Jiakai Wang is a Ph.D. student in Physics at the University of Wisconsin–Madison. His research focuses on superconducting quantum circuits, with particular interest in fluxonium qubits, Hamiltonian modeling, and quantum error correction.

He develops numerical simulation tools using Python and C++ to study driven multilevel systems, dressed states, and circuit quantization. He is especially interested in data-driven approaches to circuit design, including machine learning methods for analyzing qubit parameterization and performance trade-offs.

His broader interests include tensor network methods, optimal pulse control for superconducting qubits, and building computational infrastructure for large-scale quantum simulations.