Giter Site home page Giter Site logo

queelius / likelihood.model.series.md Goto Github PK

View Code? Open in Web Editor NEW
1.0 1.0 0.0 42.82 MB

Likelihood model for series systems with masked component cause of failure and other censoring mechanisms

Home Page: https://queelius.github.io/likelihood.model.series.md

License: GNU General Public License v3.0

R 100.00%

likelihood.model.series.md's Introduction

  • I’m Alex Towell and I can be reached at [email protected].
  • I have two masters degrees from SIUE: Computer Science and Mathematics/Statistics.
  • I’m interested encrypted search and homomorphic encryption, oblivious and probabilitistic data structures and algorithms, machine learning and statistics, AI, and programming.
  • I’m looking to collaborate on papers (some partially complete). Here are some ideas, but I'm open to other opportunities:
    • Oblivious, privacy-preserving algebraic data types for confidential computation on untrusted systems, with analysis informed by information and probability theory. The data types are algebraic in nature because I have been researching ways to compose them to facilitate building larger oblivious programs from smaller oblivious components, the essence of programming.
    • Probabilistic algorithms and probabilistic algebraic data types primarily concerned with specifying a type of approximation error (normally due to rate distortion) which I tentatively refer to as the Bernoulli Model.
      • Probabilistic data structures that model set-indicator functions, like the Bloom filter, are a well-known special case, but I seek to significantly generalize the results and propagate information about the approximation error through a family of monadic constructions.
      • I have been pursuing derivations of the expected lower-bounds on the space complexity of these approximate Bernoulli types in addition to practical near-optimal data structures that model them.
      • Related to my Computer Science thesis, I have also applied the above results to an approximate Boolean algebra for encrypted search.
    • Reliability engineering and applying statistical inference and learning to predict likely breakdowns (and its causes) of critical systems.
      • It concerns reliability theory and my publication titled "Estimating how confidential encrypted searches are using moving average bootstrap method" concerns reliability engineering.
      • My master's paper "Reliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data" is also related.
    • An information-theoretic model of an optimal adversary (provides a lower-bound on confidientiality in some cases) who, with some probability of success, compromises the confidentiality of an encrypted search system by observing a time series of inputs and outputs.
    • Decentralized "trust machines" (technological solutions to securing trust that does not rely on central authorities), Research on oblivious, privacy-preserving computations is one of the tools in automating trust, but I'm also interested in technologies like Blockchain.

likelihood.model.series.md's People

Contributors

queelius avatar

Stargazers

 avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.