Ryan Rybarczyk Ph.D.
Visiting Assistant Professor, Computer Science
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-Lead 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.
Previous research involved 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. Think GPS, but for indoor environments.
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