Contrastive, multimodal, and interpretable machine learning for photonics and beyond

I will present the trajectory of our work on the application of machine learning techniques to problems in photonic crystals and materials analysis. I will highlight our work on contrastive pre-training approaches for photonic crystal analysis, opportunities and techniques in multimodal pre-training for settings with multiple sources of complementary data, and, finally, interpretable machine learning systems with applications to topological materials analysis.