I'm an assistant professor in the Department of Computer Science at the University of Arizona. I work in large-scale data visualization and exploratory data analysis. Before coming to sunny, beautiful Tucson, I worked at AT&T Research in New York.

Interactive visualization is crucial in understanding, exploring, and presenting data, but data scale can present serious barriers for effectiveness and adoption. I study these barriers and design solutions to remove them. My work ranges from theoretical to practical; from designing solutions specifically for particular domains to designing general infrastructure for large-scale visualization.

Since joining UA, I have worked in a number of projects here, including support for interactive browsing of synteny data at CoGe, and building up interactive visualization infrastructure at NOAO and the ANTARES project. If you're interested in our work, you should also take a look at the webpage of the HDC Lab which I cofounded with my colleagues, Josh Levine and Kate Isaacs.

(I'm trying something different for writing, instead of a blog.)

My work is funded by a number of institutions, including the NSF, AT&T, and the Arizona Board of Regents.

- NSF III-1513651: Topological Data Analysis for Large Network Visualization
- NSF III-1815238: An End-to-End Pipeline for Interactive Visual Analysis of Big Data

- Nanocubes: blazing fast large data visualization.
- Lux: write WebGL shaders in Javascript, composably.
- RCloud: collaborative data analysis, on the web.
- VisTrails: a provenance-aware scientific workflow system.
- Vector-field k-means: scalable trajectory clustering.
- Afront: high-quality triangle meshing of surfaces.

- Slice and dice 200 million data points. In your browser.
- Plotting ten years of NFL plays.
- Visualizing the MLB Hall of Fame induction voting dynamics.
- 5h of footage from a Window Seat into one image.
- The Beauty of Roots.
- An interactive nomograph for Bayes' rule (explanation).
- Some optical illusions, animated with JS and d3.
- An illustration of the duality principle in convex optimization.
- Making sense of symmetric 2x2 matrices through their eigendecomposition.
- An illustration of the Bunimovich stadium.
- The full list, not necessarily in any order.