STTG Member Spotlight: Prof. Tony McDonald
Prof. Tony McDonald
What was the path to your current role?
I started as a Research assistant in the Cognitive Systems Lab at UW-Madison. Upon graduation I worked as a Senior Software Engineer and as a Manager of Software Development at Oracle. I then worked as an Assistant Professor at Texas A&M University. Currently I am an Assistant Professor in the Industrial & Systems Engineering department at UW-Madison.
What are the responsibilities associated with your role?
Research in Transportation Safety, Machine learning, and intersections between Transportation and Healthcare; Teaching courses on Design, Machine Learning, and Decision Making; and Service to the University, HFES, and TRB.
What made you decide to pursue an academic role?
I chose an academic role because I enjoy working with students and developing their talents and also because I wanted the freedom to explore my own research ideas in machine learning and human factors.
What do you enjoy the most in your current role?
What do you enjoy the most in your current role?
I enjoy all aspects of my role but working with students is the part I enjoy most. My favorite aspect of working with students is when a student is able to create something that they are proud of or complete a milestone that they did not initially believe they could.
Could you describe a favorite or recent project?
I am very excited about our ongoing work with active inference models of human behavior - particularly driving behavior (e.g., https://ieeexplore.ieee.org/abstract/document/9733256). In that work, we are using active inference theory - an emerging new framework for cognition - to develop quantitative models that can predict driver behavior and decision-making. The most interesting part of the work for me is that we are able to model not just the actions taken by the driver but also their beliefs about the environment and their perceptions. This helps us understand why drivers make decisions and may help us design better interfaces to inform driver beliefs or make automated vehicles more adaptable to human drivers.
I am very excited about our ongoing work with active inference models of human behavior - particularly driving behavior (e.g., https://ieeexplore.ieee.org/abstract/document/9733256). In that work, we are using active inference theory - an emerging new framework for cognition - to develop quantitative models that can predict driver behavior and decision-making. The most interesting part of the work for me is that we are able to model not just the actions taken by the driver but also their beliefs about the environment and their perceptions. This helps us understand why drivers make decisions and may help us design better interfaces to inform driver beliefs or make automated vehicles more adaptable to human drivers.