Polychromatic flow cytometry is a widely used technique for gathering and analyzing cellular data. The data generated is high-dimensional, and therefore notoriously difficult to visualize by a human expert. The traditional method of plotting every pair of observables of the original high-dimensional data leads to a combinatorial explosion in the number of visualizations. The usual solution is to project the data into a lower-dimensional space while approximately preserving key properties and relationships among data points. The lower dimensional data can then be easily analyzed with the help of specialized data visualization software. We introduce SANJAY, a new method for automatically generating visualizations of high-dimensional flow cytometry datasets. Our technique uses symbolic decision procedures to algorithmically synthesize 2D and 3D projections of the (original) high-dimensional data, with minimal distortion. We compare our approach to the popular MDS algorithm on a representative set of flow cytometry benchmarks, and show that the projections synthesized by SANJAY have distortions that are, on average, about two times smaller than those produced by MDS.