Academic Profile

About Me

I am currently a Ph.D. candidate at UCSD working primarily in the philosophy of science and epistemology. My research is focused on the development of machine learning (ML) systems and their impact on the sciences. In one branch of my work, I explore how scientific communities interact with algorithmic tools, and how these tools affect the dynamics of knowledge production. The other branch focuses on how algorithmic tools bear on traditional problems in the philosophy of science, such as scientific representation, confirmation, and modeling. Much of my work is interdisciplinary, drawing on methods and insights from computer science, statistics, and network science, aiming to advocate for modeling and simulation as philosophical methods. Prior to philosophy, I did research in computational geography, training deep neural networks (DNNs) to provide insights into environmental and social impacts on communities.

Research Interests

Philosophy of Science
Formal and Social Epistemology
Agent-Based Modeling and Network Science
Probability and the Metaphysics of Science

Selected Publications

Navigating Epistemic Monocultures in AI-Driven Science: A Simulation Study

Fazelpour, Sina; O'Brien, Joseph; Rubin, Hannah (2026)

Philosophy of Science

Journal Site

A Philosophical Introduction to In-Silico Clinical Trials

O'Brien, Joseph; Masotti, Joseph; Cartwright, Nancy (2026)

European Journal for Philosophy of Science

Journal Site Preprint

A multi-glimpse deep learning architecture to estimate socioeconomic census metrics in the context of extreme scope variance

Runfola, Dan; Stefanidis, Anthony; Lv, Zhonghui; O'Brien, Joseph; Baier, Heather (2024)

International Journal of Geographical Information Science

Journal Site

Works in Progress

Statistical Practice in the Sciences Under Review

This work explores current statistical practice and its relations to traditional debates in philosophy of science.

Theoretical Virtues and Modern Machine Learning Draft in Progress

I argue that while many machine learning models appear to be massively complex at first glance, our optimization methods are implicitly committed to selecting relatively simplistic models in a massive parameter space.

The Epistemic Status of Synthetic Data Draft in Progress

This paper explores the increasing role that synthetic data plays across the computational sciences, and offers guidance as to how to think about synthetic data's epistemic deliverances.

Teaching Experience

Primary Instructor

PHIL 51: Climate Ethics - Summer 26

Teaching Assistant

PHIL 14: Intro to the Nature of Reality - Fall 24
PHIL 120: Symbolic Logic - Fall 24
PHIL 10: Introduction to Logic - Winter 25
PHIL 174: Data Ethics - Spring 25
PHIL 28: Ethics in Society II - Summer 25
CAT Writing Program - AY 25-26

Curriculum Vitae

Download my complete CV for information about my academic background, research experience, publications, and outreach.

Download CV (PDF)

Contact Information

Email: j3obrien[at]ucsd[dot]edu