AnalyticAscent Homepage


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Introduction:

Welcome to my landing page, proudly hosted via Keybase. I set this up as a way for people online to get an idea of who I am and what I do, achieving a one-way cold intro I guess.

In the many years that I've used the web for anything, I've never had a MySpace, Facebook, LinkedIn, or any other "networking" site that encourages users to make their life public. Maybe it's because I'm too cypherpunk to do that, or maybe there's a hidden safety rationale, or both.

The only reason I joined Twitter in May of 2016 was to use the site's API for a data science capstone project. Slowly I started to notice that the site could be useful, if used in moderation. I've made some interesting connections on there that I couldn't have found anywhere else.

Still, I prefer to be a nobody as far as most of the world is concerned. If anything I do has a major impact on the world, I want people to direct their praise at that and not me. I never want to lose the ability to relate to everyday people at an ordinary level.

 

Background:

Words mean different things to different people, but I feel like the lists below still communicate a lot in a short time. In no particular order, here are some major things I like, dislike, and have a study interest in. If you see some overlap then perhaps we should get in touch. Collaboration over the web continues to get easier, and there's no reason geography should keep like-minded people from doing so.

 

  • Likes: encryption, decentralization, open source, positive-sum actions, innovation, finding better ways to measure the outcomes of government policy, reducing the cost of knowledge, etc.
  • Dislikes: fame, partisan politics, cronyism, overcriminalization, zero-sum thinking, equalizing downward, people with strong viewpoints who can't steel man perspectives they disagree with, etc.
  • Interests: machine learning, natural language processing, data visualization, peer-to-peer and open source software, "crypto" in both senses of the term, enabling new economies of scale, and much more.
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    My interests in tech began with current events that were taking place in 2010 that made me realize how much it can shift the balance of power. I went from wondering why anyone would want a career that involved staring at a screen for endless hours, to realizing that's what most people do with their time anyway. So why not do it for something useful?

    Prior to that, I became interested in following public policy research from as many different think tanks as possible. It was so much fun to gather links to various blog posts and policy papers to create my own custom "Opposing Viewpoints" collections. I always found it to be much deeper than trying to follow the news 24/7, especially since policy researchers had more substantial things to say that didn't have such a short shelf life.

    The same was true of scientific papers. In high school, I started getting in the habit of trying to seek out the original studies that news articles would report on. It didn't take long for me to notice that what a given study said was often very different from what the headlines of news articles claimed the major findings were.

    It didn't take long for me to get so bogged down with what I was trying to keep up with that I wanted an easier way to sort out the stronger research and reporting from everything else. For years I would occasionally brainstorm how this could be done (checking for keywords was my first thought). But the pieces wouldn't come together until some formative learning took place between 2014 and 2016. That's when data science and machine learning in particular came into view.

     

    Speaking of learning, I always had some misgivings about the state of both grade school and higher education. It all seemed a bit arbitrary, and impractical once cost vs results came to my attention.

    I started having doubts about whether college education was as fruitful as people claimed when I came across MIT's OpenCourseWare. If a world-class education could be provided at zero cost (minus the testing, hands-on stuff, and degree itself), why was tuition as high as it was?

    Whether it's evaluating statistical conclusions, or enabling new education formats, my life interests and experiences have heavily influenced the major projects I want to do in life.

     

    Projects:

    There are two main software projects I'm brainstorming and working on at this time, both of which involve a lot of practical math use.

    One involves the use of supervised machine learning to infer what information is present in passages of text, with the purpose of determining what methodology was used to reach a statistical claim. Features checked for can include what metrics were used, sample size and techniques, confounding variables, trade-offs, as well as whether any attempt was made to sort cause from effect.

    The other is an edtech app that will focus on the "three Rs" first and foremost. For reading, it will focus on teaching a child beginning with the most commonly used letters and words in the English language first. This will lead to a smoother learning curve for adults learning English as a new language as well. Overall it will aim to reduce the cost of conveying information, collaboration with peers, and certification of what a user knows to a near-zero price point.

    I'm sure other ventures will come to mind, but most will share overlap with the two things I've described above. In the end I want everyone to have a better understanding of the world around them, and the capacity to make the most of their time within it.