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Atom Interferometry with Floquet Atom Optics

https://doi.org/10.1103/PhysRevLett.129.183202

Wilkason, Thomas; Nantel, Megan; Rudolph, Jan; Jiang, Yijun; Garber, Benjamin E.; Swan, Hunter; Carman, Samuel P.; Abe, Mahiro; Hogan, Jason M. (October 2022, Physical Review Letters)
Terrestrial Very-Long-Baseline Atom Interferometry: summary of the second workshop

https://doi.org/10.1140/epjqt/s40507-025-00344-3

Abdalla, Adam; Abe, Mahiro; Abend, Sven; Abidi, Mouine; Aidelsburger, Monika; Alibabaei, Ashkan; Allard, Baptiste; Antoniadis, John; Arduini, Gianluigi; Augst, Nadja; et al (December 2025, EPJ Quantum Technology)

Free, publicly-accessible full text available December 1, 2026
Predicting Human Grasp Locations on Cup Handles by Using Deep Neural Networks to Infer Heat Signatures from Depth Data

https://doi.org/10.1109/ICMEW.2019.00012

Jiang, Yijun; Banerjee, Sean; Banerjee, Natasha Kholgade (July 2019, IEEE International Conference on Multimedia & Expo Workshops (ICMEW))

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Large Momentum Transfer Clock Atom Interferometry on the 689 nm Intercombination Line of Strontium

https://doi.org/10.1103/PhysRevLett.124.083604

Rudolph, Jan; Wilkason, Thomas; Nantel, Megan; Swan, Hunter; Holland, Connor M.; Jiang, Yijun; Garber, Benjamin E.; Carman, Samuel P.; Hogan, Jason M. (February 2020, Physical Review Letters)

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When Will Breakfast be Ready: Temporal Prediction of Food Readiness Using Deep Convolutional Neural Networks on Thermal Videos

https://doi.org/10.1109/ICMEW.2018.8551500

Jiang, Yijun; Luo, Miao; Banerjee, Sean; Banerjee, Natasha Kholgade (July 2018, 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW))

In this paper, we perform prediction of food readiness during cooking by using deep convolutional neural networks on thermal video data. Our work treats readiness prediction as ultra-fine recognition of progression in cooking at a per-frame level. We analyze the performance of readiness prediction for eggs, pancakes, and bacon strips using two types of neural networks: a classifier network that bins a frame into one of five classes depending on how far cooking has progressed at that frame, and a regressor network that predicts percentage of cooking time spent at each frame. Our work provides classification accuracies of 98% and higher within one step of the ground truth class using the classifier, and provides an average error of within 20 seconds for the elapsed time predicted using the regressor when compared to ground truth.
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