Nick Stapleton
Nick Stapleton
Data Science and Engineering (DSE)
Stapleton is a PhD student in Data Science & Engineering at the University of Tennessee, Knoxville, affiliated with the Bredesen Center and ORNL. His background emphasizes applied and interdisciplinary computing, beginning with career and technical education through Skills USA Chapter 432, where he competed in Information Technology Services and earned a bronze medal at the state level. He began his academic training at Sacramento City College, where he pursued computer science for transfer and advanced from peer tutor to Instructional Assistant in Mathematics, helping manage STEM tutoring services. He earned a B.S. in Computer Science from the University of California, Davis, with minors in Mathematics and Music, and served as an Applications Programmer (full-stack) contributing to the EyeVocab project. Stapleton has held national laboratory and teaching appointments, including a SULI internship at Brookhaven National Laboratory, instructional roles at Cal Poly San Luis Obispo (data structures and object-oriented programming), and graduate research at ORNL in quantum machine learning.
Research
Stapleton’s research lies at the intersection of statistical learning theory, applied machine learning, and emerging computational paradigms. His work examines the limits, benefits, and trade-offs between classical computing and non-classical approaches—such as quantum and hybrid systems—particularly for learning and AI workloads. A central goal of this research is to determine when emerging hardware meaningfully outperforms classical architectures, how such advantages can be quantified, and whether qualitative transitions in computational behavior can be identified across paradigms. In collaboration with ORNL’s Quantum Science Center, he develops user-facing, agentic software to support quantum–classical workflows, with an emphasis on usability, transparency, and experimental evaluation. His broader research integrates reinforcement learning, high-performance computing, and systems design, focusing on intelligent coordination of heterogeneous resources within the HPC middleware stack to enable adaptive, efficient execution of large-scale scientific workflows.