Ben Moseley
· Associate Professor of Operations ResearchVerifiedCarnegie Mellon University · Economics
Active 2008–2026
About
Ben Moseley is an Associate Professor of Operations Research at the Tepper School of Business. His profile is listed among the faculty and research sections of Carnegie Mellon University, indicating his role in academic and research activities within the institution. The page highlights his affiliation with the Tepper School of Business, but does not provide specific details about his research focus, background, or key contributions.
Research topics
- Computer Science
- Artificial Intelligence
- Data Mining
- Mathematics
- Machine Learning
- Statistics
Selected publications
arXiv (Cornell University) · 2026-04-04
articleOpen accessWe study algorithmic matroid intersection coloring. Given $k$ matroids on a common ground set $U$ of $n$ elements, the goal is to partition $U$ into the fewest number of color classes, where each color class is independent in all matroids. It is known that $2χ_{\max}$ colors suffice to color the intersection of two matroids, $(2k-1)χ_{\max}$ colors suffice for general $k$, where $χ_{\max}$ is the maximum chromatic number of the individual matroids. However, these results are non-constructive, leveraging techniques such as topological Hall's theorem and Sperner's Lemma. We provide the first polynomial-time algorithms to color two or more general matroids where the approximation ratio depends only on $k$ and, in particular, is independent of $n$. For two matroids, we constructively match the $2χ_{\max}$ existential bound, yielding a 2-approximation for the Matroid Intersection Coloring problem. For $k$ matroids we achieve a $(k^2-k)χ_{\max}$ coloring, which is the first $O(1)$-approximation for constant $k$. Our approach introduces a novel matroidal structure we call a \emph{flexible decomposition}. We use this to formally reduce general matroid intersection coloring to graph coloring while avoiding the limitations of partition reduction techniques, and without relying on non-constructive topological machinery. Furthermore, we give a \emph{fully polynomial randomized approximation scheme} (FPRAS) for coloring the intersection of two matroids when $χ_{\max}$ is large. This yields the first polynomial-time constructive algorithm for an asymptotic variant of Rota's Basis Conjecture. This constructivizes Montgomery and Sauermann's recent asymptotic breakthrough and generalizes it to arbitrary matroids.
Competitive Online Transportation Simplified
Society for Industrial and Applied Mathematics eBooks · 2026-01-01
book-chapterThe setting for the online transportation problem is a metric space \(M\), populated by \(m\) parking garages of varying capacities. Over time cars arrive in \(M\), and must be irrevocably assigned to a parking garage upon arrival in a way that respects the garage capacities. The objective is to minimize the aggregate distance traveled by the cars. In 1998, Kalyanasundaram and Pruhs conjectured that there is a \((2m - 1)\)-competitive deterministic algorithm for the online transportation problem, matching the optimal competitive ratio for the simpler online metric matching problem. Recently, Harada and Itoh presented the first \(O(m)\)-competitive deterministic algorithm for the online transportation problem. Our contribution is an alternative algorithm design and analysis that we believe is simpler.
arXiv (Cornell University) · 2026-04-04
preprintOpen accessWe study algorithmic matroid intersection coloring. Given $k$ matroids on a common ground set $U$ of $n$ elements, the goal is to partition $U$ into the fewest number of color classes, where each color class is independent in all matroids. It is known that $2χ_{\max}$ colors suffice to color the intersection of two matroids, $(2k-1)χ_{\max}$ colors suffice for general $k$, where $χ_{\max}$ is the maximum chromatic number of the individual matroids. However, these results are non-constructive, leveraging techniques such as topological Hall's theorem and Sperner's Lemma. We provide the first polynomial-time algorithms to color two or more general matroids where the approximation ratio depends only on $k$ and, in particular, is independent of $n$. For two matroids, we constructively match the $2χ_{\max}$ existential bound, yielding a 2-approximation for the Matroid Intersection Coloring problem. For $k$ matroids we achieve a $(k^2-k)χ_{\max}$ coloring, which is the first $O(1)$-approximation for constant $k$. Our approach introduces a novel matroidal structure we call a \emph{flexible decomposition}. We use this to formally reduce general matroid intersection coloring to graph coloring while avoiding the limitations of partition reduction techniques, and without relying on non-constructive topological machinery. Furthermore, we give a \emph{fully polynomial randomized approximation scheme} (FPRAS) for coloring the intersection of two matroids when $χ_{\max}$ is large. This yields the first polynomial-time constructive algorithm for an asymptotic variant of Rota's Basis Conjecture. This constructivizes Montgomery and Sauermann's recent asymptotic breakthrough and generalizes it to arbitrary matroids.
