- Worked as a co-first author to completely re-code the previous R program in Python- Optimized the system with parallel computing to improve calculating speed dramatically- Applied statistics in the feature selection to rank the biomedical features with statistical associationevaluation algorithm T-test- Compared common machine learning algorithms such as Support Vector Machine (SVM), KNearest Neighbors (KNN), Decision Tree (DTree) ,Naïve Bayesian classifier (NBayes), LogisticRegression (LR)- Proposed and tested a few novels ideas in the RIFS algorithm which outperforms the existing filterand wrapper feature selection algorithm on both transcriptomic and methylomic datasets- Published the paper on Scientific Reports