New graduate course, Spring 2017:
BA915/MATH742/STA715, Stochastic Models (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., Frazelle, A. E., and Wei, Y. (2016) Strategic open routing in service networks, under review. (pdf)(ssrn)
Finalist in the 2016 M&SOM Student Paper Competition (Entrant: A. E. Frazelle)
Arlotto, A., Wei, Y., and Xie, X. (2016) A O(log n)-optimal policy for the online selection of a monotone subsequence from a random sample, under review. (pdf)(arXiv)
Published Papers:
Arlotto, A. and Steele, J.M. (2016) A central limit theorem for costs in Bulinskaya's inventory management problem when deliveries face delays, Methodology and Computing in Applied Probability, forthcoming. (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)