Gregory P. Amis, Ph.D.
Machine learning expert and algorithm R&D team lead, with doctoral training in autonomous learning systems and production software engineering experience. My interests are in solving real-world problems in predictive analysis while advancing the state of the art.
- Resume (pdf)
Requires Adobe Acrobat Reader.
- Main dissertation project
I developed a neural network for pattern classification that learns from multiple data sources with disparate input dimensionalities, creating a unified model of the underlying class patterns. The system learns on-line, does not require parameter adjustment for robust performance, and is resilient to changing deployment contexts. In short, it is a fully autonomous learning system.
This work was done in collaboration with my PhD adviser, Gail A. Carpenter, in the Technology Lab at Boston University's Department of Cognitive & Neural Systems. A first generation of this model has been accepted for publication to the journal Neural Networks.
- Tech report (pdf)
Amis, G. & Carpenter, G. A. (2009). Self-Supervised ARTMAP. Neural Networks, doi:10.1016/j.neunet. 2009.07.026. (In press.)
Animations illustrating the self-supervised learning paradigm and self-supervised ARTMAP performance. (Requires Adobe Flash plug-in.)
Zip archive of Java implementation with MATLAB scripts.
Zip archive of a MATLAB .mat file for Boston remote sensing benchmark.
- Other research projects
- Default ARTMAP 2
A successor to default ARTMAP (Carpenter, 2003) for robust pattern learning and classification across problem domains.
- Writing samples
- Distributed ARTMAP
A summary of the distributed ARTMAP model, from
Amis, G. P., Carpenter, G. A., Ersoy, B., & Grossberg, S. (2009). Cortical learning of recognition categories: a resolution of the exemplar vs. prototype debate. Technical Report, CAS/CNS TR-2009-002, Boston, MA: Boston University.
- "Traveling Gaussian Waves" Simulation Assignment