Published in arXiv preprint arXiv:2606.29533, 2026
In this paper, we investigate the problem of forecasting for downstream agents with the goal of guaranteeing low swap regret. We improve upon existing results, achieving both enhanced performance guarantees and a computationally efficient algorithm. Furthermore, we provide results that generalize to high-dimensional settings.
Published in Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2025
In this paper, we study the trade-off between envy and inefficiency in repeated resource allocation settings with stochastic replenishments, motivated by real-world systems such as food banks and medical supply chains.
Published in Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2025
In this paper, we investigate equilibrium outcomes under the Dynamic max-min fair allocation mechanism.
Published in Big Data and Cognitive Computing, 2022
In this paper, we improve on the SemNet, a tool for analyzing simitarity in information networks, using techniques from randomizes algorithms to improve its efficacy and efficiency.
Published in arXiv preprint arXiv:2511.04817, 2025
In this paper, we investigate the problem of allocating a public good in an online setting with strategic agents and give a mechanism that achieves strong fairness and efficiency guarantees at equilibrium.
Published in arXiv preprint arXiv:2101.04714, 2021
In this paper, we characterize the asympotic distribution of various staticstics on plane trees drawn for the Nearest Neighbor Thermodynamic Model, a model describing the tsecondary structure of RNA sequences.