queelius,Alex Towell,queelius,github

👋 Hi, I’m Alex Towell and I can be reached at [email protected].
I have two masters from SIUE: Computer Science and Mathematics.
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 am also interested in technologies like Blockchain.

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