A random walk toward a net positive

Derek Kane, Deka Research and Development Avoiding boredom was my earliest career goal. My undergraduate degree was mechanical engineering, and my brother got me a job with him at Itek Optical Systems. Itek made cameras and telescopes, largely for the Department of Defense. The engineering challenges were fascinating, but the analysis and algorithm aspects of the work excited me much more than traditional mechanical engineering. However, my lack of deep mathematical training limited the analyses and algorithm development I could handle. At this job, I also noticed two career paths: one group of older engineers became middle managers whose work looked unbearably dull and who seemed very vulnerable to layoffs. A smaller group of engineers, including my boss, served as technical experts. When a new and innovative solution was required, or when a program stalled because a physical or computational challenge could not be overcome, these experts were consulted. I wanted this job.

Blogpost: Parsa Bakhtary

ParsaBakhtary It is humbling to address future and current mathematicians, but as a former algebraic geometer myself, I will do my best to share with you my story. I work as a data scientist, which the Harvard Business Review in 2012 dubbed “the sexiest job of the 21st century,” at Facebook, which has been ranked by Glassdoor as one of the best companies for which to work. The path that led me from an eager math student who despised applications to where I am today has been a strange one, but the lessons I learned in my undergraduate and graduate math classes have had a profound impact on my ability to analyze concrete problems in industry.

Opportunity: Machine Learning Workshop

Fundamentals of Machine Learning Workshop at Stanford University March 31, 2017 Discover the basics behind the application of modern machine learning algorithms. The workshop instructors will discuss a framework for reasoning about when to apply various machine learning techniques, emphasizing questions of over-­fitting/under-­fitting, regularization, interpretability, supervised/unsupervised methods, and handling of missing data.   For more information, visit the Stanford ICME... Continue Reading →

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