Ruochen won the Most Outstanding Masters Student Award
Ruochen Jiang won the Most Outstanding Masters Student Award at the UCLA Department of Statistics Commencement 2018. Congratulations Ruochen!
Jessica received the 2018 ICSA Conference Junior Researcher Paper Award
Jessica received the 2018 International Chinese Statistical Association (ICSA) Conference Junior Researcher Paper Award for the manuscript titled “Budget-Constrained Feature Selection for Binary Classification: a Neyman-Pearson Approach,” co-authored with Yiling Chen and Dr. Xin Tong.
Wei (Vivian) received the Dissertation Year Fellowship
Wei Vivian Li received the Dissertation Year Fellowship. Congratulations Wei (Vivian)!
Jessica received the inaugural Johnson & Johnson WiSTEM2D Math Scholar Award
Jessica was named as one of the six inaugural Johnson & Johnson WiSTEM2D (Women in Science, Technology, Engineering, Mathematics, Manufacturing, and Design) Scholars [ Johnson & Johnson Announcement ] [ Johnson & Johnson Profiles ] [ UCLA News ]
Vivian and Jessica published a statistical method for imputing missing values in single-cell gene expression data
[ UCLA News ] Vivian and Jessica published an article on Nature Communications about a statistical method scImpute for imputing missing gene expression values due to dropout events in single-cell RNA-sequencing (scRNA-seq) data.
Vivian and Jessica published a statistical method for estimating mRNA isoform abundance by jointly leveraging multiple RNA-seq samples
Vivian and Jessica published an article on The Annals of Applied Statistics about a statistical method MSIQ for more accurate and robust estimation of messenger RNA (mRNA) isoform abundance from multiple RNA sequencing (RNA-seq) data sets.
Jessica received the Sloan Research Fellowship
Jessica was named as one of the 2018 Sloan Research Fellows. [ Sloan Foundation Announcement ] [ New York Times ] [ University of California News ] [ UCLA News ] [ UCLA Physical Sciences News ]
Jessica and collaborators published an umbrella algorithm and a graphical tool for the Neyman-Pearson classification
Jessica and two collaborators (Dr. Xin Tong at USC and Dr. Yang Feng at Columbia) published an article on Science Advances about an umbrella algorithm to implement general binary classification algorithms the Neyman-Pearson (NP) paradigm, which aims to control the type I error (or the type II error by symmetry) under a pre-specified threshold with high probability while minimizing the other type of […]
Xinzhou and Kexin achieved top performance in the conference round of the NCI-CPTAC DREAM Proteogenomics Challenge
Xinzhou and Kexin were named the best performers in the conference round of the NCI-CPTAC DREAM Proteogenomics Challenge (Subchallenge 1) and presented at the RECOMB/ISCB Conference on Regulatory & Systems Genomics with DREAM Challenges. Congratulations!
Jessica and collaborators published another work on using statistics to quantitate translational control
Jessica and Dr. Biggin published an article on Nucleic Acids Research about using statistics to quantitate translational control in yeast, adding a new work to their line of research on quantitating the central dogma (Li et al, PeerJ 2014; Li and Biggin, Science 2015).