Hello! I’m Chris. I live in New York City and professionally argue with computers for a living. At this point I’ve spent just north of 20 years arguing with computers for money. I claim that I’ve been doing it long enough now that I usually win the fight. But that is, of course, folly. I also write from time to time. On a few occasions I’ve managed, but to be honest, I don’t care for it. I’m more of a senior IC/CTO-with-an-awesome-VP-of-engineering type.
I have a college degree (computer science) but didn’t stick around to get the PhD. Though I have coauthored several papers and I’ve professionally done research multiple times (networks/graphs & biology/proteins).
I’ve been the owner of my own business several times. Sometimes it’s even worked out well. Mostly not, though. Either way- I have a lot of stories.
I’m a huge fan of The Cult of Done. Specifically #9 “People without dirty hands are wrong. Doing something makes you right.” As such, I’ve done a lot of things at a lot of places, so here’s a sample of my focus areas and interests.
Areas of Interest
General Software Engineering
- Toward a Better Technical Interview
- Manually linking Rust binaries
mainin ELF headers
- Building a kernel for arm64
- Extreme Debugging
SRE & Operations
I’ve spent at least a decade focused on how to keep computers running. They’re like lemmings running for a cliff at full tilt. My work here has included building visibility pipelines for monitoring & alerting, all manner of custom tooling for Linux, turning pallets of servers into functioning data centers, etc.
Somehow I often wind up in the security wing of the org. I’ve worked for a public certificate authority. I’ve written a handy certificate authority tool in Ruby. Helped build a pretty fancy machine-learning-with-big-data based threat detection system for a payment company. Numerous additional other projects as well.
- Shippable Data centers & Secure Boot
- Certificates: A Primer
- Extreme Debugging (as reverse engineering)
Data Science, Machine Learning, & Research
Spent a few years moving tons of bits around and applying analysis or experiments at scale. Sometimes this was with Hadoop or Spark, other times it’s JupyterLab. For deep learning I’m slightly more comfortable with TensorFlow, but I have also worked with PyTorch. I wouldn’t call myself an expert, but in related work I’ve made several D3 visualizations. I’ve coauthored several papers in networks & graphs related to wireless multihop signal routing as well as a handful of papers in computational structural biology and metagenomics. See my Google Scholar page for papers. Check out DeepFRI in particular for a web-based protein function identification service.