About

I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. Before Stanford, I worked with John Lafferty at the University of Chicago. Prior to that, I received an MPhil in Scientific Computing at the University of Cambridge on a Churchill Scholarship where I was advised by Sergio Bacallado. I received a B.S. in Mathematics and B.A. in Chemistry at the University of Chicago.

Summer 2022: I am currently a research scientist intern at DeepMind in London.

Publications and Preprints

Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. COLT, 2022. Best Paper Award.
arXiv | conference pdf (alphabetical authorship)

Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan, Big-Step-Little-Step: Gradient Methods for Objectives with Multiple Scales. COLT, 2022.
arXiv | code | conference pdf (alphabetical authorship)

Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. AISTATS, 2021. Selected for oral presentation.
arXiv | conference pdf

Annie Marsden, Sergio Bacallado. Sequential Matrix Completion. 2017. (arXiv pre-print)
arXiv | pdf

Annie Marsden, R. Stephen Berry. Enrichment of Network Diagrams for Potential Surfaces. The Journal of Physical Chemsitry, 2015.
pdf

Annie Marsden. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. 2013.
pdf

Theses and Reports

Fourier Transformation at a Representation, Annie Marsden. Etude for the Park City Math Institute Undergraduate Summer School. July 2015.
pdf

Szemer├ędi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Done under the mentorship of M. Malliaris.
pdf

Sequential Matrix Completion. Annie Marsden. University of Cambridge MPhil. Thesis, 2016.
pdf

Teaching

Navajo Math Circles Instructor. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here!

Winter 2020 Teaching assistant for EE364a: Convex Optimization I taught by John Duchi

Fall 2018 Teaching assitant for CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019 taught by Greg Valiant

Talks

Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. AISTATS, 2021.