Historically, all of scientific research and the technology resulting from it were originally considered a singular entity. So-called “natural philosophy” lacked the disciplinary categorizations which have defined most of the past century of human exploration and innovation. Recent decades have seen a growing push towards interdisciplinary work, bridging two, or perhaps even three disciplines together, and this has proven immensely productive across science, technology, engineering, medicine, the humanities, the arts, and just about any area of human endeavors.
Personally, I think that isn’t taking things far enough. Disciplines are a product of cultural and administrative forces. In other words, they’re a social construct. Rather than just bridging disciplines, I think there’s a great deal of opportunity to be had in simply rejecting their validity. Often the biggest barrier to interdisciplinary (and especially postdisciplinary) work is that each field tends to speak its own language or dialect, introducing shorthands and notions which improve in-group communication but can harshly limit communication between groups, fields, etc.. Yet in my experience teaching graduate mathematics coursework to PhD and MD students I’ve found that when notions are effectively translated from one field to another, those barriers start to fall away. It may not be possible to translate everything into plain language, but much more could be than typically is.
Originating from that perspective, this blog is a collection of some of my thoughts on topics ranging from machine learning, neurotechnology, security, biology, AI safety, mathematics, existential risk, medicine, and beyond. My hope is to start conversations, and if you’re interested in discussing anything I’ve posted about or a topic I haven’t written on, please do reach out! I’m always eager to hear from passionate people about their topics of interest.
About Me
I’m a current MD student at Stanford University, where I also work as the group leader of the Mathematical Medicine Group affiliated with the Petritsch Laboratory in the Department of Neurosurgery. My students and I do research on problems in biomedicine where precise mathematical analysis stands to offer something new and valuable, with an emphasis on mathematically rigorous machine learning and AI. This work tends to have a complex systems science flavor to it, and applications have ranged from cancer therapy optimization and psychiatry to neurotechnology and AI safety. The latter three are the current main focus.
Prior to this I completed my MS at Stanford focused on Theoretical Biophysics and ML/AI, as well as concurrent BS degrees in Computer Science, Physics, and Systems Science & Engineering through the dual-degree program at Washington University in St. Louis (I would say “Go Bears”, but more people have thought I was talking about Berkeley than actually known WashU shares the same mascot).
The Postdisciplinary Blog 