I'm enthusiastic about sourdough pizza, cities, biosecurity, space exploration, energy, global development, bikes, and desserts.
By day I'm an engineering manager and research scientist at SecureBio. Drop me a line: evan@<company>.org
Previously I was the VP of data science and engineering at Zoba, a startup building optimization software and analytics for micromobility and on-demand delivery.
Submissions
Nomai Writing
nomai-writing.com
2 points·by Tarrosion··0 comments
The perfect global grid system is impossible; HexGeoGrids.jl
SecureBio Detection | Senior infrastructure engineer; [senior] software engineer (high-performance pipelines)
Cambridge, MA, USA (metro Boston) | ONSITE strongly preferred; REMOTE (US east coast) possible for exceptional candidates
SecureBio is a nonprofit working to protect the world from catastrophic pandemics. We operate the world’s largest metagenomic biosurveillance network, which performs daily sequencing of wastewater samples and nasal swabs to screen for emerging pathogens. Our computational team develops detection pipelines responsible for rapid processing of deep sequencing data in order to flag potential threats.
Prior experience with biology or bioinformatics is not required. If you're interested, please apply via the relevant job posting. Feel free to drop me a line with questions; email is in my profile.
Cambridge, MA, USA (metro Boston) | ONSITE strongly preferred; REMOTE (US east coast) possible for exceptional candidates
SecureBio is a nonprofit working to protect the world from catastrophic pandemics. We operate the world’s largest metagenomic biosurveillance network, which performs daily sequencing of wastewater samples and nasal swabs to screen for emerging pathogens. Our computational team develops detection pipelines responsible for rapid processing of deep sequencing data in order to flag potential threats.
Prior experience with biology or bioinformatics is not required. If you're interested, please apply via the relevant job posting. Feel free to drop me a line with questions; email is in my profile.
I'm curious to see how Claude can interact with Blender, and how people use it. I use Claude every day for both work and personal research, overall think it's a great product, but I've found it (thus far, never bet against generation n+1) remarkably terrible at spatial reasoning. That seems pretty key for Blender!
This author is really prolific in the Julia visualization ecosystem. One of my favorite projects from them is actually quite useful outside Julia: a super minimal but slick Unicode character lookup, glyphy.info.
[citation needed] that some combination of "New Urbanism, traditional neighbourhood design, streetcar suburbs, one-way streets, bike paths, walking paths, mixed-zone walkable villages (light commercial with residential), smaller single-family houses and duplexes, triplexes, houses behind houses." is not in fact optimal! (For certain objective functions)
> Is multi-agent collaboration actually useful or am I just solving my own niche problem?
I often write with Claude, and at work we have Gemini code reviews on GitHub; definitely these two catch different things. I'd be excited to have them working together in parallel in a nice interface.
If our ops team gives this a thumbs-up security wise I'll be excited to try it out when back at work.
I'm sorry for your loss, and I hope that helping others through this project helps you find some solace. IMHO, it's a mark of character that your response to having a problem is "I want to help other people so they suffer this problem less than I did."
This makes sense in the context of trying to maximize log wealth (or I think any concave function of wealth, though the arithmetic is different). But in one of OP's other articles [1], he says the Kelly criterion doesn't require trying to maximize log-wealth, that this is just a common misconception -- all that's required is maximizing something growing geometrically over time.
This I don't understand, maybe someone help me out? Say the real growth rate of capital (or interest rate available to me, whatever) is 2%/year and I have a 10 year time horizon. So $1.00 today is ~$1.22 in 10 years. More generally, if I have wealth X today I will have 1.22X in 10 years. And if X is not a constant but a random variable and I want to maximize future expected wealth (not log wealth), that's just max(E[1.22X]) and by linearity of expectation I should just maximize wealth today to maximize in 10 years time.
So Kelly being appropriate must have some other conditions, right? Wanting to maximize log wealth is surely sufficient (and individually probably ~rational). What else?
OP, you have a comment there about mouse support in the Keyboardio. I've been using a Keyboardio since 2019 and haven't much tried the mouse support -- any advice? How did you set it up?
I'm curious what kind of slow IO is a pain point for you -- I was surprised to read this comment because I normally think of Julia IO being pretty fast. I don't doubt there are cases where the Julia experience is slower than in other languages, I'm just curious what you're encountering since my experience is the opposite.
Tiny example (which blends Julia-the-language and Julia-the-ecosystem, for better and worse): I just timed reading the most recent CSV I generated in real life, a relatively small 14k rows x 19 columns. 10ms in Julia+CSV+DataFrames, 37ms in Python+Pandas...ie much faster in Julia but also not a pain point either way.
How do modern foundation models avoid multi-layer perceptron scaling issues? Don't they have big feed-forward components in addition to the transformers?
Blurb: PhD in operations research -> startup employee #2 -> scale startup, gradually moving from data science IC to VP of data science + software engineering -> now looking for mission-meaningful hands-on technical work. I'm particularly good at technical communication as well as translating from the physical world (science/business) to math models. Open to companies of any size, especially motivated by the biosecurity, biotech, robotics, and clean energy sectors.
Blurb: PhD in operations research -> startup employee #2 -> scale startup, gradually moving from data science IC to VP of data science + software engineering -> now looking for meaningful hands-on technical work at a company inventing something in the world of atoms
* Car users are quite dependent on the government for transportation, e.g. the many billions (in the US) of public dollars spent on roads each year. Hard to get around without them!
* Even if we hand-wave that away and assume car users aren't dependent on the government for transportation, surely _everyone_ is dependent for other reasons like enjoying public goods (national defense, clean air and water, rule of law), access to the social safety net, etc.?
I've heard this before -- that oversized cooling units (whether standalone AC or part of a heat pump) mean muggy interiors in the humid seasons. But...why? I'd think that a fixed amount of air compressed in the compressor means a fixed amount of condensation runoff from the unit, and it wouldn't matter much whether it's a big unit running occasionally or a small unit running frequently. Why is that wrong?
By day I'm an engineering manager and research scientist at SecureBio. Drop me a line: evan@<company>.org
Previously I was the VP of data science and engineering at Zoba, a startup building optimization software and analytics for micromobility and on-demand delivery.