The photon is the fundamental quantum of optical energy and a natural carrier of information in light–matter interactions. Optical spectroscopy and microscopy resolved at the level of individual photons, and extended to high-throughput measurements, can therefore access exceptionally rich information about a system and its underlying dynamics [1]. Machine-learning methods further enable the discovery of hidden correlations in high-dimensional photon-counting data, improving classification accuracy and interpretability in molecular analytics. 

Through combined theoretical, numerical, and experimental efforts, we develop new optical methods based on multidimensional photon correlations in polarization, frequency, space, and time. Rigorous statistical analysis and uncertainty-aware model selection allow the identification of rare events, distinct reaction pathways, and signatures of non-Markovian dynamics. Recent advances include the introduction of two-dimensional, frequency-resolved second- and third-order correlation measurements, which can robustly identify excitonic cascade emission [2], as well as frequency-fluctuation–based super-resolution microscopy [3].

[1] Tsao, C., Ling, H., Hinkle, A., Chen, Y., Jha, K. K., Yan, Z. L., & Utzat, H. (2025). Enhancing spectroscopy and microscopy with emerging methods in photon correlation and quantum illumination. Nature Nanotechnology, 1-16.

[2] Hinkle, A., Tsao, C., Duell, A., & Utzat, H. (2025). Multi-Dimensional Photon-Correlations Reveal Triexciton Features in Single Perovskite Quantum Dots. arXiv preprint arXiv:2509.12461.

[3] Chen, Y., Tsao, C., Cobb-Bruno, C., & Utzat, H. (2025). Stochastic frequency fluctuation super-resolution imaging. Optics Express, 33(3), 6514-6525.