Proceedings of the ACM on Management of Data · 2025-06-09 · 1 citations
articleCardinality estimation and conjunctive query evaluation are two of the most fundamental problems in database query processing. Recent work proposed, studied, and implemented a robust and practical information-theoretic cardinality estimation framework. In this framework, the estimator is the cardinality upper bound of a conjunctive query subject to ''degree-constraints'', which model a rich set of input data statistics. For general degree constraints, computing this bound is computationally hard. Researchers have naturally sought efficiently computable relaxed upper bounds that are as tight as possible. The polymatroid bound is the tightest among those relaxed upper bounds. While it is an open question whether the polymatroid bound can be computed in polynomial-time in general, it is known to be computable in polynomial-time for some classes of degree constraints. Our focus is on a common class of degree constraints called simple degree constraints. Researchers had not previously determined how to compute the polymatroid bound in polynomial time for this class of constraints. Our first main result is a polynomial time algorithm to compute the polymatroid bound given simple degree constraints. Our second main result is a polynomial-time algorithm to compute a ''proof sequence'' establishing this bound. This proof sequence can then be incorporated in the PANDA-framework to give a faster algorithm to evaluate a conjunctive query. In addition, we show computational limitations to extending our results to broader classes of degree constraints. Finally, our technique leads naturally to a new relaxed upper bound called the flow bound, which is computationally tractable.
Robust Gittins for Stochastic Scheduling
ACM SIGMETRICS Performance Evaluation Review · 2025-06-16 · 1 citations
article1st authorCorrespondingA common theme in stochastic optimization problems is that, theoretically, stochastic algorithms need to "know" relatively rich information about the underlying distributions. This is at odds with most applications, where distributions are rough predictions based on historical data. Thus, commonly, stochastic algorithms are making decisions using imperfect predicted distributions, while trying to optimize over some unknown true distributions. We consider the fundamental problem of scheduling stochastic jobs preemptively on a single machine to minimize expected mean completion time in the setting where the scheduler is only given imperfect predicted job size distributions. If the predicted distributions are perfect, then it is known that this problem can be solved optimally by the Gittins index policy. The goal of our work is to design a scheduling policy that is robust in the sense that it produces nearly optimal schedules even if there are modest discrepancies between the predicted distributions and the underlying real distributions. Our main contributions are: - We show that the standard Gittins index policy is not robust in this sense. If the true distributions are perturbed by even an arbitrarily small amount, then running the Gittins index policy using the perturbed distributions can lead to an unbounded increase in mean completion time. - We explain how to modify the Gittins index policy to make it robust, that is, to produce nearly optimal schedules, where the approximation depends on a new measure of error between the true and predicted distributions that we define. Looking forward, the approach we develop here can be applied more broadly to many other stochastic optimization problems to better understand the impact of mispredictions, and lead to the development of new algorithms that are robust against such mispredictions.
