Heather Newman

Heather Newman is an Assistant Professor in the Department of Computer Science at Vassar College. She earned her PhD from the interdisciplinary Algorithms, Combinatorics, and Optimization (ACO) program at Carnegie Mellon University through the Mathematics Department, advised by Ben Moseley. She received her A.B. in Mathematics with a minor in Computer Science from Princeton University in 2019 and an M.Sc. in Mathematical Sciences from Oxford in 2020.

Her research lies at the intersection of discrete mathematics, theoretical computer science, and operations research, focusing on approximation and online algorithms for combinatorial optimization problems. She is particularly interested in clustering, scheduling, and developing new beyond-worst-case models for online algorithms with pessimistic lower bounds.

The recent publications reflect a strong trend in algorithmic design for optimization problems, especially in clustering (e.g., correlation clustering, k-median), scheduling, and graph-based optimization. Her work emphasizes theoretical rigor with practical implications, often involving novel algorithmic frameworks and combinatorial techniques. She frequently publishes in top-tier theoretical venues such as ICALP, APPROX, SPAA, ICML, and SIGMETRICS.

Scientific Awards

• No awards listed in the provided text.

Advising and Grants

There is no explicit mention of graduate students or PhD advisees in the provided information. Heather Newman does not list any specific grants or funded projects, but her research output suggests active engagement in theoretical computer science research. She was involved with Moon Duchin’s Metric Geometry and Gerrymandering Group, which applies mathematical methods to redistricting and social justice issues, indicating interdisciplinary outreach and potential collaborative funding.

Labs and Research Teams

While no formal lab is mentioned, Heather Newman has been affiliated with interdisciplinary research groups, notably the Metric Geometry and Gerrymandering Group led by Moon Duchin. This group brings together mathematicians and computer scientists to analyze gerrymandering through geometric and algorithmic tools. Her primary research collaborations appear to be with leading figures in theoretical computer science, including Ben Moseley, Kirk Pruhs, and Anupam Gupta, suggesting active participation in a broader research network focused on algorithm design and analysis.