Mark A Coletti

Staff Scientist

As a staff scientist at the Oak Ridge National Laboratory, I am actively performing research in the areas of geoinformatics, machine learning, and evolutionary computation.  My current principal research focus is on using asynchronous steady-state evolutionary algorithms to optimize deep-learner architectures and hyper-parameters. I am also involved in research related to volunteered geographic information and agent-based modeling.

Previously, I was the Chair of the Penn State Postdoctoral Society, and as such was responsible for organizing career enhancement, personal improvement, and social activities for over 460 postdoctoral scholars.  Also, I worked at George Mason University's Center for Social Complexity and Evolutionary Computation Laboratory where I developed an evolutionary computation C++ toolkit; a biologically inspired cognitive model for a DARPA Grand Challenge; a Joint Improvised Explosive Device Defeat Organization related multiagent simulation; an Office of Naval Research  Multidisciplinary University Research Initiative Office sponsored massive multiagent simulation of pastoral and farming behavior in eastern Africa; and a geospatial extension, GeoMason, for the multi-agent simulation toolkit MASON.  

Earlier in my career I worked as a senior software engineer in the Washington, DC, area on projects for the National Oceanic and Atmospheric Administration, Federal Highway Administration, U. S. Army's Materiel Command, the U. S. Army Topographic Engineering Center, and the United States Geological Survey.  These projects included an expert system to correct human sourced sea surface meteorological data, an expert system for validating materiel purchases, a topographic visualization system, a road surface wear calculator, and a toolkit for spatial data format conversion.   

Awards

July 2009, Best Graduate Student Workshop Paper at GECCO

Facilities

Titan and Summit

Specialized Equipment

I use the Titan and Summit supercomputer platforms for my evolutionary computation / deep learner research.