- 10 years industry experience as Software and Systems Engineer
- Masters Systems Engineering, University of Arizona, 2006
- Doctoral Student - Computer Engineering
- NSF WVEPSCOR STEM Fellow 2012
- NSF WV NanoSAFE Fellow 2013
- Unsupervised Learning
- Association Learning
- Biclustering
- Large Datasets where Attributes >= Instances
- Bioinformatics
Crowd Heuristics Algorithm Learning Kit (CHALK)
Goal: Develop a Virtual Screeing for DNA Aptamers leveraging insights from aptamer researchers
Algorithm: EMFP
- Combining Association Learning and Probabilistic Clustering into a Biclustering Framework
- Pattern-based, Soft Biclustering Algorithm for Binary Dbs
- High quality ( F, pd, prec), low error ( RMSE, pf ) statistically relevant biclusters
- Compared to BicBin, vanUitert08
Paper pending internal review
Further Research:
- Show that Prelic's Match Score is biased against statistically relevant small clusters in high noise databases. Prelic06
- Develop a streaming EMFP implementation
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