Li Chen is currently a data scientist at Intel Corporation. She focuses on developing advanced and principled machine learning methods for cloud workload characterization and cloud computing performance. Li Chen received her Bachelor’s degree with summa cum laude from the University of Oregon within three years, where she majored in both pure and applied mathematics. She received her first Master’s of Science degree at Oregon State University, where she majored in mathematics with a concentration in actuarial mathematics. Li Chen joined the PhD program in the Department of Applied Mathematics and Statistics at the Johns Hopkins University in 2010. She received her second Master’s degree in Engineering in Applied Mathematics and Statistics in 2013. She received her PhD degree in Applied Mathematics and Statistics in May 2015. Her research interests primarily include applications of machine learning, statistical pattern recognition, computational statistics, random graph inference, data mining, inference for high-dimensional data, dimension reduction and cloud performance analytics. Her research has been featured in a number of pioneering scientific and engineering journals and conferences including IEEE Transactions on Pattern Analysis and Machine Intelligence, Annals of Applied Statistics, Parallel Computing, and AAAI Conference on Artificial Intelligence. She has given over 30 technical presentations, including at PNSQC 2015, the Joint Statistical Meeting (the largest statistics conference in North America), AAAI conference, International Joint Conference on Artificial Intelligence, and Spring Research Conference on Statistics and Industry Technology.
- Cultivating Software Performance in Cloud Computing- Li Chen, Pooja Jain, Kingsum Chow, and Colin Cunningham, 2016 Technical Paper, Abstract, Paper, Slides, Notes, Video.
- Brewing Analytics Quality for the Cloud Performance- Li Chen, Kingsum Chow, Pooja Jain, Emad Guirguis, Tony Wu, 2015 Technical Paper, Paper, Slides, Notes, Video.