My applications have focused on noisy, high dimensional small datasets in which it is either very expensive or impossible to get more data. One example is rare

My applications have focused on noisy, high dimensional small datasets in which it is either very expensive or impossible to get more data. One example is rare class prediction on long form text data eg phone calls, podcasts, transcripts. Other networks including neural networks and LLMs are either not flexible enough or require far too much data to achieve the necessary performance. Structured hierarchical modeling is the balance between those two extremes. Another example is in genomic analysis. Similarly high dimensional, noisy, low data. Additionally, you don’t actually care about the predictions, you want to understand what genes or sets of genes are driving phenotypic behaviors. I’d be happy to go into more depth via email or chat if this is something you are interested in (on my profile). Some useful reads [1] https://sturdystatistics.com/articles/text-classification https://sturdystatistics.com/articles/text-classification [2] https://pmc.ncbi.nlm.nih.gov/articles/PMC5028368/ https://pmc.ncbi.nlm.nih.gov/articles/PMC5028368/