(Why mention Greenland? It’s a movie reference.)
I figured I’d give an update on my personal life.
A year ago, I resigned from my position in France and moved back to Denmark. I had planned to spend a few months as a visiting researcher in my old haunts at the Niels Bohr Institute, courtesy of the spare funding of a generous friend. There turned out to be more funding than expected, and what was planned as just a few months was extended to almost a year.
I spent that year learning something new. It was still an amplitudes project, trying to make particle physics predictions more efficient. But this time I used Python. I looked into reinforcement learning and PyTorch, played with using a locally hosted Large Language Model to generate random code, and ended up getting good results from a classic genetic programming approach. Along the way I set up a SQL database, configured Docker containers, and puzzled out interactions with CUDA. I’ve got a paper in the works, I’ll post about it when it’s out.
All the while, on the side, I’ve been seeking out stories. I’ve not just been a writer, but a journalist, tracking down leads and interviewing experts. I had three pieces in Quanta Magazine and one in Ars Technica.
Based on that, I know I can make money doing science journalism. What I don’t know yet is whether I can make a living doing it. This year, I’ll figure that out. With the project at the Niels Bohr Institute over, I’ll have more time to seek out leads and pitch to more outlets. I’ll see whether I can turn a skill into a career.
So if you’re a scientist with a story to tell, if you’ve discovered something or accomplished something or just know something that the public doesn’t, and that you want to share: do reach out. There’s a lot that can be of interest, passion that can be shared.
At the same time, I don’t know yet whether I can make a living as a freelancer. Many people try and don’t succeed. So I’m keeping my CV polished and my eyes open. I have more experience now with Data Science tools, and I’ve got a few side projects cooking that should give me a bit more. I have a few directions in mind, but ultimately, I’m flexible. I like being part of a team, and with enthusiastic and competent colleagues I can get excited about pretty much anything. So if you’re hiring in Copenhagen, if you’re open to someone with ten years of STEM experience who’s just starting to see what industry has to offer, then let’s chat. Even if we’re not a good fit, I bet you’ve got a good story to tell.
4 gravitons 
“if you’ve discovered something or accomplished something or just know something that the public doesn’t, and that you want to share: do reach out.”
People are often interested but don’t yet know how to use the ideas I most recently presented to the Oxford Philosophy of Physics Seminar on October 24th, titled “A Dataset & Signal Analysis Interpretation of Quantum Field Theory”, on YouTube here, https://youtu.be/61H0o8W9xg8 (or glance through the PDF of the slides, https://www.academia.edu/125766143). There is a foundation of three published articles in Physica Scripta 2019, Annals of Physics 2020, and JPhysA 2022.
A focus on data analysis brings machine learning closer to generalized probability and quantum measurement theory and has the potential to reduce the woo aspect of QM.
I’m a flawed messenger and the message is incomplete, so I’m expecting not to hear from you, but I will happily correspond further or talk. I want to share these ideas because I would like to see what better mathematicians than I am can do with them.
Best wishes for 2025, in any case.
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Hello Matt, I’ve been a reader of your blog for many years. I’ve worked
in the tech industry for 46 years ever since I received my Ph.D. in High
Energy Physics. I don’t work in Copenhagen but I do work for Oracle. I’m
a database and AI guy. I was an Adjunct Professor in the Computer
Science department at the University of Texas for 18 years until I
retired in 2018. I too have jumped on the GenAI bandwagon and I’m trying
to move Oracle in that direction. I have a series of blogs on Medium
https://medium.com/@phil.cannata_84963. It might be interesting to
chat about common interests if you’re interested?
Phil
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“I looked into reinforcement learning and PyTorch, played with using a locally hosted Large Language Model to generate random code, and ended up getting good results from a classic genetic programming approach.”
This is a wild sentence! You ended up getting something actually useful from using a genetic algorithm to come up with formulae? How can you evaluate fitness?
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I’ll have a longer post when we put the paper up, but short summary: there’s a method used to evaluate Feynman diagrams that involves solving a large, overcomplete linear system many times for different values of input parameters. We used genetic programming to find programs that select a subset of the linear system that still solves for what we need, giving higher fitness to those that find smaller systems.
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