About

Alison Bernstein joined the Department of Pharmacology and Toxicology at the Ernest Mario School of Pharmacy of Rutgers University in 2022. Prior to this, she spent six years at Michigan State University's College of Human Medicine in the Department of Translational Neuroscience. She earned a BA in Biological Basis of Behavior and the History and Sociology of Science at the University of Pennsylvania and a PhD in Biological and Biomedical Sciences, specializing in Molecular Genetics and Genomics, at Washington University in St. Louis. Her postdoctoral training was conducted at Emory University in the Rollins School of Public Health and the School of Medicine. Her research focuses on the intersection of toxicology, neuroscience, and genetics, driven by a long-standing interest in how genes and environment impact the brain. Outside of her research, she is the co-founder of SciMoms, a science communication non-profit, which gained recognition during the COVID pandemic in Time magazine. She enjoys cooking and baking, doing craft projects with her kids, playing board games, reading sci-fi and fantasy novels, and playing tennis.

Research topics

• Computer Science
• Combinatorics
• Mathematics
• Discrete mathematics
• Algorithm

Selected publications

• Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary

  arXiv (Cornell University) · 2020 · 33 citations

  1st authorCorresponding
  • Computer Science
  • Combinatorics
  • Mathematics

  Designing dynamic graph algorithms against an adaptive adversary is a major goal in the field of dynamic graph algorithms. While a few such algorithms are known for spanning trees, matchings, and single-source shortest paths, very little was known for an important primitive like graph sparsifiers. The challenge is how to approximately preserve so much information about the graph (e.g., all-pairs distances and all cuts) without revealing the algorithms' underlying randomness to the adaptive adversary. In this paper we present the first non-trivial efficient adaptive algorithms for maintaining spanners and cut sparisifers. These algorithms in turn imply improvements over existing algorithms for other problems. Our first algorithm maintains a polylog$(n)$-spanner of size $\tilde O(n)$ in polylog$(n)$ amortized update time. The second algorithm maintains an $O(k)$-approximate cut sparsifier of size $\tilde O(n)$ in $\tilde O(n^{1/k})$ amortized update time, for any $k\ge1$, which is polylog$(n)$ time when $k=\log(n)$. The third algorithm maintains a polylog$(n)$-approximate spectral sparsifier in polylog$(n)$ amortized update time. The amortized update time of both algorithms can be made worst-case by paying some sub-polynomial factors. Prior to our result, there were near-optimal algorithms against oblivious adversaries (e.g. Baswana et al. [TALG'12] and Abraham et al. [FOCS'16]), but the only non-trivial adaptive dynamic algorithm requires $O(n)$ amortized update time to maintain $3$- and $5$-spanner of size $O(n^{1+1/2})$ and $O(n^{1+1/3})$, respectively [Ausiello et al. ESA'05]. Our results are based on two novel techniques. The first technique, is a generic black-box reduction that allows us to assume that the graph undergoes only edge deletions and, more importantly, remains an expander with almost-uniform degree. The second technique we call proactive resampling. [...]

  DOI

Recent grants

• CAREER: Sublinear Graph Algorithms: New Insights for Foundational Problems

  NSF · $552k · 2020–2025

Frequent coauthors

• Sepehr Assadi

  Rutgers, The State University of New Jersey

  22 shared

• Clifford Stein

  Columbia University

  22 shared

• Vahab Mirrokni

  17 shared

• MohammadHossein Bateni

  16 shared

• Maximilian Probst Gutenberg

  ETH Zurich

  12 shared

• Eva Rotenberg

  Technical University of Denmark

  11 shared

• Jacob Holm

  University of Copenhagen

  11 shared

• Sebastian Forster

  University of Salzburg

  8 shared

Labs

• Bernstein LabPI

Education

• PhD, Biology and Biomedical Sciences

  Washington University in Saint Louis

  2009

• BA Biological Basis of Behavior

  University of Pennsylvania

  2000

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