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This engineer hasn’t worked on anything cool lately.
Hoping to find a new job later this year and move onto something more interesting as a byproduct of that. Assuming that doesn’t lead to me being drowned in meetings and emails…
Working to make sure the electric grid doesn’t blow up when we add a few more generators.
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hits bong
For the last few years, I’ve been working on an open-source Ethernet switch.
That’s interesting as fuck, thanks for the good read.
For the most part, making sure our products run safely and groaning at the previous philosophy of moving fast and using duct tape for everything.
Oh and creating standards and templates for documentation, because that doesn’t exist either. The downside of working for a newer green energy company is that the typically established processes and methods aren’t established yet. And you get the fun task of changing that.
and you get the fun task of changing that.
Honestly not sure if you’re being sarcastic or not
Mostly sarcastic, but not entirely. It is kinda nice to be able to make the standard so that it meets everything you personally want.
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Yeah exactly. It’s just annoying how much work that takes
I worked in a lightweieght LMS that I will improve soon https://github.com/navitux/workshops_project/
Postdoc in engineering research - we’re using machine learning to predict chemical properties relevant to combustion, speeding up the discovery of cleaner liquid fuels as we transition away from fossil fuels!
interesting. how does that process work?
TL;DR, I throw a bunch of molecules at a pile of linear algebra, and hope predicted values line up with known experimental values; then I use the pile of linear algebra on novel molecules.
There’s a bit more to it than that, like how to represent molecules in a computer-readable format, generating additional input variables (molecular characteristics), input variable down-selection and/or dimensionality reduction, the specific ML models we use (feed-forward MLPs and graph convolution nets), and how to interpret results as they relate back to combustion.
From a broad perspective, our work is just a small part of a larger push from the Department of Energy to find economically-viable alternative liquid fuels. ML speeds up the process of screening candidate molecules, for example those found in bio-oil resulting from pyrolizing and catalytically-upgrading lignocellulosic biomass or other renewable sources. Our colleagues don’t have to synthesize large samples of many molecules just to test their properties and determine how they will behave in existing engines (a very costly and time-consuming process), instead we predict the properties and behaviors to highlight viable candidates so our colleagues can focus on analyzing those.
These papers (1, 2, 3) best outline the procedures and motivations for this work. PM me if you can’t get access and I’ll send you them!
cool, i’ll check out those papers. have you had much success in discovering new molecules this way? it seems like if it works then that means that the neural net that emerged has discovered some law of physics (or property of chemistry, w/e) that we do not know. in other words, within the sequence of calculations lies some physical law.
Sort of - the models are able to predict numerical property values given a large amount of data to observe during training. In other words, given the scope of known data, we can extrapolate predictions for new data. The predictive capabilities of the model are only as reliable as the data used to train it, and unfortunately in our case we only have hundreds of samples per property, as opposed to other ML tasks with millions of samples. This highlights how much time it actually takes to find, synthesize, and experimentally test molecules!
Unfortunately neural networks, especially traditional multi-layered feed-forward networks, are often seen as a “black box” approach to regression and classification, where we don’t really understand how a network learns or why its weights are tuned the way they are. Analysis methods have come a long way, but ambiguity still exists.
What we have done, however, is find the statistical significance of specific molecular substructures as they relate to combustion properties. For example, when we trained our models to predict sooting propensity (amount of pollution formed during combustion), we noticed that various algorithms such as random forest regression were putting a heck of a lot more weight into a molecular variable measuring path length (length of carbon chains, number of higher order bonds); from this, we were able to conclude that long-chain hydrocarbons with a higher number of double or triple bonds form more soot, and an idea of what mechanistic pathways we should stay away from when producing bio-oil.
As for fuel-grade molecules, we’ve found that furanic compounds and compounds with cyclohexane substructures generally have equal operating efficiency (cetane number), equal energy density (lower heating value, MJ/kg), operate well in various environments (optimal flash, boiling, and cloud points, deg. C), all while producing much less soot (yield sooting index) compared to diesel fuel. The next step is finding a cheap way to mass produce the stuff!
