Suggestions
Easton Huch
Statistics & ML Researcher @ The University of Michigan | Freelance Applied Scientist
Easton Huch is a Freelance Applied Scientist currently self-employed, with a strong academic background in statistics. He is a 4th-year PhD student in the Department of Statistics at the University of Michigan, where he has been developing his expertise in statistical methods and applications.14
He holds a Master of Science in Statistics from Brigham Young University, contributing to his comprehensive understanding of data science and economics. Prior to pursuing his PhD, Easton worked as a data scientist in the tech industry for several years, which provided him with practical experience in applying statistical techniques to real-world problems.5
Easton is actively engaged in academic research and discussions related to his field, often sharing insights on platforms like LinkedIn.23 His professional endeavors reflect a commitment to advancing knowledge in statistics through both academic and applied lenses.
Highlights
I've been thinking about a dissertation topic since I finished my master's degree several years ago. I have a strong inclination toward working in an industry data science position when I graduate, so I'd like to choose a topic that would be interesting and useful to applied data scientists. With that background, what are some areas of statistics / ML / data science that, if better understood and developed, would positively impact your work?
I recently found two quotes that summarize my perspective on problem solving within analytics and data science:
“If I had an hour to solve a problem, I’d spend 55 minutes thinking about the problem and five minutes thinking about solutions” – Albert Einstein
“It’s better to solve the right problem approximately than to solve the wrong problem exactly” – John Tukey
Both underscore a theme I’ve noticed in my time at Lucid: understanding the problem is more important than developing a perfect solution. If the solution seems complicated, it might be time to take a step back and examine the problem more holistically. Often, stating the problem in simple terms naturally leads to a simple—but sufficient—solution.
#LucidNerding
