CARLOS SCHEIDEGGER BLOG COURSES CODE VIS WRITING PAPERS ABOUT CV

I'm an associate professor in the Department of Computer Science at the University of Arizona. I work in large-scale data visualization and 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 for 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.)

FUNDED PROJECTS

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

STUDENTS

  • MSc student: Katherine Best
  • PhD student: Rebecca Faust (expected 2021)
  • PhD student: Mingwei Li (expected 2021)
  • PhD student: Zhenge Zhao (expected 2021)
  • Zhe Wang, PhD, July 2019, now research scientist at ASML

TRAVEL

2019-11: Germany: ML + Vis Dagstuhl
2019-10: Vancouver: IEEE VIS
2019-07: Redmond: MSR Faculty Summit
2019-06: Nashville: Vis EC Summer Camp
2019-05: Calgary: SDM 2019
2019-04: Yale: Biostats seminar
2019-03: NYC: NYU Tandon
2019-01: Atlanta: ACM FAT* 2019
2019-01: Houston: rstudio::conf

CODE

  • 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.

SOME STUFF I MADE

PAPERS