publications

2023

1. 

   Rank-heterogeneous preference models for school choice

   Amel Awadelkarim, Arjun Seshadri, Itai Ashlagi, and 2 more authors

   In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2023

   Abs arXiv Code

   School choice mechanism designers use discrete choice models to understand and predict families’ preferences. The most widely-used choice model, the multinomial logit (MNL), is linear in school and/or household attributes. While the model is simple and interpretable, it assumes the ranked preference lists arise from a choice process that is uniform throughout the ranking, from top to bottom. In this work, we introduce two strategies for rank-heterogeneous choice modeling tailored for school choice. First, we adapt a context-dependent random utility model (CDM), considering down-rank choices as occurring in the context of earlier up-rank choices. Second, we consider stratifying the choice modeling by rank, regularizing rank-adjacent models towards one another when appropriate. Using data on household preferences from the San Francisco Unified School District (SFUSD) across multiple years, we show that the contextual models considerably improve our out-of-sample evaluation metrics across all rank positions over the non-contextual models in the literature. Meanwhile, stratifying the model by rank can yield more accurate first-choice predictions while down-rank predictions are relatively unimproved. These models provide performance upgrades that school choice researchers can adopt to improve predictions and counterfactual analyses.

2020

1. 

   Prioritized restreaming algorithms for balanced graph partitioning

   Amel Awadelkarim, and Johan Ugander

   In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020

   Abs arXiv Code

   Balanced graph partitioning is a critical step for many large-scale distributed computations with relational data. As graph datasets have grown in size and density, a range of highly-scalable balanced partitioning algorithms have appeared to meet varied demands across different domains. As the starting point for the present work, we observe that two recently introduced families of iterative partitioners—those based on restreaming and those based on balanced label propagation (including Facebook’s Social Hash Partitioner)—can be viewed through a common modular framework of design decisions. With the help of this modular perspective, we find that a key combination of design decisions leads to a novel family of algorithms with notably better empirical performance than any existing highly-scalable algorithm on a broad range of real-world graphs. The resulting prioritized restreaming algorithms employ a constraint management strategy based on multiplicative weights, borrowed from the restreaming literature, while adopting notions of priority from balanced label propagation to optimize the ordering of the streaming process. Our experimental results consider a range of stream orders, where a dynamic ordering based on what we call ambivalence is broadly the most performative in terms of the cut quality of the resulting balanced partitions, with a static ordering based on degree being nearly as good.

2017

1. 

   Finite-element implementation and verification of complex fluid models based on evolving natural configurations, motivated by studies of blood

   Amel Awadelkarim

   PSU, Engineering Science and Mechanics, 2017