Teaching at CUNY

Transcriptomics and AI for Genome Analysis (BIO/CS 37105-77105) introduces students to the intersection of RNA biology and artificial intelligence. It covers the fundamentals of gene expression and single-cell RNA sequencing, progressing through computational analysis techniques and into machine learning and transformer-based models applied to biological data. The capstone focus is on virtual cell modeling — using AI to predict how cells respond to genetic perturbations without wet-lab experiments.

Transformer Architecture LLMs and Mechanistic Interpretability (BIO/CS 47102-79006) takes a deeper dive into the inner workings of the AI models introduced in the first course. Students build transformers from scratch, study the mathematics of self-attention, and then pivot to mechanistic interpretability — a research discipline focused on reverse-engineering what neural networks actually compute. Topics progress from toy models and superposition through circuit analysis, sparse autoencoders, and attribution graphs, culminating in real-world applications like model steering and AI safety.