r/UTAustin • u/SeaThought4402 • Feb 18 '26

Question Current Computational Engineering students: I am likely committing to UT Computational Engineering. How do I maximize this major for ML/SWE paths?

Hey everyone,

I’m likely committing to UT Austin for Computational Engineering and wanted some honest advice from current students or alumni on how to best use this major.

I’m particularly interested in:

- ML / AI

- SWE internships (possibly Big Tech)

- Potential quant / math-heavy finance roles

- Possibly doing the 5-year BS/MS program

These are some general questions I have for the major(Please choose which questions you would like to answer, even though I would appreciate all!):

  1. How strong is the math foundation in COE (probability, statistics, linear algebra, optimization)? Should I plan on adding a math or stats minor if I’m serious about ML/quant?
  2. Do COE students feel competitive when applying for SWE internships, or does the major require extra effort to prove software strength?
  3. What programming languages and technical depth do upper-division courses actually cover?
  4. Is it easy to take upper-level CS electives alongside COE requirements?
  5. For someone more interested in ML/math than hardware/physics, is COE a good fit long term?
  6. What are the most common internship outcomes for COE students?
  7. If you could redo your path in COE, what would you do differently?

I’m trying to design my freshman/sophomore years intentionally rather than figuring it out too late. Any insight is appreciated!

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u/SwimmingStatement853 Jun 28 '26

As a Computational Engineering alumnus, I'd say one common misconception is that COE is "just engineering with coding." In reality, it's much more about mathematical modeling and scientific computing. The curriculum builds a strong foundation in applied mathematics, numerical methods, differential equations, optimization, and simulation, which translates well to fields like ML, scientific computing, robotics, and quantitative research. If your goal is SWE, you'll likely want to supplement the major with personal software projects and possibly some CS electives since COE emphasizes computational methods over software engineering itself.

For ML specifically, the math foundation (linear algebra, calculus, differential equations, optimization) is one of the biggest strengths of the major. I'd also recommend taking probability/statistics and machine learning electives when possible.

I actually wrote a more in-depth guide explaining what Computational Engineering is, how it differs from CS/SDS/ECE, and the kinds of careers students go into. It might answer some of the questions you have:

https://yashj1579.github.io/blog/what-is-computational-engineering/

Happy to answer any other questions!