Skills: Statistical consulting; time series, spatial and spatio-temporal modeling; regression analysis; classification methods

Proficient in R, JMP and SPSS. Familiar with Python, MATLAB and SAS.

Statistics in the Community at UIUC. Champaign-Urbana, Illinois. President/Founder (January 2013-present)

A Pro-bono statistical consulting organization,  reaching out to educational institutions, non-profits, and local small businesses in order to encourage collaboration between the University and the community.

  • Connect with local non-profits and academic departments.
  • Clear communication of technical material to non-technical audiences
  • Provide statistical analaysis for local organization including the Girl Scouts of Central Illinois, YWCA, Walking with Angels, Rape, Advocacy, Counseling and Educational Services (RACES), Urbana Police Department. and UIC Specialized Care.
  • Provide statistical analysis for colleagues in UIUC graduate departments of Food and Nutritional Sciences, Psychology, and more.

Walking with Angels. Champaign, IL (June 2016-present)
Statistical Consultant

  • Ongoing statistical consultant for a team of collaborators working in HIV Drug Resistance research in Kenya.

Dow Agrosciences. Research Park Champaign, IL (January 2015-August 2015)
Statistical Intern

  • Performed high dimensional genetic data analysis in Python using Linear mixed modeling, and parametric regression, i.e. Ridge, Lasso and Elastic-Net regression
  • Built yield prediction models from data with over 1000,000 observations and more than 10,000 covariates.

The Dow Chemical Company. Research Park Champaign, IL (May 2012-August 2014)
Statistical Intern

  • Designed and analyzed experiments for High Throughput research using JMP and Excel
  • Explored causal inferences and confounding factors from experimental output
  • Developed JMP tool for Dunnett-Tukey-Kramer multiple comparison test now used throughout Dow.
  • Developed an image classification tool still used at Dow incorporating logistic regression and unsupervised learning such as PCA and cluster analysis.