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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery

Published in arXiv, 2025

Introduced Simulation-Grounded Neural Networks (SGNNs), a new modeling framework that uses mechanistic simulations to train neural networks, combining the interpretability of scientific theory with the flexibility of deep learning. SGNNs deliver state-of-the-art performance across disciplines—from forecasting epidemics to inferring hidden variables—while offering a novel form of mechanistic interpretability.

Learning From Simulators: A Theory of Simulation-Grounded Learning

Published in arXiv, 2025

The paper presents a foundational theory for Simulation-Grounded Neural Networks (SGNNs), showing that (i) they implement amortized Bayesian inference with a simulation prior and converge toward the Bayes-optimal predictor, and (ii) they can provably recover unobservable scientific quantities (with interpretability tied to the mechanistic simulation) even when empirical methods fail.

Not All Accuracy Is Equal: Prioritizing Diversity in Infectious Disease Forecasting

Published in arXiv, 2025

This paper argues that epidemic forecasting ensembles often underperform because constituent models share correlated errors. It demonstrates that diversity in modeling approaches, not just individual accuracy, is the key driver of ensemble performance, and proposes strategies to design ensembles that maximize complementary information across models.

talks

teaching

Calculus 1

Undergraduate course, University of Michigan, Department of Mathematics, 2023

Instructor of record for calculus 1.