**Alessandro Arlotto**

Associate Professor of Business Administration, Mathematics, and Statistical Science

Decision Sciences, The Fuqua School of Business

Department of Mathematics (secondary appointment)

Department of Statistical Science (secondary appointment)

Email: alessandro.arlotto@duke.edu

**New: **

- WORDS 2018: Workshop in Operations Research and Data Science (December 1, 2018 at Duke University) (webpage)
- New graduate course: BA915/MATH742/STA715, Stochastic Models
**(the course begins on January 23!)**(syllabus)

**Education:**

Ph.D., University of Pennsylvania, 2012

A.M., University of Pennsylvania, 2009

M.S., Università degli Studi di Torino (Italy), 2007

B.S., Università degli Studi di Torino (Italy), 2004

**Research Interests:**

- Applied probability
- Stochastic modeling
- Stochastic dynamic programming
- Combinatorial optimization
- Applications to management sciences and economics

**Grants Funded: **

- National Science Foundation, CAREER Award, The effects of centralized and decentralized sequential decisions on system performance. (nsf)
- National Science Foundation, Conference on probability theory and combinatorial optimization. (nsf)

**Submitted Papers: **

- Arlotto, A. and Xie, X. (2018) Logarithmic regret in the dynamic and stochastic knapsack problem,
*under review*. (pdf) (arXiv) - Arlotto, A. and Gurvich, I. (2018) Uniformly bounded regret in the multi-secretary problem,
*under review*. (pdf) (arXiv)

**Published Papers:**

- Arlotto, A., Frazelle, A. E., and Wei, Y. (2018) Strategic open routing in service networks,
*Management Science*, forthcoming. (pdf) (ssrn)- Finalist in the 2016 M&SOM Student Paper Competition (Entrant: A. E. Frazelle)

- Arlotto, A. and Steele, J.M. (2018) A central limit theorem for costs in Bulinskaya's inventory management problem when deliveries face delays,
*Methodology and Computing in Applied Probability*,**20**, 839-854. (pdf) (arXiv) - Arlotto, A., Wei, Y., and Xie, X. (2018) An adaptive O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample,
*Random Structures & Algorithms*,**52**, 41-53. (pdf) (arXiv) - Arlotto, A. and Steele, J.M. (2016) A central limit theorem for temporally non-homogenous Markov chains with applications to dynamic programming,
*Mathematics of Operations Research*,**41**, 1448-1468. (pdf) (arXiv) - Arlotto, A. and Steele, J.M. (2016) Beardwood-Halton-Hammersley theorem for stationary ergodic sequences: a counterexample,
*The Annals of Applied Probability*,**26**, 2141-2168. (pdf) (arXiv) - Arlotto, A., Mossel, E., and Steele, J.M. (2016) Quickest online selection of an increasing subsequence of specified size,
*Random Structures & Algorithms*,**49**, 235-252. (pdf) (arXiv) - Arlotto, A., Nguyen, V. V. , and Steele, J.M. (2015) Optimal online selection of a monotone subsequence: a central limit theorem,
*Stochastic Processes and their Applications*,**125**, 3596-3622. (pdf) (arXiv) - Arlotto, A., Gans, N., and Steele, J.M. (2014) Markov decision problems where means bound variances,
*Operations Research*,**62**, 864-875. (pdf) - Arlotto, A. and Steele, J.M. (2014) Optimal online selection of an alternating subsequence: a central limit theorem,
*Advances in Applied Probability*,**46**, 536-559. (pdf) (arXiv) - Arlotto, A., Chick, S.E., and Gans, N. (2014) Optimal hiring and retention policies for heterogeneous workers who learn,
*Management Science*,**60**, 110-129. (pdf) (online appendix) - Arlotto, A., Chen, R.W., Shepp, L.A. and Steele, J.M. (2011) Online selection of alternating subsequences from a random sample,
*Journal of Applied Probability*,**48**, 1114-1132. (pdf) (arXiv) - Arlotto, A. and Steele, J.M. (2011) Optimal sequential selection of a unimodal subsequence of a random sequence,
*Combinatorics, Probability and Computing*,**20**, 799-814. (pdf) (arXiv) - Arlotto, A. and Scarsini, M. (2009) Hessian orders and multinormal distributions,
*Journal of Multivariate Analysis*,**100**, 2324-2330. (pdf)

**Conference Proceedings:**

- Arlotto, A., Chick, S.E., and Gans, N. (2010) Optimal employee retention when inferring unknown learning curves,
*Proceedings of the 2010 Winter Simulation Conference*, 1178-1188. (pdf)

**Events:**

WORDS 2018: Workshop in Operations Research and Data Science

The Fuqua School of Business, Duke University

December 2, 2018

WORDS 2017: Workshop in Operations Research and Data Science

The Fuqua School of Business, Duke University

December 2, 2017

2017 Southeastern Probability Conference

Duke University

May 15-17, 2017

Conference on Probability
Theory and Combinatorial Optimization

The Fuqua School of Business, Duke University

March 14-15, 2015