Executive Summary

• Developing predictive analytics using genomic data
• Selected Publications on PubMed

Education

• 2010 Postdoc, IBM Computational Biology Center
• 2008 Ph.D. in Biomedical Engineering, Johns Hopkins University
• 1997 B.S.E in Bioengineering, University of Pennsylvania

Research Topics

• Applying large-scale computing to problems in human genetics
• Developing analysis methods to unravel the genetic basis of disease
• Integrating genomic data with electronic medical records to develop predictive clinical analytics to help physicians better serve patients
• Crowd-sourcing biological data analysis and systems biology models (DREAM)
• Cancer genomics

Background and Approach

The biomedical research community is at an inflection point where DNA sequencing and related omic (genome, transcriptome, proteome, ...) data acquisition platforms are beginning to be deployed to study people. Just as the research community is glimpsing a handful of examples of individual human DNA sequences for the first time, entrepreneurial clinical researchers are trying to deliver on the promise of personalized medicine.

Personalized omic medicine will transform healthcare in application areas where genetic determinism is strong. Cancer, a collection of diseases of the DNA, is poised for the greatest return on investment in the near term. Also, diagnosis and treatment of rare genetic anomalies is for the first time possible using genome sequencing.

Researchers pursuing innovation in personalized omic medicine require a comfort level in at least biology or medicine, plus one or more of {math, statistics, engineering, physics, computer science, computer know-how, other}. My formal training is in biology and systems engineering. I've applied a pragmatic approach to high-throughput genomic data analysis for 14 years. I'm starting to get good at it.