Generating, Analyzing, and Understanding Sensory and Sequencing Information

NEWS
  • 2018-2019 GAUSSI fellowships awarded! Aeriel Belk, Alyssa Melvin, Dayton Pierce, Julius Stuart, Luke Schwerdtfeger, Lyndsey Gray, Michael May, Sere Williams, & Steven Lakin.
  • Reyes Murrieta awarded NIH Ruth L. Kirschstein National Research Service Fellowship 
  • Bridget Eklund & Adam Heck awarded NSF Graduate Research Fellowship.
  • Aeriel Belk & Heather Deel awarded NIJ Graduate Research Fellowships in Science, Technology, Engineering, and Mathematics

Apply to the GAUSSI Program

The GAUSSI program provides a flexible curriculum for graduate students whose research involves the analysis of large biological datasets. We offer a limited number of NSF-funded one-year fellowships. More information is available on the GAUSSI Fellowship application page. We will be hosting GAUSSI Information Meetings for any CSU graduate student interested in learning how to generate and analyze large biological datasets, especially those from sensing and sequencing experiments.

January 30 @ 10am in Pathology Bldg Rm 107
February 7 @ 1pm in Computer Science Bldg Rm 305

The application deadline for the 2019-2020 fellowship awards is March 10, 2019 at midnight (mst). All materials must be submitted prior to the deadline for full consideration. Required materials, in addition to the online application, include: your personal statement; a letter of support from your faculty advisor(s); 2 letters of recommendations from individuals who do not serve as your adviser(s); unofficial transcripts of your undergraduate degree(s) and graduate studies; and unofficial GRE score results.

If you do not qualify for the fellowship, which is geared mostly to students in their first two years of graduate work and requires a green card or American citizenship; you can still participate in the GAUSSI program. Benefits of the GAUSSI program include preferred access to GAUSSI courses, career planning, professional development opportunities, and mentoring and seminars in the area of large scale biological data analysis.

To become involved, please contact Kate Sherrill.