I am dedicating my life to making biology easier to engineer.
There’s a very real and unique opportunity to rewrite history over the next few decades. Computing, statistical tooling (what the misguided call “AI”??) and high-throughput + miniaturized assays are providing an opportunity.
Those with comparatively less resources but considerably more capability than the incumbent will rebuild biotech from scratch, ushering in a new age of consumer biologics, synthetic life forms, and personalized therapeutics.
The visible cadence of progress has been stalled because people aren’t ambitious enough or diversely technical. Social cancer and (misaligned) incentives certainly don’t help.
However, there has never been a better time in history to try to actually engineer biology.
And while dreams of civilization scale progress and a collective drive for understanding have largely died with my generation, where the great physicists have been eschewed for head bobbing Tik Tok idiots, there is still hope for a future of progress.
We have the library of Alexandria on our mobile phones and the collective body of scientific literature at our fingertips . What’s keeping a team of polyglot programmers with strong mathematical priors from consuming the world with synthetic biology?
Foundational interests + inspirations:
- distributed + serverless computing
- bayesian statistics
- sparse coding
- microservice architectures (mostly K8s)
- early 1800s American history and the great inventors
- coding schemes w.r.t biological sequence
- information theory + evolution
Some technical notes/derivations here
Reach out if you are not bad and want to work on these problems for real.
github | twitter