Sarah M. Morin

My name is Sarah. I am a CS Researcher and (former) Software Engineer.

Research

My research interests lie (broadly) at the intersection of theory and systems. I believe we can improve the security, reliability, and performance of distributed systems using formal methods. These days, I’m fascinated by formal verification, high-level programming languages, and consensus protocols. Through my work, I hope to bridge the gap between powerful theoretical results and practical designs.

Currently, I am collaborating with Prof. Gabe Parmer (George Washington University) to develop a low-level, systems optimized Datalog variant. The language will enable the declarative specification of low-level systems code and the compiler (built in Rust) will produce efficient, executable implementations.

Background

I earned my B.S. in Computer Science from The George Washington University. During my time as an undergraduate I worked as a Research Assistant for Professor Poorvi Vora and contributed to several publications on risk-limiting audits to statistically verify election outcomes. I also served as a Teaching Assistant in the Computer Science Department and taught for the Computer Architecture, Systems Programming, and Discrete Structures courses.

After graduating from GW, I joined Oracle as a Software Engineer on the File System Storage team of Oracle Cloud Infrastructure (OCI) where I was later promoted to Member of Technical Staff. As a developer on the data-plane I was given the opportunity to tackle many complex problems in distributed storage such as introducing support for SMB protocol and building a user quota enforcement system. I recently left my role at Oracle to return to academia and pursue a PhD in computer science. More on that in this blog post.

I live in San Francisco with my partner, Gus, and our cats, Zuko (yes, as in Avatar: The Last Airbender) and Kiwi. In my free time, I enjoy cooking, knitting, playing pool, and watching hockey. Although I have quite a few hobbies, there is nothing I enjoy more than learning and solving problems.

selected publications

USENIX Security

Minerva– An Efficient Risk-Limiting Ballot Polling Audit

Filip Zagórski, Grant McClearn, Sarah Morin, Neal McBurnett, and Poorvi L. Vora

In 30th USENIX Security Symposium (USENIX Security ’21), Aug 2021

Abs PDF

Evidence-based elections aim to produce trustworthy and compelling evidence of the correctness of election outcomes, enabling the detection of problems with high probability. They require a well-curated voter-verified paper trail, compliance audits, and a rigorous tabulation audit of the election outcome, known as a risk-limiting audit (RLA).

This paper focuses on ballot polling RLAs which can require that a very large sample of ballots be drawn. The main ballot polling RLA in use today, BRAVO, is designed for use when single ballots are drawn at random and a decision regarding whether to stop the audit or draw another ballot is taken after each ballot draw. But in practice, ballot polling audits draw many ballots in a single round before determining whether to stop.

Direct application of BRAVO to large rounds results in considerable inefficiency. We present MINERVA, a risk-limiting audit that addresses this problem. When compared to the BRAVO stopping rule being applied at the end of the round, for a first-round with 90% stopping probability, MINERVA halves the number of ballots required across all state margins in the 2020 US Presidential election. When compared to the BRAVO stopping rule being applied after examination of individual ballots, MINERVA reduces the number of ballots by about a quarter. MINERVA requires that round sizes are predetermined; this does not appear to be a drawback for large first rounds which have been typical choices for election officials.

Ballot-polling audits are the leading option in most states. MINERVA significantly reduces the necessary expense for contests with close margins and thus makes adopting RLAs easier. Wider adoption of RLAs is a critical step in increasing public confidence in elections.

MINERVA was used in Ohio’s pilot RLA of the primaries in May 2020 in Montgomery County. We provide open-source implementations of MINERVA. The code has been integrated as an option in Arlo, the most widely-used RLA software.
VOTING 20

A Note on Risk-Limiting Bayesian Polling Audits for Two-Candidate Elections

Sarah Morin, Grant McClearn, Neal McBurnett, Poorvi L. Vora, and Filip Zagórski

In Fifth Workshop on Advances in Secure Electronic Voting, Financial Cryptography, 2020

Abs PDF

This short paper provides a general form for a polling audit that is both Bayesian and risk-limiting: the Bayesian Risk-Limiting (Polling) Audit, which enables the use of a Bayesian approach to explore more efficient Risk-Limiting Audits. A numerical example illustrates the implications to practice.