Published in ACM SIGKDD, 2022
We develop a framework for analyzing patterns of a disease or pandemic such as Covid using a new method of spatio-temporal decomposition called diffusion NMF (D-NMF).
Authors: Kevin Quinn, Evimaria Terzi, and Mark Crovella
2025
Introduces partial interpretable clustering problem and algorithm, forgoing the requirement that every data point must be explained by the interpretable clustering model.
Authors: Kevin Quinn, Evimaria Terzi, Heikki Mannila
2024
In this paper we study fairness for multi-winner voting rules in a metric setting, focusing our attention on a novel definition we call group inefficiency to evaluate representation for select groups of voters.
Authors: Kevin Quinn, Moon Duchin
2023
A clustering methodology for studying geographic waves of infection evolving over time.
Authors: Kevin Quinn, Evimaria Terzi, Mark Crovella
