Benjamin Moseley

Associate Professor, Carnegie Mellon University
Ben Moseley is the Carnegie Bosch Associate Professor of Operations Research in the Tepper School of Business at Carnegie Mellon University (CMU) and is a consulting professor at the start-up Relational AI. Professor 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 the algorithmic foundations of machine learning, big data analysis (e.g. relational in-database algorithms, distributed algorithm design, and streaming), and approximation and online algorithms. Ben Moseley has won several best paper awards including a 2015 IPDPS Best Paper Award, a SPAA 2013 Best Paper Award, and a SODA 2010 Best Student Paper Award. His work has been recognized with an oral presentation at NeurIPS 2021, an oral presentation at NIPS 2017 and a spotlight presentation at NIPS 2018. Moseley's work has been supported by generous grants from the National Science Foundation, Office of Naval Research, Yahoo, Infor, Google, and Bosch.

Program Visits

Logic and Algorithms in Database Theory and AI, Fall 2023, Visiting Scientist
Algorithms and Uncertainty, Fall 2016, Research Fellow
Algorithms, operations research, machine learning, combinatorial optimization