Scaling Up Computing Education in a Time of AI
Academics - Jan 23, 2026
Artificial intelligence is accelerating discoveries across medicine, protein design, materials science, hurricane modeling, and more. At The University of Texas at Austin, the Texas Advanced Computing Center (TACC) is building the talent pipeline with academic courses that teach students how to work with AI and high-performance computing at real scale.
TACC's Joe Stubbs lectured on intelligent systems in Fall 2025. Photo credit: Jorge Salazar, TACC.
Why this matters for research teams
Access to top-tier systems is valuable. Turning that access into reproducible results and faster workflows is what changes outcomes in the lab and in the field.
"We're not only here to build big computers, but to make sure people use them well," said Dan Stanzione, associate vice president for research at UT Austin and executive director of TACC. "One great way to do this is by teaching students on our campus, as part of their formal education, the methods of large-scale computational science."
Training grounded in real infrastructure
For more than two decades, TACC has hosted National Science Foundation-funded systems that support academic research at scale, including:
- Horizon: The NSF Leadership-Class Facility system launching in 2026
- Ranch: Part of the NSF LCCF; the largest academic data storage system in the nation
- Frontera: The fastest academic supercomputer in the U.S.
- Vista: Optimized for AI workloads
- Lonestar6: Serving UT System via the University of Texas Research Cyberinfrastructure Portal (UTRC)
- Stampede3: A scientific workhorse supporting thousands of U.S. researchers
- Jetstream2: A flexible cloud-based computing resource
Courses that meet current needs
TACC contributes to graduate education through the Oden Institute for Computational Engineering and Science, and to undergraduate education through the Cockrell School of Engineering's Computational Engineering program.
Tools and Techniques of Computational Science sets a practical foundation: hardware principles, key programming languages, and the operating system environment required to get real performance from supercomputers.
Software Design for Responsible Intelligent Systems takes students from concept to operation: designing, implementing, validating, and running real-world intelligent systems using scalable data analysis and modern machine-learning techniques-with a focus on responsible use.
What researchers can expect from graduates
- Better use of advanced systems through informed choices on hardware, software stacks, and optimization
- Workflows that are reproducible, testable, and prepared for scale
- AI systems built with data rigor, monitoring, and validation from day one
Learn more
Explore TACC's work and education initiatives on the TACC website.
If you're planning a skills roadmap for your team, you can also browse curated AI courses by job role here: Complete AI Training.
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