Department of Computer Science
Associate Professor and Fellow,
I research image analysis and data visualization to improve the computation of imaging-based science.
Science pairs measurement tools that produce experimental data with computational tools to process the data. Advances in scanned imaging modalities (like MRI and confocal microscopy) are constantly increasing the speed, resolution, and sophistication of image measurements. Scientists can now form hypotheses and conduct experiments faster than they can find or create the computational analysis best matched to their new image data. Unfortunately, the process of creating new software remains slow or opaque for many people, and advances in parallel computing (required for large images) complicate the process even for experts.
I collaborate with physical and biomedical researchers who acquire image data to answer scientific questions. My research simplifies how informative visualizations are created, and improves how relevant image features are detected, sampled, and quantified. I am also interested in the theoretical and perceptual bases of effective data visualization. I foster re-usable and reproducible computational science by making all my research software open-source.