Most of the blatant tax fraud is much lower down the economic ladder because below a certain threshold recovery doesn’t justify the cost and people know this. The amount you can get away with is far below the threshold where it would be worth the risk for wealthy parties. The best ROI for auditors in many of these cases is to make regular object lessons at random to discourage it rather than systematically prosecute it.
AFAIK, the increased spending at the IRS did not lead to concomitant offsetting recoveries. This is a predictable outcome, the amount of enforcement activity has been pretty finely tuned for decades to optimize ROI. Most of the recoveries come from changing focuses on compliance to areas that haven’t seen much enforcement activity in many years. Fighting entropy basically.
If you assume that most large recoveries are from sloppiness rather than systematic tax fraud, it changes what is going to be an effective strategy.
And maybe as a Bonus what do you make of the smaller (relative) taxrate the bigger fish (companies/wealthier individuals) pay?
Remote: Yes
Willing to relocate: Yes
Technologies: Python (PyTorch, TensorFlow), Reinforcement Learning, Medical Imaging, Machine Unlearning, Physics-informed ML, Quantum Computing, Protein Structure Prediction, NLP, Java, SQL
Résumé/CV: https://drive.google.com/file/d/1b9UfeqFvEu0t4sTGNd8yiJ1m60x...
Email: essence_mallard.5a@icloud.com
Hi, Kilian here :) ML engineer / research-minded generalist with a recent M.Sc. in Informatics from TUM, including a thesis on machine unlearning in medical imaging (@Harvard Medical School). I’ve worked on problems like reinforcement learning for tumor landmark detection in MRI, and deep learning for physical systems and protein structure prediction.
I like forming own ideas and following them through — from literature review to implementation, evaluation, and iteration. I can reproduce papers, fine-tune models, explore new methods, and design experiments that actually test hypotheses. I’m especially motivated by early-stage work that blends research thinking with practical engineering.
In team settings, I tend to gravitate toward coordination and planning roles, and outside of work, I’ve led a 300+ member volleyball department for several years — which taught me how to manage people, not just models.
I'm looking for roles at the intersection of research and engineering, eg early-stage AI projects where curiosity, ownership, technical depth matter and impact-driven development are valued.
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I also suspect I spend less time ruminating and second-guessing myself and other anxious behaviours that I imagine would come with having someone talking in your ear all day, but that's probably off topic.
Isn't the same meta process at play when thinking about more fuzzy topics?
We teach state-of-the-art generative AI algorithms for images, videos, proteins, etc. together with the mathematical tools to understand them.
Flow and diffusion models are mathematically demanding subjects - which is why many lectures restrict themselves to teaching high level intuition. Here, we give a mathematically rigorous and self-contained introduction yet aimed at beginners in AI. We hope you will like it!
Thanks @missedthecue, i wasn't aware this was productized and tested (though hoped it was)