Alessandro Arlotto, Associate Professor of Business Adminstration and Mathematics
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.
- Contact Info:
|Ph.D.||University of Pennsylvania||2012|
|M.A.||University of Pennsylvania||2009|
|M.S.||University of Turin (Italy)||2007|
|B.S.||University of Turin (Italy)||2004|
- Recent Publications
- Arlotto, A; Xie, X, Logarithmic regret in the dynamic and stochastic knapsack problem with equal rewards,
Stochastic Systems, vol. 10 no. 2
pp. 170-191 [doi] [abs]
- Arlotto, A; Frazelle, AE; Wei, Y, Strategic open routing in service networks,
- Arlotto, A; Gurvich, I, Uniformly bounded regret in the multi-secretary problem
(October, 2017) [abs]
- Arlotto, A; Steele, JM, A central limit theorem for temporally nonhomogenous Markov chains with applications to dynamic programming,
Mathematics of Operations Research, vol. 41 no. 4
pp. 1448-1468, Institute for Operations Research and the Management Sciences (INFORMS) [doi] [abs]
- Arlotto, A; Mossel, E; Steele, JM, Quickest online selection of an increasing subsequence of specified size,
Random Structures & Algorithms, vol. 49 no. 2
pp. 235-252, WILEY [doi] [abs]
- Recent Grant Support
- CAREER: The effects of centralized and decentralized sequential decisions on system performance, National Science Foundation, 1553274, 2016/05-2021/04.