PHONE (918) 812-5562 • EMAIL arlo.lyle [at] gmail.com
To use artificial intelligence techniques in the implementation of real world consumer applications.
M.S. in Artificial Intelligence, University of Georgia, 2007
B.S. in Computer Science (Cum Laude), University of Tulsa, 2005
Languages: Java, C/C++, Perl, Visual Basic, Lisp, Clips, SQL, Prolog
Operating Systems: Windows, Linux, Mac OSX
Software: Microsoft Visio, Microsoft Visual Studio 6, Weka
USDA Forest Service - NED-2
NED-2 is a robust, intelligent, goal-driven decision support system that integrates vegetation growth models, wildlife models, silvicultural models, GIS, and visualization tools for forest ecosystem management. NED-2 uses a blackborad architecture and a set of semi-autonomous agents to manage these tools for the user.
Baseball Prediction Using Ensemble Learning (masters thesis)
My thesis involved implementing several machine learning and ensemble learning techniques in order to predict season statistics for batters playing in the major leagues.
LPA-Speech is an interface between LPA-Prolog and the Microsoft Speech API. LPA-Speech provides functionality for speech generation and recognition.
conferences and publications
Baseball Prediction Using Ensemble Learning - Arlo Lyle - Georgia Graduate
LPA-Speech: An Interface Between LPA-Prolog and Microsoft SAPI - Arlo Lyle -
Managing for Visual Goals in the NED-2 Decision-Support System for Forest Ecosystems -
PollenPro: A General Pollen Forecasting System - K. Johnson, A. Lyle, B. Waters, S. East,
The University of Georgia - Artificial Intelligence Center (8/2005 - current)
The University of Tulsa - McFarlin Library (08/2001 - 08/2005)
The University of Tulsa - Dept. of Math and Computer Science (08/2004 - 05/2005)