Kan Shao

(812) 856-2725
PH 029

  • Carnegie Mellon University, Ph.D. in Civil & Environmental Engineering and Engineering & Public Policy, 2011
  • Carnegie Mellon University, M.S. in Machine Learning, 2011
  • Carnegie Mellon University, M.S. in Civil & Environmental Engineering, 2007
  • University of Science & Technology Beijing, B. Eng in Environmental Engineering, 2006
  • SPH-V 546 Risk Assessment, Policy, and Toxic Regulations
  • SPH-V 241 Foundations of Environmental Health
  • SPH-V 625 Integrated Modeling for Environmental Health Research
  • Principal Investigator. IU President's International Research Awards (PIRA). Project Title: Probabilistic Risk Assessment to Enhance Risk-Based Decision Making for Food Safety Regulation in China - A Case Study of Inorganic Arsenic in Rice. $149,862 (2017-2020)
  • Principal Investigator. Three projects supported by IUB-SPH Developmental Research Grants for Pre-Tenure Faculty. $20,000. (2015-2017)
  • Principal Investigator. US Environmental Protection Agency. Project Title: Dose-Response Analysis for Arsenic Risk Assessment. $2,767. (2014-2015)
  • 05/2017 - present, Treasurer/Secretary, Risk Assessment Specialty Section (RASS), Society of Toxicology
  • 12/2011 - 11/2016, Chair, Chair-Elect, Vice Chair of Dose-Response Specialty Group, Society for Risk Analysis
  • 8/2011 - 7/2014, ORISE Postdoctoral Fellow, US EPA National Center for Environmental Assessment
  • 06/2016. Wheeler MW; Shao K; Bailer AJ. Quantile Benchmark Dose Estimation for Continuous Endpoints. Environmetrics. 2015, 26: 363-372. Nominated for 2016 Centers for Disease Control and Prevention (CDC) Charles C. Shepard Science Award
Research Interests

Dr. Shao's recent research has been focused on advancing human health risk assessment to support environmental policy making, with a special interest in improving dose-response assessment through innovative statistical and analytical methodologies. Additionally, he is working on a project to quantitatively estimate health risk imposed by rice consumption in support of food safety regulation making. For more information please visit my webpage (http://pages.iu.edu/~kshao/).

Selected Publications

Yang G; J Li; Y Wang; C Chen; H Zhao; K Shao*. 2018. Quantitative Ecotoxicity Analysis for Pesticide Mixtures
using Benchmark Dose Methodology. Ecotoxicology and Environmental Safety. 159: 94-101.

Chiu WA; DA Axelrad; C Dalaijamts; C Dockins; K Shao; AJ Shapiro; G Paoli. 2018. Beyond the RfD: Broad application of a probabilistic approach to improve chemical dose-response assessments for noncancer effects. Environ Health Perspect 126(6): 067009

Shao, K*; A Shapiro. 2018. A Web Based System for Bayesian Benchmark Dose Estimation. Environmental Health Perspectives. 126 (1): 017002. https://doi.org/10.1289/EHP1289

Shao, K*; BC Allen; MW Wheeler. (2017). Bayesian Hierarchical Structure for Quantifying Population Variability to Inform Probabilistic Health Risk Assessments. Risk Analysis. 37(10): 1865-1878.

Wheeler MW*, AJ Bailer, T Cole, B Park, and K Shao. (2017). Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints. Risk Analysis. 37(11): 2107-2118.

Shao, K*; JS Gift. (2014). Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data. Risk Analysis . 34(1): 101-120.

Antonelli, R#, Shao, K#, DJ Thomas, R Sams II, J Cowden*. (2014). AS3MT, GSTO, and PNP polymorphisms: Impact on arsenic methylation and implications for disease susceptibility. Environmental Research. 132: 156-167.(# indicates equal contribution)

Shao, K*; JS Gift; RW Setzer. (2013). Is the Assumption of Normality or Lognormality for Continuous Response Data Critical for BMD Estimation? Toxicology & Applied Pharmacology. 272(3): 767-779.

Shao, K*. (2012). A Comparison of Three Methods for Integrating Historical Information for Bayesian Model Averaged Benchmark Dose Estimation. Environmental Toxicology & Pharmacology, 34(2): 288-96.