Yoonkyung Lee facts for kids
Quick facts for kids
Yoonkyung Lee
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| Alma mater | Seoul National University University of Wisconsin-Madison |
| Known for | kernel method dimensionality reduction machine learning |
| Scientific career | |
| Fields | Statistics |
| Thesis | Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data (2002) |
| Doctoral advisor | Grace Wahba |
Yoonkyung Lee is a smart professor who teaches Statistics at Ohio State University. She also works with computer science and engineering students there. Her special research helps computers learn and understand data better. She uses math ideas like kernel methods, dimensionality reduction, and regularization to make machine learning programs smarter.
Contents
What is Yoonkyung Lee's Career Path?
Early Education and Degrees
Yoonkyung Lee started her journey in Korea. She studied computer science and statistics at Seoul National University. She earned her first degree in 1994 and a master's degree in 1996.
Advanced Studies and Research
She then moved to the United States to continue her studies. In 2002, she earned her Ph.D. in statistics. This was at the University of Wisconsin–Madison. Her teachers were Grace Wahba and Yi Lin. Her Ph.D. work was about support vector machines. These are special computer programs that help sort and understand data. She used them to study microarray data and information from satellites.
Teaching at Ohio State
After finishing her Ph.D., Professor Lee joined the faculty at Ohio State University in 2002. She worked hard and became a full professor in 2016.
Why is Professor Lee Recognized?
Awards and Honors
In 2015, Yoonkyung Lee received a special honor. She was chosen as a Fellow of the American Statistical Association. This award is given to people who do amazing work in statistics.
Contributions to Science
She earned this award for her important research. Her work on "multicategory support vector machines" helps computers sort many different types of data. She also helped connect the fields of statistics and machine learning. This means she helped people who study numbers and people who make computers learn to work together. She also helped her profession by serving on important committees.
