Lauren Burns

Profile Picture of Lauren Burns

Lauren Burns

  • Fox School of Business and Management

    • Statistics, Operations, and Data Science

      • Associate Professor of Instruction

      • Chair


Lauren Burns is an Assistant Professor of Instruction in the Department of Statistical Science. She teaches traditional and online BBA, honors, and general education courses, as well as courses in the Fox MBA and Executive MBA programs. Outside of teaching, Dr. Burns has also worked on various consulting projects for the Center for Statistical Analysis at Temple.

Dr. Burns’ research interests are in the areas of survival analysis, specifically the presence of non-proportional hazards, high-dimensional data, and dimension reduction. She focuses mostly on applications in Biostatistics dealing with micro-array gene expression data.

Before beginning her career here, Dr. Burns received her M.S. degree and PhD in Statistics from Temple’s Fox School of Business. She also studied at Muhlenberg College, where she earned a Bachelor of Science degree in Mathematics and Economics.

Research Interests

  • Survival analysis
  • Non-proportional hazards
  • Microarray gene expression data analysis
  • High-dimensional data
  • Dimension reduction

Courses Taught




STAT 0827

Statistical Reasoning & Games of Chance


STAT 1001

Quantitative Methods for Business I


STAT 1102

Quantitative Methods for Business II


STAT 1902

Honors Quantitative Methods for Business II


STAT 2103

Statistical Business Analytics


STAT 5001

Quantitative Methods for Business


STAT 5607

Advanced Business Analytics


STAT 5801

Statistical Analysis for Management


Selected Publications


  • Asadi, M., Devarajan, K., Ebrahimi, N., Soofi, E., & Spirko‐Burns, L. (2022). Elaboration Models with Symmetric Information Divergence. International Statistical Review, 90(3), 499-524. Wiley. doi: 10.1111/insr.12499.

  • Spirko-Burns, L. & Devarajan, K. (2021). Supervised Dimension Reduction for Large-Scale "Omics" Data With Censored Survival Outcomes Under Possible Non-Proportional Hazards. IEEE/ACM Trans Comput Biol Bioinform, 18(5), 2032-2044. United States. 10.1109/TCBB.2020.2965934

  • Spirko-Burns, L. & Devarajan, K. (2020). Unified methods for feature selection in large-scale genomic studies with censored survival outcomes. Bioinformatics, 36(11), 3409-3417. England. 10.1093/bioinformatics/btaa161