- Associate Professor of Business Adminstration
- Associate Professor of Statistical Science (Secondary)
- Associate Professor of Mathematics (Secondary)
Alessandro Arlotto is an Associate Professor of Business Administration, Mathematics, and Statistical Science at Duke University. Alessandro holds a primary appointment in the Decision Sciences area of Duke University’s Fuqua School of Business and secondary appointments in the departments of Mathematics and Statistical Science. Alessandro received his Ph.D. in 2012 from the University of Pennsylvania and joined Duke University in the same year.
Alessandro’s research interests are in probability, optimization and their applications to business and economics. His research has appeared in several journals including the Annals of Applied Probability, Management Science, Mathematics of Operations Research, Operations Research, and Stochastic Processes and their Applications. Alessandro is a recipient of the Faculty Early Career Development (CAREER) award from the National Science Foundation.
At Duke, Alessandro teaches the core course Probability and Statistics in the Daytime and Executive MBA programs as well as the Quantitative Business Analysis course for the Master in Management Studies. Alessandro also teaches the graduate course Stochastic Models.
CAREER: The effects of centralized and decentralized sequential decisions on system performance awarded by National Science Foundation (Principal Investigator). 2016 to 2021
Conference on Probability Theory and Combinatorial Optimization awarded by National Science Foundation (Principal Investigator). 2015 to 2016
"A Central Limit Theorem for Costs in Bulinskaya’s Inventory Management Problem When Deliveries Face Delays." Methodology and Computing in Applied Probability 20.3 (September 1, 2018): 839-854. Full Text
Arlotto, A, Frazelle, AE, and Wei, Y. "Strategic open routing in service networks (Accepted)." Management Science (2018).
Arlotto, A, and Xie, X. "Logarithmic regret in the dynamic and stochastic knapsack problem." Corr abs/1809.02016 (2018).
"An adaptive O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample." Random Structures and Algorithms 52.1 (January 1, 2018): 41-53. Full Text
Arlotto, A, and Gurvich, I. "Uniformly bounded regret in the multi-secretary problem (Submitted)." (October 20, 2017).
Arlotto, A, and Steele, JM. "A Central Limit Theorem for Temporally Nonhomogenous Markov Chains with Applications to Dynamic Programming." Mathematics of Operations Research 41.4 (November 2016): 1448-1468. Full Text
Arlotto, A, Mossel, E, and Steele, JM. "Quickest online selection of an increasing subsequence of specified size." Random Structures & Algorithms 49.2 (September 2016): 235-252. Full Text
Arlotto, A, and Steele, JM. "Beardwood–Halton–Hammersley theorem for stationary ergodic sequences: A counterexample." The Annals of Applied Probability 26.4 (August 2016): 2141-2168. Full Text
Arlotto, A, Nguyen, VV, and Steele, JM. "Optimal online selection of a monotone subsequence: a central limit theorem." Stochastic Processes and Their Applications 125.9 (September 2015): 3596-3622. Full Text
Arlotto, A, Gans, N, and Steele, JM. "Markov Decision Problems Where Means Bound Variances." Operations Research 62.4 (August 2014): 864-875. Full Text