Recently we’ve started down the rabbit hole of fungus-derived bio-oils, terpenes (yes, those terpenes!) derived from fungus may be useful for use as soot-reducing fuel additives.
this is actually very interesting. i take it you’ve heard of the concept of “mechanistic interpretability”? perhaps you could learn something about your networks by implementing some of that methodology. here’s a glossary. also recommend poking around neelanda’s blog if you want to learn more.
Thanks for sharing! These seem to focus on LLMs/transformers, but since they use MLPs I should be able to find a way to adapt them for my use!
NDA :(
Nothing at all man. So many meeting, so depressing. Can’t wait to retire
Sometimes I have meetings about scheduling more meetings
Fun Fullstack webapp project (Springboot, React/RTK)
I’m working on open source session replay tool (skipping the name not to promote it explicitly, but its quite easy to guess since our niche has not too many fully OS companies) as R&D/js library maintainer; at the same time I’m making my own lemmy app :)
Very fun and quite the opposite experience (going in deep with browser specs and API vs thinking about mobile UI and features)
Been using some free time at work to make an inventory of pipeline stream crossings and plan on making it a GIS feature class for regular maintenance. This was inspired by an encased sanitary sewer essentially becoming a low head dam (just eroded, not discharging sewage) in a homeowners back yard and we were unaware until someone called.
It’s mostly just been tracing the features so far, but I’m thinking about where to take it next. Thinking a good direction to go next will be to use the elevation model to try and find manholes in high slope areas and ditches so they can be identified for monitoring for erosion or I&I.
Sounds like important work that most people never hear about. Glad to hear about it.
oh wow this post blew up! cool stuff y’all. unfortunately what i’m doing for job is boring (software engineer), but i try to do cool stuff on the side. like a laravel code generation thing, which also helps with a community exchange website that uses esri arcgis to map the things. not done yet and my progress is very slow because i can’t dedicate all my time to it. also helping some friends with a game in unity. also been trying to learn about lemmy, activitypub, decentralized apps, etc. and get involved in development.
They don’t pay us to work on cool things.
I’m marginally improving factory layouts bit by bit. Probably for the next 50 years
I’m sure there are people who genuinely find that kind of work to be really cool, but I’m with you. It wasn’t enough for me.
I just could not get motivated over my projects being “Maybe we should store the pallets right here in packaging instead of 100ft away on the other side of the building” or “Let’s replace the screwdrivers in assembly with drills to increase productivity”. Who the fuck needs an engineering degree to tell them that drills are way faster than screwdrivers?
Let alone the bullshittery around monitoring every minute of each employee’s day and trying to squeeze every ounce of productivity out of it. One manager gave an entire presentation about how if every operator is 1 minute late coming back to their station from break it adds up to like 2 full weeks for 1 employee by the end of the year.
Like…I saw that manager gossiping with HR for anywhere from like 15-45 minutes every day. But here we’re trying to harass our employees for taking ONE EXTRA MINUTE of their 30 minute lunch break.
I just couldn’t. Continuous Improvement can be cool but not when it’s that kind of stuff, not to me anyway.
Just wanted to say that if you feel similarly and it’s making you miserable there are cool engineering jobs out there. Even Continuous Improvement can be really fun if the manufacturing process is complex and requires actual engineering to improve.
Yeah I like a lot of what I do and it honestly gives me a lot of opportunities to express my class consciousness and stand up for reasonable expectations of workers. But yeah I do really miss research and want to be doing cooler stuff. I’m just still a junior engineer and can’t afford grad school yet.
I’m also absolutely looking for other work it just seems nobody is interested in junior engineers except the military.
Im also in this boat and it breaks my heart seeing this shit.
Designing circuit breakers
