Linlin Chen Headshot

Linlin Chen

Associate Professor

School of Mathematical Sciences
College of Science

585-475-7619
Office Hours
By Appointments.
Office Location

Linlin Chen

Associate Professor

School of Mathematical Sciences
College of Science

Education

BS, Peking University (China); Master of Computer Science, Rice University; MA, Ph.D., University of Rochester

585-475-7619

Select Scholarship

Journal Paper
Chen, Linlin, et al. "Transcriptome Signature in Young Children with Acute Otitis Media due to Streptococcus Pneumoniae." Microbes and Infection 14. (2012): 600-609. Print.
Linlin, Chen, et al. "Hypertonic Saline and Desmopressin: A Simple Strategy for Safe Correction of Severe Hyponatremia." American Journal of Kidney Disease. (2012): doi:10.1053/j.ajkd.2012.11.032. Web.
Published Conference Proceedings
Chen, Linlin, et al. "Sentinel Lymph Node: A Tale of Two Methods, A Natural Experiment." Proceedings of the Annual Meeting of the American Roentgen Ray Society (ARRS). Ed. American Roentgen Ray Society. Vancouver, BC: n.p., 2012. Print.
Formal Presentation
Chen, Linlin. “Overcoming Adverse Effects of Correlationsin Microarray Data Analysis.” 2010 Joint Statistical Meetings. Vancouver, Canada.August, 2010. Presentation.

Currently Teaching

STAT-406
3 Credits
This course is a continuation of STAT-405 covering classical and Bayesian methods in estimation theory, chi-square test, Neyman-Pearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference.
STAT-511
3 Credits
This course is an introduction to the statistical-software package R, which is often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions; graphics; matrices and arrays; simulations and app development with Shiny.
STAT-584
3 Credits
This course is intended to introduce students to categorical data analysis. Topics include: contingency tables, matched pair analysis, Fisher's exact test, logistic regression, analysis of odds ratios, log linear models, multi-categorical logit models, ordinal and paired response analysis.
STAT-611
3 Credits
This course is an introduction to the statistical-software package R, which is often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions; graphics; matrices and arrays; simulations and app development with Shiny.
STAT-641
3 Credits
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building.
STAT-784
3 Credits
The course develops statistical methods for modeling and analysis of data for which the response variable is categorical. Topics include: contingency tables, matched pair analysis, Fisher's exact test, logistic regression, analysis of odds ratios, log linear models, multi-categorical logit models, ordinal and paired response analysis.

In the News

  • November 15, 2021

    two researchers wearing masks and sitting next to a computer setup.

    Engineering faculty awarded NSF funding to improve computing system memory

    Dorin Patru and Linlin Chen, faculty-researchers at RIT, received a grant from the National Science Foundation to upgrade functions of programmable memory. They, along with colleagues from University of Rochester, will develop new algorithms to improve the internal computing memory system to enable scalable and more robust performance.