- I’m Alex Towell and I can be reached at lex@metafunctor.com.
- 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.
Code butcher, cancer survivor, hobby jogger.
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reliability-estimation-in-series-systems
reliability-estimation-in-series-systems PublicReliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data
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elasticsearch-lm
elasticsearch-lm PublicElasticSearch Query Fine-Tuning Training Data for Large Language Models
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wei.series.md.c1.c2.c3
wei.series.md.c1.c2.c3 PublicWeibull series system estimation from data with censored lifetimes and masked component cause of failure.
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