Mark E. Borsuk

Associate Professor of Engineering

I have experience teaching courses in modeling, decision analysis, systems analysis, and statistics at the undergraduate, professional, and graduate levels. 

My teaching style can be characterized as “decision-oriented” because of its emphasis on the role of students in formulating problems, selecting methods of analysis, and critically evaluating the results.  This makes the students active participants in the learning experience and helps them to transfer the knowledge and skills they gain to other situations.  This approach has been popular with students, who have commented that my classes are “very challenging and very interesting.  [They require] a lot of thinking in ways not commonly done in other courses.”

By using real-world problems and data, I provide students with a compelling context for their learning.  The problems I choose are often pressing, involve some amount of controversy, and have a high degree of scientific uncertainty.  When students have the opportunity to touch on these aspects, it stimulates their interest in the implementation of an appropriate method of analysis.  Their active role in structuring problems also allows them to apply their conceptual abilities.  This type of thinking is not always encouraged in conventional quantitative courses.

ENGS 93: Statistical Methods in Engineering

Statistics involves the collection, analysis, interpretation, and presentation of data.  These tasks are fundamental elements of the engineering profession and, in an increasingly information-driven society, also play an important role in our everyday lives.  This course will provide students with tools for structuring data-driven problems, identifying and describing sources of uncertainty, performing inference and hypothesis tests, designing effective experiments, and graphically communicating results.  The Bayesian perspective will be introduced, emphasizing the natural connection between probability, inference, and decision-making.  Numerical analysis will be performed using Microsoft Excel and R, a popular open-source statistical programming language.

ENGG 177: Decision-Making under Risk and Uncertainty

Making decisions under conditions of risk and uncertainty is a fundamental part of every engineer and manager’s job, whether the situation involves product design, investment choice, regulatory compliance, or human health and safety.  This course will provide students with both qualitative and quantitative tools for structuring problems, describing uncertainty, assessing risks, and reaching decisions, using a variety of case studies that are not always amenable to standard statistical analysis.  Bayesian methods will be introduced, emphasizing the natural connections between probability, utility, and decision-making.

ENGG 107: Bayesian Statistical Modeling and Computation

This course introduces the Bayesian approach to statistical modeling as well as the computational methods necessary to implement models for research and application. Methods of statistical learning and inference will be covered for a variety of settings. Students will have the opportunity to apply these methods in the context of their own research or area of application in the form of a term project.