His story: Originally from the Republic of Korea, Young completed his bachelor studies at Stanford University where he obtained his Bachelor of Science in Mathematics. After graduating, he worked at a financial startup where he tried several Machine Learning algorithms to develop automatic trading strategies. While working in that position, he came to realize that having a deeper theoretical understanding of statistics would be helpful in the long run. For that reason, he started to consider grad school.
Young was hesitant to quit his job and become a student again until his coworker reminded him that the younger he is, the easier it will be to learn new concepts – and he was never going to be as young again as he was at that time. With that encouragement, Young decided to come to the University of Michigan.
When it came to choosing a grad school, there were a number of reasons why U of M was the ideal choice for Young, but the foremost reason was because of the people in the department. “Faculty members’ doors are always open, our staff are always eager to help, and the overall atmosphere among our cohorts is friendly and nice,” said Young.
His interests: Broadly speaking, one thing that unifies Young’s research topics is “online learning.” This area of statistics studies decision making processes where the data instances arrive sequentially. Sequential decision making becomes more natural especially in the era of big data (e.g., online ads recommendation or autonomous driving).
His advice for future students of Statistics: One struggle Young has faced as a Ph.D. student in statistics is that many researchers and practitioners from different backgrounds try to apply Machine Learning techniques in their fields. With so many different parties in play, it becomes more and more important to communicate efficiently with everyone. Therefore, it is always a big challenge to explain theoretical concepts of Machine Learning using the language of the audience. Being able to communicate in Layman’s terms and across various fields of learning is absolutely essential.
Before studying Statistics, Young had a strong background in mathematics which he says has been very helpful. “Even though Statistics is a different discipline than Mathematics, my math background helped me a lot when learning fundamentals in Statistics. It is also true that there are many applied ongoing research opportunities, but having a deep theoretical understanding only helps. I recommend spending a significant portion of time, especially at the beginning, in understanding those theories.”
What’s next for Young?: Young intends to pursue a career as a data scientist or software engineer with a company that is actively trying to apply Machine Learning to real-life situations.