Computing, AI, and Systems
From AI-powered flood forecasting to smart infrastructure systems, CAS research turns data into decisions that matter.
Engineering Intelligence for Complex Systems
Computing, AI, and Systems (CAS) brings advanced computation, data-driven intelligence and systems thinking into civil, construction and environmental engineering.
At NC State, CAS research helps engineers better understand complex systems, anticipate risk and make smarter decisions — from infrastructure and transportation to water systems and energy networks.
By combining computing, optimization and artificial intelligence, this work is transforming how engineering problems are modeled, analyzed and solved.
Our graduates pursue careers at traditional firms, government agencies, national laboratories, and universities, as well as at companies such as SAS, Cisco, IBM, Microsoft, GE, and Intel.
What CAS Enables
CAS research focuses on four interconnected areas:
- Scientific Computing
Advancing the computational tools that power large-scale modeling and simulation - Systems Modeling and Optimization
Guiding complex decisions through predictive modeling and data-driven analysis - Cyber-Physical Systems
Connecting digital intelligence with physical infrastructure for real-time monitoring and control - Data Science, AI, and Analytics
Using machine learning and advanced analytics to uncover patterns, improve predictions and enhance performance
Why It Matters
As infrastructure systems become more connected and data-driven, CAS plays a critical role in helping communities:
- Respond to changing conditions in real time
- Improve the safety and reliability of infrastructure
- Make better decisions under uncertainty
- Build smarter, more resilient systems
This work supports safer communities, more efficient systems and more sustainable solutions at scale.
Key Resources
Faculty and Contacts
| Core Faculty | |
| Sankar Arumugam | Stochastic methods, Hydroclimatology, Water Resources |
| John Baugh | Software Design, Formal Methods, Scientific Computing, Cyber-Physical Systems |
| Emily Berglund | Complex Systems Analysis, Systems and Optimization, Water Systems Analysis |
| Murthy Guddati | Computational Mechanics, Inverse Modeling and Optimization, Wave Propagation |
| Ali Hajbabaie | Large-scale System Modeling and Optimization, Distributed Collaboration, Distributed Coordination |
| Kumar Mahinthakumar | High-Performance Computing, Numerical methods, Water Systems Analysis, Groundwater modeling |
| Ranji Ranjithan | System modeling, optimization, and analysis, Meta-heuristic algorithms, Applied Machine Learning |
| Affiliated Faculty | |
| Casey Dietrich | Coastal Hydrodynamics, Large-Scale Modeling of Coastal Hazards, Finite elements, High-Performance Computing |
| Marc Hoit | Complex Systems Analysis, Systems, and Optimization, Water Systems Analysis |
| Jeremiah Johnson | |
| Dan Obenour | Stochastic methods, Water quality modeling, Geospatial modeling |
| Fernando Garcia | Complex Systems Analysis, Systems, and Optimization, Water Systems Analysis |
| Kevin Han | Construction automation and robotics |
Courses, Academic Pathways, and Facilities
Students engage with CAS through undergraduate and graduate coursework in computing, modeling and data-driven engineering, as well as research opportunities across disciplines.