what I know now that I wish I had known as I was applying for jobs out of grad school.  My response to questions such as this is always that there is no one course I wish I had taken, no one skill I wish I had acquired, no one opportunity that would have pushed my early career down a dramatically different path. This is not to say that I haven’t reaped the benefits of broad exposure to numerous skills commonly bullet-pointed on tech industry data scientist job descriptions. I would not be doing my due diligence if I pretended otherwise. That being said, hiring decisions for mathematicians based exclusively, or even primarily, on those bullet points are poorly considered. (Learn some coding, stats, and optimization methods, though … it can only help you).

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NSF-IPAM Workshop – 2017 UPDATE

SUMMARY RECOMMENDATIONS NSF-IPAM Mathematical Sciences Internship Workshop Full report available here.  (UPDATES IN BLUE ITALICS) The report above reflects discussions and recommendation from the September 1-2, 2015 NSF-IPAM Mathematical Sciences Internship Workshop held at the Institute for Pure and Applied Mathematics (IPAM) at UCLA. The workshop was organized by Russel Caflisch, Mathematics, UCLA; Alan Lee,... Continue Reading →

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