About

Who I am

I recently defended my PhD in Biomedical Engineering doing work on cancer detection in the Ngo lab at CEDAR (part of Oregon Health & Science University). I am interested in using and developing computational tools that allow scientists to better understand their genomic data, and data in general.

Currently looking for my next opportunity!

Scientific questions I find interesting

• Are tumors found with imaging different from those found with sequencing? Is one modality better for finding tumors that have a higher mortality?
• Is it possible to create simplified models of any cell that are human understandable? i.e. toy circuits that work for all of the different pathways that exist within a cell.
• If it is possible to create these simplified models, can we find ways to measure how much error they have from the “ground truth”?
• How long did it take life to evolve/why is the cell seemingly the only unit of life that exists on earth and why is so much of our DNA shared?
• What would cellular life on other planets look like? Could we ever detect it if we were on a planet with life?
• What important questions can be asked or answered with faster algorithms? Is there any algorithm where a 10-100x speedup would change the way research is done in that field?
• How many computational algorithms can be defined with an easy to measure cost function? Which ones have feasible methods to find the optimum in an acceptable time frame? (Which algorithms exist in P?)

Approaches I find interesting right now

• Probabilistic models and linear models
• Computational models that can be used to increase human understanding not just predict future states.
• Lean and proof based coding to improve safety and allow for more trust about code that is generated
• GPU acceleration of algorithms and multi threading approaches