67 citations · 67 across the 2 of their papers we have counts for
8 papers
Understanding the development of interest and self-efficacy in active-learning undergraduate physics courses
Remy Dou, Eric Brewe, Geoff Potvin +2
Modeling Instruction (MI), an active-learning introductory physics curriculum, has been shown to improve student academic success. Peer-to-peer interactions play a salient role in…
Restoring the structure: A modular analysis of ego-driven organizational networks
Robert P. Dalka, Justyna P. Zwolak
Organizational network analysis (ONA) is a method for studying interactions within formal organizations. The utility of ONA has grown substantially over the years as means to analy…
Ray-based framework for state identification in quantum dot devices
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre +4
Quantum dots (QDs) defined with electrostatic gates are a leading platform for a scalable quantum computing implementation. However, with increasing numbers of qubits, the complexi…
Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
Shangjie Guo, Amilson R. Fritsch, Craig Greenberg +2
Most data in cold-atom experiments comes from images, the analysis of which is limited by our preconceptions of the patterns that could be present in the data. We focus on the well…
Auto-tuning of double dot devices in situ with machine learning
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre +7
The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applicatio…
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
Justyna P. Zwolak, Sandesh S. Kalantre, Xingyao Wu +2
Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery…