About Me


About Me

I am a mathematician that worked in financial services for eight years, most recently at BlackRock, spending a significant portion of my free time conducting research in information theory. I spent the last four years conducting this research full-time, and the last two years coding and writing full-time. In addition to my scientific writing, I’ve also published in The Atlantic, and elsewhere, writing about banking, finance, and economics, which was widely cited by bank regulators, and other legal and financial professionals, including Judge Richard Posner.

I received my J.D. from New York University School of Law, and my B.A. in Computer Science from Hunter College, City University of New York.

About My Work

I’ve reduced machine learning and deep learning to a set of algorithms that are so fast, they can run on any consumer device, with run times that are a small fraction of comparable techniques. I’ve also rewritten all of special relativity using objective time, and developed a novel and unified theory of gravity, charge, and magnetism.

Formal working papers are available on my ResearchGate homepage.

Algorithms are available here.

For a high level summary of my work in A.I., read my paper, Vectorized Deep Learning.

Theoretical Foundations

All of my work in physics and artificial intelligence follows almost entirely from the works of Alan Turing and Claude Shannon.

My model of physics treats reality itself as a computational engine, and in particular, treats elementary particles as combinatorial objects. I show that, remarkably, Einstein’s equations for time-dilation follow, despite the fact that my model has absolutely no superficial connection or similarity to relativity. In short, I’ve developed an entirely new model of physics that is closer to Newton’s idea of a mechanical universe by making use of contemporary theories of information and computation.

My model of artificial intelligence imitates tasks accomplished by machine learning and deep learning algorithms, but my model is radically more efficient than any other algorithms that I’m aware of: all of my algorithms have a low-degree polynomial runtime, allowing them to accomplish extremely high-dimensional, sophisticated tasks such as 3D object classification, projectile path prediction, and image classification, quickly and accurately on ordinary, cheap consumer devices.

The fundamental observation that underlies my model of AI is that the complexity of an object depends upon the level of granularity that we use to observe the object. If we take a very detailed view of an object, its complexity will be high, whereas if we take a less detailed, “impressionistic” view of an object, its complexity will be low.

This simple, common sense observation is remarkably useful. Specifically, my algorithms search for a local optimum level of complexity in between these two extremes, which I’ve found to be the point at which the actual structure of an object comes into focus. This allows, for example, my algorithms to categorize a dataset, or partition an image, with no prior information at all, simply by iterating through different levels of granularity until it finds the optimum level of complexity that reveals the actual structure of the data, or the image.

This simple initial procedure allows a core set of three algorithms (image partition, categorization, and prediction) to accomplish nearly everything that can be done in AI, with simple “plug-ins” that address the particular tasks at hand.

Contact Info

Resume: C. Davi Resume

Email: charles@blacktreeautoml.com

Fine Art

I also compose music, which you can find on soundcould and vimeo, and write a blog about fine art, Kalles Kultur.

If you’ve made it this far, you should probably read my books, Sketches of the Inchoate, and VeGa.

And as imagination bodies forth
The forms of things unknown, the poet’s pen
Turns them to shapes and gives to airy nothing
A local habitation and a name.


15 thoughts on “About Me

  1. Does Buffett’s PUT Sale ($4.5 bil) require any margin collateral, if out of money by a significant margin? I know it’s not exerciserable until 2019 but he is writing them market to market.

  2. Charles, I did a post about the Michael Lewis piece on Portfolio. Did I get these correct?

    “The arrangement bore the same relation to actual finance as fantasy football bears to the N.F.L. Eisman was perplexed in particular about why Wall Street firms would be coming to him and asking him to sell short. “What Lippman did, to his credit, was he came around several times to me and said, ‘Short this market,’ ” Eisman says. “In my entire life, I never saw a sell-side guy come in and say, ‘Short my market.’ ”

    I’ve already used the football and betting analysis in my losing debate with Derivative Dribble.

    “The only difference was that there was no actual homebuyer or borrower. The only assets backing the bonds were the side bets Eisman and others made with firms like Goldman Sachs.”

    Sorry, if done right, these can be useful for determining the likelihood of default. It’s not the product. I wouldn’t do it, but I’ll use the info.

    Take care,

  3. I love the format of your website. It’s far more readable than most blogs. And of course the content is top notch. Keep up the good work.

  4. Hi Don,

    Something you need to keep in mind is that firms use financial instruments to alter the risks that their portfolios contain. That means that there are probably investors out there who are indifferent to real bonds and synthetic bonds. That is, they just want the exposure to credit risk.

  5. Just read Theory of CDS pt2. Brilliant!

    (yes, I read the others, and they’re pretty good, too. Thanks for your efforts in writing them all.)

  6. Mate – you are a genius. This blog is great and I would advise anybody starting out in the industry (if there is anyone starting out or any of the industry left) to read this.

  7. Excellent blog, enlightening and informative. Could you also consider writing on a related subject-bonds/interest rate swaps, etc. and in particular the affect on states, school districts,etc.? My independent research on this subject disturbing and confusing to this common taxpaying schmuck. Thanks for your good work and clear head.

  8. Quite fun snippets to read. Having just stumbled upon your blog while researching synthetic CDOs, I appreciate the links to your other articles for the background material they provide.

    You may have already read the book “Fools Gold”, which I am in the process of reading, but I have found this to be an enthralling read on the history of credit derivatives at J.P. Morgan and their role in the recent credit crisis.

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