Author: zzlalo

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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A National Laboratory Internship Experience

Joey Hart is a PhD Candidate in the Department of Mathematics, North Carolina State University I had a very interesting, and on some levels unique, internship experience in the Optimization and Uncertainty Quantification Department at Sandia National Laboratories. The origins of my internship came several months before through collaborations with my eventual mentor on another project Bart van Bloemen Waanders. During the 2016-2017 academic year, I was a graduate research fellow in the SAMSI (Statistics and Applied Mathematical Sciences Institute) program on optimization. Through this fellowship I began collaborations with several applied mathematicians and computational scientists. Our work led to Bart inviting me to apply for an internship under him in the upcoming summer of 2017, thus the story begins.

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Mathematicians are Needed in Industry

Greg Coxson At this point in my career, I have worked at a number of organizations, usually technology companies with military contracts. I am convinced that mathematicians strengthen organizations, and sometimes make revolutionary changes, often in small ways that are not celebrated as often as they should.

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Unsolicited Basketball Coaching Advice from a Student Researcher

Charlotte Eisenberg This summer I spent a lot of time with NCAA Division I basketball statistics. As a student researcher at Davidson, I examined at least a hundred statistics tracked across the division, from the height and experience of the players to the team free throw and turnover percentages, each potentially linked to the success of a team. My goal was to find the strongest correlations and use them to predict the outcomes of games. Many of the statistics most strongly correlated with winning games, like offensive and defensive efficiency (points and points against per 100 possessions), felt more descriptive than predictive. I became curious about what sub-statistics contributed to efficiency that could be more easily isolated and coached. What statistics would an analytics minded team focus on to see the greatest increase in their win percentage?

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