Why a DIY PHD?

I’ve designed my own course of advanced study for two reasons. The first is that a standard PhD program isn’t an option: fellowships don’t pay enough to support a family. The second is that my ideal course of study winds through multiple domains in a way that’s difficult to capture within a traditional graduate program.

The goal is to research how we parse the world and make knowledge of it, how we organize this knowledge, how we derive insights from it, and how we can collectively and justly make decisions based on what we learn. This includes work in data modeling and ontologies, information management and engineering, inference and analytics, and social choice theory and radical democracy. This also includes critical theoretical work on the pitfalls and power dynamics within these endeavors to make sure I don’t get too myopically, and dangerously, utopian about the ability to achieve world peace through better deliberative methods and information management. And to make sure that I can’t pretend that modeling reality is a value-neutral and apolitical act; that we are not constantly in danger of enshrining our biases in the way we choose to stuff the world into categories.

Why the Rework?

It’s been about a year since I was promoted and decided to delay my original DIY graduate program to side quest a curriculum for my new position, and I’ve been promoted again. Instead of continuing to delay this project I’m reworking it so that there’s a greater degree of overlap with things I need to learn for work anyway, and so that it’s more narrowly scoped to topics and domains that I’m already conversant with. This means I’ll be able to make progress on certain topics during work hours, and it also substantially lessens the overall cognitive load. This should make it a lot easier to actually finish.

The Curriculum

The curriculum below is the foundational part of the project. I will likely expand it based on what most interests me and based on specific research questions I have, but this is the core. The first step is to read the fundamental critical counterweight texts in each section. The second is to get into the extensions.

Knowledge, Modeling, and Abstraction

Fundamentals

• Data and Reality by William Kent
• The What and How of Modeling Information and Knowledge by C. Maria Kent
• The Model Thinker by Scott E. Page

Extensions

• Metadata by Jeffrey Pomerantz
• Semantic Web for the Working Ontologist by Allemang and Hendler
• Categories and Compositionality: with a view to Applications by
• Formal Concept Analysis: Mathematical Foundations by Bernhard Ganter and Rudolf Wille

Critical Counterweight

• Sorting Things Out: Classification and Its Consequences by Susan Leigh Star and Geoffrey C. Bowker
• Data Feminism by Catherine D’Ignazio and Lauren F. Klein
• Power/Knowledge by Michel Foucault
• Seeing Like a State by James C. Scott
• The Ordinal Society by Marion Fourcade and Kieran Healy

Data Infrastructure and Information Management

Fundamentals

• The Discipline of Organizing by Robert J. Glushko
• Data Management: Databases and Data Analytics by Richard T. Watson

Extensions

• Meeting the Challenges of Data Quality Management by Laura Sebastien-Coleman
• Fundamentals of Data Engineering by Joe Reis and Matt Housley
• Data Engineering Design Patterns by Simon Späti
• SQL Antipatterns: Avoiding the Pitfalls of Database Programming by Bill Karwin
• The Data Warehouse Toolkit 3e by Ralph Kimball and Margy Ross
• Statistical Data Cleaning with Applications in R by Mark van der Loo and Edwin de Jonge

Critical Counterweight

• The Algorithmic Foundations of Differential Privacy by Cynthia Dwork and Aaron Roth
• Indigenous Data Sovereignty: Toward an Agenda by Tahu Kukutai and John Taylor

Analysis, Inference, and Data Mining

Fundamentals

• Statistical Rethinking by Richard McElreath
• Probability Theory: The Logic of Science by E.T. Jaynes
• Forecasting: Principles and Practice by Rob J. Hyndman and George Athanasopoulos
• Data Visualization: A Practical Introduction by Kieran Healy

Extensions

• Data Mining: Concepts and Techniques by Jiawei Han, Jian Pei, and Hanghang Tong
• Introduction to Statistical Learning by Trevor Hastie, Rob Tibshirani, Daniela Witten, and Gareth James
• Interpretable Machine Learning by Christopher Molnar
• Causal Inference: What If by Miguel A. Hernán and James M. Robins
• Causal Inference: The Mixtape by Scott Cunningham
• Network Science by Albert-László Barabási

Critical Counterweight

• Weapons of Math Destruction by Cathy O’Neil
• Fairness and Machine Learning: Limitations and Opportunities by Solon Barocas, Moritz Hardt, and Arvind Narayanan

Collective Decision-Making and Radical Democracy

Fundamentals

• Handbook of Computational Social Choice edited by Felix Brandt, Vincent Conitzer, Ulle Endriss, Jérôme Lang, and Ariel D. Procaccia
• Deliberative Democracy and Beyond: Liberals, Critics, Contestations by John S. Dryzek

Extensions

• Group Agency: The Possibility, Design, and Status of Corporate Agents by Christian List and Philip Pettit
• Democratic Reason: Politics, Collective Intelligence, and the Rule of the Many by Hélène Landemore
• Governing the Commons by Elinor Ostrom
• People and Forests: Communities, Institutions, and Governance by Clark C. Gibson, Margaret A. McKean, and Elinor Ostrom
• Understanding Institutions:The Science and Philosophy of Living Together by Francesco Guala

Critical Counterweight

• Agonistics by Chantal Mouffe
• Politics: The Central Texts by Roberto Mangabeira Unger

Written on: April 2, 2026