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Ben Moseley is the Carnegie Bosch Assistant Professor of Operations Research in the Tepper School of Business at Carnegie Mellon University (CMU). He is an Assistant Professor of Machine Learning in the School of Computer Science (by courtesy) and member of the Ph.D. program Algorithms, Combinatorics and Optimization (ACO). He received a Ph.D. from the University of Illinois in Computer Science and was advised by Chandra Chekuri.
Moseley's research interests are broadly in operations research, theoretical computer science, and machine learning. He works on the design, analysis, and evaluation of algorithms. He is currently working on scheduling theory, distributed computing, and the theoretical foundations of machine learning.
Moseley's research interests are broadly in operations research, theoretical computer science, and machine learning. He works on the design, analysis, and evaluation of algorithms. He is currently working on scheduling theory, distributed computing, and the theoretical foundations of machine learning.
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AAAI 2024no. 18 (2024): 20649-20656
SIAM Symposium on Simplicity in Algorithmspp.100-106, (2024)
CoRR (2024)
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arXiv (Cornell University) (2023)
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Journal of Schedulingno. 6 (2023): 519-521
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Found. Trends Optim.no. 4 (2023): 340-417
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Dagstuhl Reportsno. 2 (2023): 1-19
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