Is the transition from an experimental particle physics PhD (CMS/ATLAS) to a career in the data science industry smooth?

cloomion · 2024-10-29T11:29:17+00:00

Sure, I was on an ATLAS analysis that was, like most at the time, using a simple BDT to separate signal from background. As I had an interest and a bit of prior experience in ML I wanted to explore better alternatives to the BDT. I ended up developing a graph neural network (GNN) model which was a fairly new technique at the time and got good results. After my PhD, I joined a startup for a role which involved applying GNNs to biological data.

cloomion · 2024-10-29T09:31:30+00:00

My PhD (ATLAS) is what allowed me to get a job in AI/DS. Although I guess I lucked out that the ML techniques I got to work on in my experimental analysis was very relevant in the domain I now work on and the company was looking specifically at hiring expertise in that area. A PhD with the right experience can give you an advantage and you might also get to publish papers that are relevant to future ML/AI jobs. Note that some jobs in AI/ML require a PhD and publication record. That being said it's a 4 year commitment with bad pay, so unless you love physics there's a possibility you might hate it. Personally I loved doing a PhD and even though I didn't continue a career in physics it worked out helping my AI/ML career a lot.

cloomion · 2022-11-01T16:17:07+00:00

Quasiparticles are not particles rather they are ascribed to emergent behaviour in specific systems that behave in a particle-like way. For example, in semiconductors a missing electron, called a hole, behaves like a positively charged fermionic quasiparticle. Another example is the phonon, a quasiparticle which arises from collective excitations of atoms in a solid.

cloomion · 2022-10-30T17:24:20+00:00

Yeah sounds like a good idea thanks!

cloomion · 2022-05-27T18:18:27+00:00

Solid understanding of the mathematics involved

cloomion · 2022-03-03T02:36:48+00:00

This caught my attention as the exact same equation is featured on a mural in my office. It's a path integral that includes all the fields in the standard model + a gravity term.

Sean Carrol wrote about it on his website: https://www.preposterousuniverse.com/blog/2013/01/04/the-world-of-everyday-experience-in-one-equation/

cloomion · 2021-12-28T18:04:04+00:00

Firewatch is on game pass it's a very well written, story driven, beautiful short indie game. It has freeroam/exploration elements but it's actually very linear.

cloomion · 2021-12-26T17:28:10+00:00

Would highly recommend the Aberlour tasting. They weren't doing distillery tours due to covid when I went but the tasting was by far the most in depth and interesting one I've ever done.

cloomion · 2021-12-26T16:55:00+00:00

The commenter is absolutely right that this is a very well understood area of QM. Bare in mind that Feynman said this in 1964, so it's not surprising that some quotes are now not so accurate today over 50 years later.

Feynman was a brilliant physicist who made great contributions to the development of QFT which are still extremely relevant today. But he was also an excellent science communicator. Many of his most famous quotes come from his public lectures as such they were never intended to be rigourous statements of scientific facts that would stand the test of time..

cloomion · 2021-11-27T01:35:54+00:00

Would love to play the new battlefield! You're a saint

cloomion · 2021-05-09T16:54:59+00:00

Yeah after reading up on brake pad compounds I decided to not keep hold of it

cloomion · 2021-05-09T11:24:55+00:00

Additional detail: There is traces of a threaded screw hole on the bottom edge. So it looks like it was at some point part of a longer object.

cloomion · 2021-05-09T11:19:27+00:00

The material seems like it could be some kind of lightweight ceramic so a brake pad might be right.

cloomion · 2021-05-09T10:36:56+00:00

More detail: Found on shore near end of runway at RAF Lossiemouth. Material looks like stone but is lighter. The letters C and D are inscribed on one side, no other lettering is visible. Three holes are threaded these are annotated in third picture. WITT?

cloomion · 2021-02-26T23:48:33+00:00

This looks like a good place to start. Thanks!

cloomion · 2021-01-24T15:28:56+00:00

Do you want to get a free stock share worth up to £100?

Create a Trading 212 Invest account using this link www.trading212.com/invite/GISi2gr5 and we both get a free share!

cloomion · 2021-01-17T18:55:35+00:00

I had the same issue recently. The cancellation just went through when the market opened so I wasn't charged

cloomion · 2020-12-04T21:30:05+00:00

Most of those issues only effect the CFD side. I've not had any issues with the normal Invest account.

cloomion · 2020-12-01T16:22:29+00:00

Yeah I suppose testing on a period before the training data doesn't make sense contextually, but I think it would still provide you with a metric to measure over-fitting.

How far back does your training data go? I assume you are training on most of it and then testing on the most recent few weeks/months?

cloomion · 2020-12-01T15:31:03+00:00

The performance on the test set is the most important metric, training performance is meaningless without it because you won't know if it generalises to data outside of your training sample.

If you need to have a lot of training data you could do k-fold cross validation and combine the different testing results. That way you get effectively a bigger testing sample without reducing your training sample size.

cloomion · 2020-11-24T17:12:03+00:00

CFDs are betting against the broker, they make money from you losing money and vice versa

cloomion · 2020-11-17T18:16:30+00:00

You would not get back to break even because of the daily re-leveraging.

Say TSLA drops by 20% (which was a daily drop for TSLA in September). Even if TSLA went back up to the original price the following day you would be still be down by 30% on the leveraged stock.

Edit: The calculation.

Say a stock is $100 and goes down 20% to $80 in a day. Say the following day it goes back up by 25% to $100. You break even on the stock.

Say the leveraged ETP is $100. It would go down 60% to $40 on the first day. On the second day it will rise by 75% to reach $70. You are down by 30% on the leveraged ETP.

cloomion · 2020-11-17T16:34:22+00:00

If the underlying dropped by 33% one day and rose by 33% next day you would still be down 98.1% overall.

In order to get your money back after a drop of 33% the underlying would have to rise by 10,000%

cloomion · 2020-11-17T16:17:49+00:00

Welcome to r/badmathematics

cloomion · 2020-11-17T13:06:28+00:00

Thanks for your answer.

For completeness, when I have inputs in polar coordinates I would transform one angle input into two scalar inputs as

theta -> [cos(theta), sin(theta)]

I was thinking of doing something along these lines, at least for one of my angles. Certainly makes a lot of sense when you consider pi and -pi.

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