Managing High-Bandwidth Memory is a Parallel Scheduling Problem (full paper only)
2025-07-16
articleOpen accessHigh-Bandwidth Memory (HBM) is a decade-old memory technology that is increasingly commonly being used in highly-parallel machines such as GPUs and multicores. Comparatively, HBM has higher bandwidth, smaller capacity, and similar latency to other DRAM technologies. Many systems use both HBM and other DRAM technologies, where HBM is naturally closer to the processor in the conceptual memory hierarchy. Thus, a natural resulting question is how one should best manage a collection of processes running on a HBM/DRAM memory hierarchy. Prior work introduced a theoretical model for addressing this question, and gave a competitive policy for the objective of minimizing makespan. Our main technical contribution is to give a competitive policy for the more commonly appropriate total/average response/completion time objective. However, we believe the broader, and more important contribution, is to make explicit the case (hinted at in the prior literature) that managing an HBM/DRAM hierarchy should be thought of as a parallel scheduling problem. To that end, we introduce a new online scheduling model that we call the semi-normal model. We then show how to use a competitive algorithm for scheduling in the semi-normal model as a black box to obtain a competitive algorithm for managing a HBM/DRAM memory hierarchy. Thus, as a result of this black-box conversion, competitiveness results in the semi-normal model translate (essentially) automatically into competitiveness results in the HBM/DRAM management model. Our main technical result is then an application of such a translation. That is, we show that a natural variant of the Round Robin (processor sharing) algorithm, naturally adapted for the seminormal model, is competitive for the objective of average/total completion time. Thus, we obtain an algorithm for managing a HBM/DRAM hierarchy that is competitive for the objective of average/total completion time, using this black-box reduction.
Robust Gittins for Stochastic Scheduling
ArXiv.org · 2025-04-14 · 1 citations
preprintOpen access1st authorCorrespondingA common theme in stochastic optimization problems is that, theoretically, stochastic algorithms need to "know" relatively rich information about the underlying distributions. This is at odds with most applications, where distributions are rough predictions based on historical data. Thus, commonly, stochastic algorithms are making decisions using imperfect predicted distributions, while trying to optimize over some unknown true distributions. We consider the fundamental problem of scheduling stochastic jobs preemptively on a single machine to minimize expected mean completion time in the setting where the scheduler is only given imperfect predicted job size distributions. If the predicted distributions are perfect, then it is known that this problem can be solved optimally by the Gittins index policy. The goal of our work is to design a scheduling policy that is robust in the sense that it produces nearly optimal schedules even if there are modest discrepancies between the predicted distributions and the underlying real distributions. Our main contributions are: (1) We show that the standard Gittins index policy is not robust in this sense. If the true distributions are perturbed by even an arbitrarily small amount, then running the Gittins index policy using the perturbed distributions can lead to an unbounded increase in mean completion time. (2) We explain how to modify the Gittins index policy to make it robust, that is, to produce nearly optimal schedules, where the approximation depends on a new measure of error between the true and predicted distributions that we define. Looking forward, the approach we develop here can be applied more broadly to many other stochastic optimization problems to better understand the impact of mispredictions, and lead to the development of new algorithms that are robust against such mispredictions.
The Nonstationary Newsvendor with (and Without) Predictions
Manufacturing & Service Operations Management · 2025-03-04 · 2 citations
articleProblem definition: The classic newsvendor model yields an optimal decision for a “newsvendor” selecting a quantity of inventory under the assumption that the demand is drawn from a known distribution. Motivated by applications such as cloud provisioning and staffing, we consider a setting in which newsvendor-type decisions must be made sequentially in the face of demand drawn from a stochastic process that is both unknown and nonstationary. All prior work on this problem either (a) assumes that the level of nonstationarity is known or (b) imposes additional statistical assumptions that enable accurate predictions of the unknown demand. Our research tackles the Nonstationary Newsvendor without these assumptions both with and without predictions. Methodology/results: In the setting without predictions, we first design a policy that we prove (via matching upper and lower bounds) achieves order-optimal regret; ours is the first policy to accomplish this without being given the level of nonstationarity of the underlying demand. We then, for the first time, introduce a model for generic (i.e., with no statistical assumptions) predictions with arbitrary accuracy and propose a policy that incorporates these predictions without being given their accuracy. We upper bound the regret of this policy and show that it matches the best achievable regret had the accuracy of the predictions been known. Managerial implications: Our findings provide valuable insights on inventory management. Managers can make more informed and effective decisions in dynamic environments, reducing costs and enhancing service levels despite uncertain demand patterns. This study advances understanding of sequential decision-making under uncertainty, offering robust methodologies for practical applications with nonstationary demand. We empirically validate our new policy with experiments based on three real-world data sets containing thousands of time-series, showing that it succeeds in closing approximately 74% of the gap between the best approaches based on nonstationarity and predictions alone. History: This paper was selected as part of the 1RR initiative between the M&SOM Journal and the MSOM Society. This particular paper was part of the 2024 MSOM Service Operations SIG Conference. Funding: L. An and B. Moseley were supported in part by a Google Research Award, an Infor Research Award, a Carnegie Bosch Junior Faculty Chair, NSF [Grants CCF-2121744 and CCF-1845146] and ONR [Grant N000142212702]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1168 .
