Scholar
Benjamin Moseley
Google Scholar ID: m6NDX0IAAAAJ
Carnegie Mellon University
Citations & Impact
Citations
2,882
H-index
24
i10-index
59
Publications
20
Co-authors
45
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Won several best paper awards, including 2015 IPDPS Best Paper, 2013 SPAA Best Paper, and 2010 SODA Best Student Paper. His work was recognized with oral presentations at NeurIPS 2021 (top 1% of submissions), NIPS 2017 (top 1.3% of submissions), and spotlight presentations at NeurIPS 2023 and NIPS 2018 (top 3.5% of submissions). Received generous grants, including an NSF CAREER Award, support from NSF CCF and OE, Office of Naval Research, two Google Faculty Research Awards, a Yahoo! ACE Award, Infor faculty award, and a Carnegie-Bosch faculty chair.
Research Experience
- Associate Professor at Tepper School of Business, Carnegie Mellon University, and consulting professor at Relational AI. Also an Associate Professor (by courtesy) in the School of Computer Science and a member of the ACO Ph.D. program. Previously, Assistant Professor at Washington University's Department of Computer Science and Engineering (2014-2017), Research Assistant Professor at Toyota Technological Institute at Chicago (2012-2014), and Visiting Professor at Sandia National Laboratories (2013). Frequently affiliated with Yahoo Labs.
Education
- Ph.D. from the University of Illinois in Computer Science, advised by Chandra Chekuri.
Background
- Research Interests: Operations Research, Theoretical Computer Science, and Machine Learning. Focuses on the design, analysis, and evaluation of algorithms, with current emphasis on the algorithmic foundations of machine learning, distributed algorithm design, and scheduling and logistics algorithms.
Miscellany
- Enjoys inspiring students through teaching, selected as a 'Top 50 Undergraduate Business School Professor' by Poets and Quants. Developed the undergraduate minor in Business Analytics and Optimization at Tepper, and created a range of courses for UBA, MBA, and MSBA on machine learning for business, optimization for business, and the use of probability and statistics in business.
