Dr. Akkarit Sangpetch is Director of the AI Engineering Institute (AIEI) at CMKL University, a Carnegie Mellon–KMITL collaboration in Bangkok, Thailand, which he co-founded in 2017. His work centers on building sovereign AI infrastructure for Thailand, including open framework spanning compute, workforce, and applied AI. He leads curriculum and program design for CMKL's AiCE undergraduate programs, with particular focus on systems programming, open network architecture, learning engineering and accelerated computing.

Beyond AIEI, Dr. Sangpetch collaborates with Thai corporations for enterprise AI transformation research & development including large-scale deployments spanning logistics, smart cities, and digital infrastructure. He holds a background in computer systems and cloud infrastructure from Carnegie Mellon University.

• Ph.D., Electrical and Computer Engineering, Carnegie Mellon University, USA
• M.S., Electrical and Computer Engineering, Carnegie Mellon University, USA
• B.S. (Honors), Computer Science, Carnegie Mellon University, USA
• B.S. (Honors), Electrical and Computer Engineering, Carnegie Mellon University, USA

Distributed Systems, Cloud Computing, AI & HPC Infrastructure, Machine Learning Application

1. Y. Guo, C. Qian, Y. Mo, and A. Sangpetch, "GaussianSlicer: Efficient surface reconstruction from cross-sectional slices with Gaussian splatting," in Proc. IEEE ICASSP, 2025, pp. 1–5.
2. Y. Dong et al., "PigSense: Structural vibration-based activity and health monitoring system for pigs," ACM Trans. Sensor Netw., vol. 20, no. 1, pp. 1–43, 2024.
3. K. Udomchoksakul, O. Sangpetch, and A. Sangpetch, "GPU performance tuning and power efficiency on the DGX A100 cluster," in Proc. IEEE CloudCom, 2022, pp. 170–177.
4. K. Boonchuay et al., "Software vulnerability assessment: Vendor, scanner, and user analysis," in Proc. IEEE CloudCom, 2022, pp. 214–221.
5. A. Sangpetch and O. Sangpetch, "PEX: Privacy-preserved, multi-tier exchange framework for cross platform virtual assets trading," in Proc. IEEE CCNC, 2020, pp. 1–4.
6. A. Sangpetch et al., "Thoth: Automatic resource management with machine learning for container-based cloud platform," in Proc. CLOSER, 2017, pp. 75–83.
7. O. Sangpetch and A. Sangpetch, "Security context framework for distributed healthcare IoT platform," in Proc. Int. Workshop HealthyIoT, 2016, pp. 71–76.
8. A. Sangpetch and H. S. Kim, "VDEP: VM dependency discovery in multi-tier cloud applications," in Proc. IEEE CLOUD, 2015, pp. 694–701.