Pilsung Kang
Associate Professor
Department of Software Science
Dankook University, South Korea
Research Areas
Quantum Computing, Parallel Systems, and High-Performance Computing on Edge Systems
Pilsung Kang
Associate Professor
Department of Software Science
Dankook University, South Korea
Research Areas
Quantum Computing, Parallel Systems, and High-Performance Computing on Edge Systems
Bio
Pilsung Kang received his PhD in computer science from Virginia Tech in 2010 under the supervision of professors Srinidhi Varadarajan and Naren Ramakrishnan. He worked at Samsung Electronics on software for flash-based storage systems from 2010 to 2016. He switched to academia in 2016, and is now a member at Dankook University, South Korea.
Recent Publications
Preprints
"Barren Plateaus as Destructive Interference: A Diagnostic Framework and Implications for Structured Ansatz," arXiv:2605.01319 (submitted to Quantum)
"Interaction as Interference: A Quantum-Inspired Aggregation Approach," arXiv:2511.10018
"Quantum Causality: Resolving Simpson's Paradox with DO-Calculus," arXiv:2509.00744 (submitted to Quantum)
"Quantum Entanglement as Super-Confounding: From Bell's Theorem to Robust Machine Learning," arXiv:2508.19327 (submitted to ACM TQC)
"Emergent Bifurcations in Quantum Circuit Stability from Hidden Parameter Statistics," arXiv:2508.00484 (submitted to IEEE TQE)
"QFFN-BERT: An Empirical Study of Depth, Performance, and Data Efficiency in Hybrid Quantum-Classical Transformers," arXiv:2507.02364 (submitted to IEEE Access)
Published Papers
"Performance Evaluation of Modern GPU Accelerator-Based Edge Systems: A Holistic Approach", H. Lee, P. Kang, IEEE Internet of Things Journal. 12(23), pp. 51716-51729 (2025)
"Evaluating Coding Proficiency of Large Language Models: An Investigation Through Machine Learning Problems", E. Ko, P. Kang, IEEE Access. 13, pp. 52925-52938 (2025)
"Evolving Scientific Code Adaptations with Modularization Frameworks", P. Kang, Automated Software Engineering. 30, article no. 24 (2023)
"Programming for High-Performance Computing on Edge Accelerators", P. Kang, Mathematics. 11(4):1055 (2023)
Projects at High-Performance Computing Lab (HPCL)
Quantum Causal Inference for Trustworthy AI
Develop a unified framework that integrates quantum principles with causal inference to ensure fairness, explainability, and robustness in AI systems
Implementing High-Performance Computing (HPC) on Edge Accelerators
Leverage GPU-based edge systems for HPC applications