PhD in Statistics, 2014
Previous Education: B.S. in Statistics, Fudan University
Hometown: Rushan, China
Current Job: Senior Data Scientist, CapitalG (formerly Google Capital)
Why did you choose Ohio State for your graduate degree in statistics?
I really loved the breadth of research interests of professors at OSU, the variety of course offerings, and that students get to learn about their own interests through coursework and individual studies before deciding on research directions and advisors. The vibrancy and affordability of Columbus were big pluses for me too.
What was your research focus during your graduate program?
Bayesian Analysis of Non-Gaussian Spatial and Temporal Processes
Describe a memorable moment during your graduate program.
Not one particular moment but I always remember the fun times over drinks with friends from the department at Pint House, The Little Bar, etc., especially after qualifying exams. Grateful to have met a group of diverse, collegial and fun classmates and we have built friendships far beyond grad school (the bonds are strong once you've been through one or two qualifiers together).
Describe your current job and how your time in the program prepared you for a career in your field.
I recently transferred from Google to CapitalG, Alphabet's growth equity fund. We invest in companies around the world that drive market disruption by harnessing long-term technology trends. My role on the team is to build machine learning models to bring data-driven insights into the investment process. I am still benefiting from the extensive and rigorous statistical training I received at OSU. The department offers a wide range of courses (both Bayesian and frequentist approaches are well covered) that allowed me to build a solid (self-evaluated; I hope my advisors don't mind) statistical foundation. I also learned a lot from working with my advisors, Dr. Peter Craigmile and Dr. Xinyi Xu, who have very high standards for research and always pushed me to be rigorous and do higher quality work. Their principles still resonate with me in the corporate world.
Do you have any advice for prospective students or is there anything else you would like to share?
The skills of doing "data science" are becoming rapidly democratized. However, there is a big difference between simply knowing how to run the most popular model and truly having the quantitative maturity to solve a problem which only comes with years of training and experience in my opinion. In terms of learning, my advice would be:
1) be patient and build a solid understanding of the basics
2) be curious about new developments from both academia and industry