Sally Dong
Sally Dong

Dong is pronounced “D-oh-n.”

I’m a postdoctoral researcher at MIT working in AI safety and evals.

I completed my PhD in theoretical computer science at the University of Washington, advised by Yin Tat Lee and Thomas Rothvoss. Before that, I studied computer science and pure mathematics at the University of Waterloo.

hello@sallydong.me

Publications

Convex optimization with combinatorial characteristics: new algorithms for linear programming, min-cost flow, and other structured problems

PhD thesis, 2024 · PDF

I (aspire to) update the document from time to time, to improve exposition and fix bugs.

Faster min-cost flow and approximate tree decomposition on bounded treewidth graphs

With Guanghao Ye

ESA 2024 · arXiv

The extension complexity of polytopes with bounded integral slack matrices

With Thomas Rothvoss

IPCO 2024 · arXiv

Faster algorithms for separable linear programs

With Gramoz Goranci, Lawrence Li, Sushant Sachdeva, and Guanghao Ye

SODA 2024 · arXiv

Decomposable non-smooth convex optimization with nearly-linear gradient oracle complexity

With Haotian Jiang, Yin Tat Lee, Swati Padmanabhan, and Guanghao Ye

NeurIPS 2022 · arXiv

Nested dissection meets IPMs: planar min-cost flow in nearly-linear time

With Yu Gao, Gramoz Goranci, Yin Tat Lee, Richard Peng, Sushant Sachdeva, and Guanghao Ye

SODA 2022 · arXiv

A nearly-linear time algorithm for linear programs with small treewidth: a multiscale representation of robust central path

With Yin Tat Lee and Guanghao Ye

STOC 2021 · Invited to the SICOMP special issue · arXiv

Computing circle packing representations of planar graphs

With Yin Tat Lee and Kent Quanrud

SODA 2020 · arXiv

About

I grew up in Toronto, Canada. Outside of research, I enjoy reading history and current-affairs non-fiction, being in nature, and I sometimes take film photographs.