I am currently a Senior Image AI/ML Scientist at a large BioPharma company. Among other things, I primarily research computational methods for analyzing large cellular microscopy images to discover novel drug targets, and to understand and improve patient responses in clinical trials. This imaging research includes deep learning, statistical analysis, feature extraction, spatial and community/neighborhood analysis, and integration with biomarker, genomic, transcriptomic, or clinical data.

Previously, I was a Data Management Research Scientist for the Large Synoptic Survey Telescope, at the University of Washington dept. of Astronomy, where I researched algorithms and developed software for image subtraction and transient detection, with interest in all kinds of transients, particularly supernovae.

Prior to that, I was a Senior Computational Systems Biology Research Scientist at the Institute for Systems Biology, where I developed computational algorithms and methods for modeling systems-level biological data.

For my PhD, I developed and applied methods for detection of faint signals in astronomical images, and applied them to discover nearby and distant supernovae. This research contributed to the discovery of the accelerating expansion of the universe (as part of the High-Z Supernova Search Team), a discovery which received the Nobel Prize in Physics (2011), the Gruber Cosmology Prize (2007), and the Breakthrough Prize in Fundamental Physics (2015).

  • Some of my published computational biology software:
    • Netmotsa - Network-oriented Gibbs sampling for motif detection
    • cMonkey - Biclustering of mRNA expression data, constrained by biological priors (interaction networks, DNA sequence motifs, etc.)
    • Inferelator - Inference of gene regulatory networks (in conjunction with cMonkey)
    • MeDiChI - Model-based deconvolution of genome wide DNA binding (ChIP-chip) data
    • tilingArraySeg - Multivariate segmentation of high-resolution tiling microarray data