Research/Creative Interests

Dr. Waschka’s research interests center on applied statistics, spanning biostatistics, data science, and machine learning. He develops and applies both semi-parametric and parametric models for high-dimensional data, often within a causal inference framework, to estimate targeted parameters. While much of his work is motivated by biomedical questions—such as treatment regime protocols and precision medicine—he is broadly interested in applying statistical methods across diverse fields and enjoys collaborative work wherever thoughtful quantitative approaches are needed.

What types of “data” do you commonly use?

High dimensional data, longitudinal data, time series data, spacial data, electronic medical records, biomedical data.

I work on semi-parametric and parametric statistical models where I use causal framework and machine learning to estimate targeted parameters and obtain causal inference.

What types of collaborations are you open to?

I am open to collaborating on all types of projects

What agencies, organizations, or foundations do you have experience with to apply for grants?

  • NSF

Student Qualifications

  • I am particularly interested in students who want to apply statistics and data analytics to their fields.

Interested? Email me to set up a meeting.