We are leveraging computer vision and high-content imaging to rewrite the drug discovery process for solid tumor cancers. The vast majority of drug screens ask questions that are too simplistic from a biological perspective, limiting our ability to discover effective cancer therapeutics. By pairing AI with multiplexed imaging of patient-derived cells, we can target the most harmful behaviors of metastatic cells, resulting in a more promising pipeline of drug candidates.
Formerly: Director of Data Science @Cape Analytics
Cape uses geospatial data and large-scale aerial imagery to develop AI models that detect and describe residential homes across the US. Our technology stack included Python, PostgreSQL, tensorflow/caffe, AWS, and GCP.
Formerly: Director of Data Science @RadiumOne
RadiumOne developed and automate targeting algorithms for programmatic advertising at RadiumOne. Our technology stack included Python, Hive on Hadoop, GraphLab, R, and Tableau.
June 2014: Insight Data Science
In three weeks, I developed an application that uses graph theory and a minimization algorithm to generate playlists with exceptionally smooth transitions between songs.
June 2014: Stanford PhD in Materials Science & Engineering
Thesis topic: Protein-engineered hydrogels for spinal cord regeneration.
Fellowships: NSF Graduate Research Fellowship and a Stanford Graduate Fellowship.
My thesis project relied heavily on image processing and statistical analysis to describe 3-dimensional biological structures in a quantifiable way. The process of parsing the signal from the noise, identifying critical metrics, and presenting meaningful results were the most exciting aspects of my thesis research.
I am currently seeking my next step:
a data science position in which I can leverage my analytic skills toward a direct and influential impact in the marketplace. If you would like to know more about my areas of expertise, or to discuss data science in general: