The photon is the fundamental unit of energy—and information—carried by light. Optical spectroscopy and microscopy resolved at the level of individual photons, and at scale, can therefore unlock high information content about the system and its dynamics [1]. Machine learning can further uncover hidden correlations that improve classification accuracy 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. Statistical analysis and uncertainty-aware model selection enable the identification of rare events, distinct reaction pathways, and non-Markovian dynamics. Recent advances include the introduction of two-dimensional, frequency-resolved second- and third-order measurements, which can unambiguously identify excitonic cascade emission [2], and frequency-fluctuation super-resolution microscopy [3].

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[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.