University of Wisconsin Madison College of Engineering

Emphasis in Computational Design

To learn more about the other emphasis areas and the emphasis area concept, go to the undergraduate webpage.

Students can design their emphasis elective curriculum to suit their preferences, with the help of their advisor. Tom's choices included the following:

Engineering foundation:

Comp Sci 302: Introduction to Programming

Engineering and society:

Envir St 367: Renewable Energy Systems

Emphasis electives:

MSE 530: Thermodynamics of Solids

MSE 560: Fundamentals of Atomistic Modeling

Comp Sci 367: Introduction to Data Structures

ECE 354: Machine Organization and Programming

Comp Sci 564: Database Management Systems

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  2. Computational design
photo of Tom at the blackboard
Tom Angsten, a computational materials undergraduate student. 

Why Computational Materials?

As a freshman Tom Angsten was interested in both Computer Science and Materials Science. He decided to go into the MS&E department, and learned about the Computational Materials Group. When the department curriculum changed, allowing students to build a coherent set of electives around an emphasis area, Tom knew exactly what he wanted to do: Computational Materials Science.   


These days Tom’s interests lie in leveraging computers to solve materials problems. He says “I don’t like approximations from the top down approach. I like the idea that you can, in principle, calculate materials properties exactly starting with first principles. Computational Materials really fits my personality.”


Tom enjoyed taking MSE 560, Fundamentals of Atomistic Modeling, with Professor Morgan. He says deformations (MSE 441) with Professor Izabela Szlufarska is nice “because she sometimes incorporates her research into the course to demonstrate where computational approach becomes useful.” He liked taking the graduate level thermodynamics course (MSE 530) because it brought Dr. Alan Luo, a Technical Fellow at General Motors and adjunct professor in the department, who talked about the use of computational tools in designing of new alloys for improving efficiency of automobiles.


Tom took independent study with Professors Morgan and Szlufarska. For senior design Tom is working to generate a dilute binary alloy diffusion database from high throughput ab initio calculations. He gets to work one-on-one with a graduate student, Tam Mayeshiba, in the Computational Materials Group.Together they are developing a code in Python that interfaces with first-principle quantum mechanical codes and automates calculations for diffusion coefficients. He attends group meetings with the Computational Materials Group and is expected to function at a graduate level. He says “I feel very prepared for graduate school. My experience has been invaluable.”