- 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
Arlotto, A., and J. M. Steele. “A central limit theorem for costs in Bulinskaya’s inventory management problem when deliveries face delays.” Methodology and Computing in Applied Probability, 2018. Manual, doi:10.1007/s11009-016-9522-7. Full Text
Arlotto, Alessandro, and Xinchang Xie. “Logarithmic regret in the dynamic and stochastic knapsack problem..” Corr, vol. abs/1809.02016, 2018.
Arlotto, A., et al. “An adaptive O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample.” Random Structures and Algorithms, vol. 52, no. 1, Wiley, Jan. 2018, pp. 41–53. Manual, doi:10.1002/rsa.20728. Full Text
Arlotto, A., et al. “Strategic open routing in service networks.” Management Science, INFORMS, 2018.
Arlotto, A., and I. Gurvich. Uniformly bounded regret in the multi-secretary problem. Oct. 2017.
Arlotto, A., and J. M. Steele. “A central limit theorem for temporally nonhomogenous Markov chains with applications to dynamic programming.” Mathematics of Operations Research, vol. 41, no. 4, Nov. 2016, pp. 1448–68. Manual, doi:10.1287/moor.2016.0784. Full Text
Arlotto, A., et al. “Quickest online selection of an increasing subsequence of specified size.” Random Structures and Algorithms, vol. 49, no. 2, Sept. 2016, pp. 235–52. Scopus, doi:10.1002/rsa.20634. Full Text
Arlotto, A., and J. M. Steele. “Beardwood–Halton–Hammersley theorem for stationary ergodic sequences: a counterexample.” The Annals of Applied Probability, vol. 26, no. 4, Aug. 2016, pp. 2141–68. Manual, doi:10.1214/15-AAP1142. Full Text
Arlotto, A., et al. “Optimal online selection of a monotone subsequence: A central limit theorem.” Stochastic Processes and Their Applications, vol. 125, no. 9, July 2015, pp. 3596–622. Scopus, doi:10.1016/j.spa.2015.03.009. Full Text
Arlotto, A., et al. “Markov decision problems where means bound variances.” Operations Research, vol. 62, no. 4, Aug. 2014, pp. 864–75. Manual, doi:10.1287/opre.2014.1281. Full Text