minus plus magnify speech newspaper atomic biology chemistry computer-science earth-science forensic-services globe info math matrix molecule neuroscience pencil physics pin psychology email share atsign clock double-left-chevron double-right-chevron envelope fax phone tumblr googleplus pinterest twitter facebook feed linkedin youtube flickr instagram
Ryan Rybarczyk, Visiting Assistant Professor, Computer Science


  • Ph.D. Computer Science, Indiana University Purdue University Indianapolis, 2016
  • M.S. Computer Science, Indiana University Purdue University Indianapolis, 2010
  • B.S. Computer Science, Butler University, 2007

Courses Taught / Teaching

  • CSCI 12000 Windows on Computer Science (Fall 2017)
  • CSCI 24000 Computing II (Fall 2017)
  • CSCI 36300 Principles of Software Design (Fall 2017)
  • CSCI 24000 Computing II (Spring 2018)
  • CSCI 50700 Object-Oriented Design and Programming (Spring 2018)


Current research involves the study and impact of the introduction of career guidance into each year of a student's undergraduate study. By integrating such career prep into the curriculum students can gain a better understanding of the types and expectations of the careers they will face upon graduating. Exploration on the Peer-Led Team Learning model being applied at the various levels of study (e.g., 200-level, 300-level, 400-level, and graduate level) and how first year computing can better be taught to students to aid in the transition into upper level course curriculum. Additional research involves the impact of socio-economic influences as well as gender studies with respect to Computer Science education at both the undergraduate and graduate levels in domestic higher learning institutions.

Other existing research includes indoor tracking solutions and location-based systems for emergency responders. This work attempts to capitalize on the pervasiveness of sensors, specifically mobile devices, in order to construct ad-hoc sensor networks for the purpose of tracking indoors. Specific areas of research focus involve software engineering and distributed system aspects (encapsulation of sensors as software services, sensor discovery and communication, and multi-sensor data fusion) as well as exploration into various machine learning techniques (sensor subset selection & optimization) that can aid in improving the overall accuracy of the positional estimate; Similar to GPS, but for indoor environments.