Kean University Senior Daniel Pareja was recently awarded the John Dobosiewicz Research Day Interdisciplinary Award for his work entitled “Algorithmic Mechanisms for Reliable Internet-based Task Computing under Collusion.” Kean University Professor Miguel A. Mosteiro sparked Pareja’s passion for research and the rapidly evolving field of machine learning. Working closely with his mentor, Pareja looks forward to building upon the foundational research skills he acquired at Kean during his graduate career.
“The practicality of this research is important to me, as I like to start with a theoretical problem, and carry it through to implementation and evaluation,” said Daniel. “The work challenged me to understand mathematical concepts I had not yet learned in the classroom, and learn new programming tools. My enjoyment and awe for mathematics sustained me through the challenge.”
Pareja and Mosteiro’s work, in collaboration with scholars from other countries, deals with the application of game theory concepts to Internet-based distributed computing. Pareja’s initial role was to modify emBOINC, an emulator software for the BOINC platform that is a voluntary-peer distributed system, to run simulations of the team’s models. Pareja quickly acquired an understanding of the theoretical models behind their research, and the core code of BOINC and emBOINC.
“Daniel is a creative thinker, with a motivation for research that is very unusual in undergraduate students,” Mosteiro observerd.
Daniel successfully completed his challenging research assignment within two months. Familiarizing himself with Visual Studio so to integrate C++ code with MATLAB functionality, Pareja conducted extensive testing. His findings reinforced the thesis that the pure equilibria model performs optimally on average. His work was accepted for publication at the Public Library of Science Journal (PLOS ONE) and will appear in 2015. He also presented at the G- LSAMP 6th Annual Conference in October 2014.
Regarding his undergraduate research experience Pareja said, “I was closely exposed to three university research communities, which greatly accelerated my understanding of research methods and topics. I expect to be able to contribute quickly, at a high level, to a graduate research program, and look forward to new research topics related to machine learning.”