Daniel, an NSF IGERT Graduate Research Fellow, is researching how to
design learning agents that solve sustainable energy problems. Such
agents need to take robust decisions under uncertainty, while
learning, predicting, planning and adapting to changing environments.
Daniel's research included designing a learning agent for controlling
a smart thermostat, and designing the champion power-trading agent
that won the finals of the 2013 Power Trading Agent Competition.
Previously, Daniel was part of the champion RoboCup 3D simulation
team, UT Austin Villa. Outside work, Daniel enjoys literature,
theatre, hiking and biking.
urieli [at] cs utexas edu