Published Pages

a selected portfolio

[May 2024]
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Curriculum Vitae.
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my long-form resume

tags: dustin lennon, applied scientist, statistician

[February 2021]
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Personalized Point Processes: A Simple Bayesian Analysis.
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This post describes a homogeneous Poisson process using a Gamma conjugate prior that can be used to estimate a pooled, per-subject intensity given a collection of realizations.

tags: point process, bayesian, multiple realizations

[February 2021]
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Doubly and Randomly Censored Data.
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A derivation of the density functions and likelihood expression associated with doubly and randomly censored data.

tags: censored data, likelihood, distribution, derivation, event data

[September 2018]
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Probability Integral Transform, A Proof.
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This post elucidates the connection between the generalized inverse, the cdf, the quantile function, and the uniform distribution.

tags: statistics, theory, probability integral transform, proof

[September 2016]
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Adaptive Rejection Sampling.
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This post describes and implements an adaptive rejection sampler for log-concave densities.

tags: adaptive sampling, rejection sampling, logconcave density, distribution

[November 2012]
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Probabilistic Performance Guarantees for Oversubscribed Resources.
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This paper constructs a model for shared resource utilization, determines stochastic bounds for resource exhaustion, and simulates results.

tags: simulation, stochastic resource allocation

[December 2023]
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Command Line QR Codes.
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A simple python script to generate a QR code from a URL

[April 2021]
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Numerical Linear Algebra: Householder a'la Golub and Van Loan.
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Exploring properties of Householder transformations.

tags: numerical linear algebra, householder

[October 2019]
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An Introduction to pytrope.psycopg2_extras.
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This notebook serves as an introduction to the pytrope.psycopg2_extras package.

tags: python, matplotlib, pytrope, helper methods

[October 2019]
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An Introduction to pytrope.matplotlib_extras.
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This notebook serves as an introduction to the pytrope.matplotlib_extras package.

tags: python, matplotlib, pytrope, helper methods

[December 2020]
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Cash Flow Considerations for Machine Learning.
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A recent job talk

tags: cash flow, loans, survival analysis, logrank statistic, machine learning, lending club

[September 2019]
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[GH07] Figure 3.1: Mothers' education and children's test scores.
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This notebook replicates results in GH07, section 3.1, using (a) the statsmodels package and (b) the pystan package.

tags: statsmodels, pystan, linear regression, bayesian analysis, non-informative priors

[October 2015]
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Uber Interview Challenge.
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My take on an Uber data-science assignment.

tags: uber, pre-interview, homework

[October 2013]
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Optimal Lending Club Portfolios.
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The paper characterizes an optimal, managed portfolio with annual returns in excess of 12%.

tags: lending club, optimal portfolios, survival random forest, machine learning

[August 2019]
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Unlocking TrueType Fonts: Fontforge, Matplotlib, and Partially Ordered Occlusions.
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This post shows how to merge ttf files and manipulate truetype glyphs.

tags: python, fontforge, matplotlib, shapely

[August 2019]
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Published Pages Cheatsheet.
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This page catalogs the features available on the Published Pages framework.

tags: publishing, framework, feature overview

[July 2019]
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Recommendation Systems, A Mathematical Overview.
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This post provides an overview of recommendation systems and algorithms.

tags: machine learning, recommendation systems, algorithms

[July 2019]
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Recommending a Data Warehouse.
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This webpage gives an overview of Gartner's 2019 'critical capabilities' analysis and showcases a webtool that provides personalized recommendations.

tags: data warehouse, planning, personalization, gartner

[August 2015]
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Namecheap, Dynamic IP Addresses, and Hosting Multiple Sites.
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This post shows how to augment Namecheap's ddclient script to support multiple hosts on a dynamic IP.

tags: namecheap, ddclient, dynamic ip address, multiple sites

[March 2021]
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Annotations: Elements of Statistical Learning.
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My margin notes from reading Hastie, Tibshirani, and Friedman

tags: notes, elements of statistical learning, machine learning