Competitive Online Transportation Simplified
ArXiv.org · 2025-08-11
preprintOpen accessThe setting for the online transportation problem is a metric space $M$, populated by $m$ parking garages of varying capacities. Over time cars arrive in $M$, and must be irrevocably assigned to a parking garage upon arrival in a way that respects the garage capacities. The objective is to minimize the aggregate distance traveled by the cars. In 1998, Kalyanasundaram and Pruhs conjectured that there is a $(2m-1)$-competitive deterministic algorithm for the online transportation problem, matching the optimal competitive ratio for the simpler online metric matching problem. Recently, Harada and Itoh presented the first $O(m)$-competitive deterministic algorithm for the online transportation problem. Our contribution is an alternative algorithm design and analysis that we believe is simpler.
Incremental Approximate Single-Source Shortest Paths with Predictions
ArXiv.org · 2025-02-12
preprintOpen accessThe algorithms-with-predictions framework has been used extensively to develop online algorithms with improved beyond-worst-case competitive ratios. Recently, there is growing interest in leveraging predictions for designing data structures with improved beyond-worst-case running times. In this paper, we study the fundamental data structure problem of maintaining approximate shortest paths in incremental graphs in the algorithms-with-predictions model. Given a sequence $σ$ of edges that are inserted one at a time, the goal is to maintain approximate shortest paths from the source to each vertex in the graph at each time step. Before any edges arrive, the data structure is given a prediction of the online edge sequence $\hatσ$ which is used to ``warm start'' its state. As our main result, we design a learned algorithm that maintains $(1+ε)$-approximate single-source shortest paths, which runs in $\tilde{O}(m η\log W/ε)$ time, where $W$ is the weight of the heaviest edge and $η$ is the prediction error. We show these techniques immediately extend to the all-pairs shortest-path setting as well. Our algorithms are consistent (performing nearly as fast as the offline algorithm) when predictions are nearly perfect, have a smooth degradation in performance with respect to the prediction error and, in the worst case, match the best offline algorithm up to logarithmic factors. As a building block, we study the offline incremental approximate single-source shortest-paths problem. In this problem, the edge sequence $σ$ is known a priori and the goal is to efficiently return the length of the shortest paths in the intermediate graph $G_t$ consisting of the first $t$ edges, for all $t$. Note that the offline incremental problem is defined in the worst-case setting (without predictions) and is of independent interest.
Recent grants
CAREER: Pushing the Theoretical Limits of Scalable Distributed Algorithms
NSF · $500k · 2019–2025
NSF · $250k · 2018–2022
NSF · $114k · 2018–2019
NSF · $250k · 2017–2018
NSF · $253k · 2016–2018
Frequent coauthors
- 138 shared
Sungjin Im
University of California, Merced
- 73 shared
Kirk Pruhs
University of Pittsburgh
- 31 shared
Alireza Samadian
- 28 shared
Mahmoud Abo Khamis
- 25 shared
Sergei Vassilvitskii
- 25 shared
Kyle Fox
The University of Texas at Dallas
- 19 shared
Denis Trystram
Université Grenoble Alpes
- 15 shared
Thomas Lavastida
The University of Texas at Dallas
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