- B.S, Statistics, Nankai University, China, 2013
- M.S, Statistics, Michigan State University, USA, 2015, GPA 4.0/4.0
- Ph.D, Statistics, Michigan State University, USA, 2020, GPA 4.0/4.0
- Jun. 2020 - Current, Applied Research Data Scientist, Forecasting at Linked[IN] Corporation.
- Aug. 2019 - Jun. 2020, Research assistant, Department of Statistics and Probability, Michigan State University
- Research neural networks in high-dimensional low sample-size (HDLSS) data problems.
- May. 2019 - Aug. 2019, Applied Researcher, Forecasting [IN]tern at Linked[IN] Corporation.
- Design and use machine learning models to forecast site traffic and build pipeline.
- Aug. 2013 - May. 2019, Teaching assistant, Department of Statistics and Probability, Michigan State University
- Lead undergraduate statistics recitations and labs
- Work in the statistics helproom
- Design and teach undergraduate courses as a lecturer
- Yang, K., & Shen, X. (2017). On the selection consistency of Bayesian structured variable selection. Stat, 6(1), 131-144.
- Yang, K. & Maiti, T. (2018). Ultra high dimensional generalized additive model estimation with high accuracy. (Presented on JSM 2018, under revision with peer review journal)
- Yang, K. & Maiti, T. (2019). On the Classification Consistency of High-Dimensional Sparse Neural Network, 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Washington, DC, USA, 2019, pp. 173-182.
- Yang, K. & Maiti, T. (2020). Statistical Aspects of High-Dimensional Sparse Artificial Neural Network Models. Machine Learning and Knowledge Extraction. 2020, 2(1), 1-19.
- Yang, K. & Mairi, T. (2020). Ensemble Neural Network Variable Selection in High-dimensional Regression and Classification Problems.
- Expert in Python, R and LaTeX
- Proficiency in Matlab, Linux, Git and SQL
- Experience in C++, IOS (Swift), Scala, Spark, SAS, Minitab and Gurobi
- Also uses Microsoft Office
- Machine learning: regression, classification, clustering, dimensionality reduction, time series forecasting, neural networks, tree methods, ensemble
- Statistics: confidence intervals, hypothesis testing, A/B testing, survival analysis, design of experiment, Bayesian modeling, stochastic process, spatial statistics
- Computer science: data structure, optimization, algorithm
- University Fellowship, Nankai University, 2009
- Honorable mention prize, North America Mathematical Contest in Modeling, 2012
- Scientific and Creative award, Nankai University, 2012
- William L. Harkness Award, for excellence in teaching, Michigan State University, 2017
- SAMSI deep learning workshop travel award, SAMSI, 2019
- IEEE 6th DSAA travel award, IEEE, 2019
- SAS Certified Base Programmer for SAS 9, issued Feb 2015
- SAS Certified Advanced Programmer for SAS 9, issued Mar 2015
- Neural Network and Deep Learning on Coursera, issued Oct 2019
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization on Coursera, issued Oct 2019