**Funding sources from
NSF provided under the grants DMS-0806145 / 0902075, CAREER award CMMI-0846816
and** **CMMI-1069064 are gratefully acknowledged. Previous
support includes NSF grant 0595595**

*Recurrent theme in
many of the following articles: Interplay between probability theory and
optimal design of algorithms. In other words, what coarse analysis (typically
done to obtain asymptotics) tells you about the
design of efficient Monte Carlo simulation methods.
*

**Publications and Scholarly Works (in cronological order within sections)**

__Articles
in Journals (published or accepted for publication)__

__Articles in
Journals (accepted up to minor revision or in their second round)__

__Conference
Proceedings (published or accepted for publication)__

__Conference
Proceedings (accepted up to a minor revision)__

__Book Chapters
and Encyclopedia Articles__

__Articles Submitted
for Publication (or to be submitted within the summer of 2011)__

__Sample Pre-prints,
Extended Abstracts and Working Papers (Please Request if Needed)__

1.
__Doctoral thesis title__

Limit Theorems and Approximations with Applications to
Insurance Risk and Queueing Theory (2004) Stanford
University, Department of Management Science and Engineering. Advisor,
Professor Peter Glynn

2.
__Articles in Journals (published or accepted for
publication)__

** Denotes a publication given the 2009 Applied Probability Society Best Publication Award.

* Finalist paper in the 2010 INFORMS Junior Faculty Interest Group Forum Competition.

& Honorable mention in the 2011 INFORMS Nicholson Student Paper Competition (as supervisor).

% Finalist INFORMS 2010 Junior Faculty Interest Group competition.

Most links of
the published or accepted papers prompt to a summary page which includes a bibtex record and the paper.

1.
Pekoz, E., and **Blanchet**, **J.** Heavy-traffic Limit Theorems via
Embeddings. *Probability in the
Engineering and Informational Sciences*, 20 (2006), pp. 595-598

2.
**Blanchet**, **J.**, and Glynn, P. Corrected
Diffusion Approximations for the Maximum of Light-tailed Random Walk. *Annals of Applied Probability*, 16
(2006), 2, pp. 952-983. (T)

3.
**Blanchet**, **J.**, and Glynn, P. Uniform Renewal Theory with Applications to
Geometric Sums. *Advances in Applied
Probability*, 39 (2007), 4, pp 1070 – 1097.

4.
**Blanchet**, **J.**, Glynn, P., and Liu, J. C. Fluid Heuristics, Lyapunov
Bounds and Efficient Importance Sampling for a
Heavy-tailed G/G/1 Queue. *Queueing** Systems:
Theory and Applications*, 56 (2007), 3, pp. 99 – 113.** (S)

5.
**Blanchet**, **J.** and Glynn, P. Efficient Rare Event Simulation for the Single
Server Queue with Heavy Tailed Distributions. Annals of Applied Probability, 18 (2008), 4,
pp. 1351 – 1378.**

6.
**Blanchet**, **J.**, and Liu, J. C. State-dependent Importance Sampling for
Regularly Varying Random Walks. *Advances
in Applied Probability*, 40, (2008), pp 1104-1128. (S)

7.
Asmussen, S., **Blanchet**, **J.**, Rojas-Nandayapa, L., and Juneja, S. Efficient
Simulation of Tails Probabilities of Sums of Correlated Lognormals. To
appear in *Annals of Operations Research*,
Special vol. in honor of Reuven Rubinstein.

8.
**Blanchet**, **J.**, Glynn, P., and Lam, H. Rare-event Simulation of a Slotted Time
M/G/s Queue. *Queueing** Systems: Theory and Applications*, 67,
(2009), pp 33 – 57. (S), (I)

9.
Olvera-Cravioto, M., **Blanchet**, **J.** and Glynn P. On the
Transition from Heavy Traffic to Heavy Tails for the M/G/1 Queue I: The Regularly Varying Case. *Annals of Applied Probability*, 21,
(2011), pp 645-668. (C)

10. **Blanchet**, **J.** Importance Sampling
and Efficient Counting for Binary Contingency Tables. *Annals of Applied Probability*, 19, (2009), pp 949 – 982.**

11. **Blanchet**, **J.**, and Li, C. Efficient
Rare-event Simulation for Heavy-tailed Compound Sums. *ACM TOMACS Transactions in Modeling and Computer Simulation*, 21,
(2011), pp 1-10.

12. **Blanchet**, **J.**, and Li, C. Efficient
Simulation for the Maximum of Infinite Horizon Gaussian Processes. To
appear in *Journal of Applied Probability.
*(S)

13. **Blanchet**, **J.** and Liu, J. Efficient
Importance Sampling in Ruin Problems for Multidimensional Regularly Varying
Random Walks. *Journal of Applied
Probability*, 47, (2010), 301-322.* (S)

14. **Blanchet**, **J.** and Zwart, B. Asymptotic Expansions of Renewal Equations
with Applications to Insurance and Processor Sharing. *Math. Meth. in Oper.
Res.*, 72, (2010), 311-326.

15. L'Ecuyer, P., **Blanchet****, J**., Tuffin,
B., and Glynn, P. W. Asymptotic Robustness of Estimators in
Rare-Event Simulation. *ACM TOMACS
Transactions in Modeling and Computer Simulation*, 20, (2010), pp 1-41.

16. Lam,
H. K., **Blanchet, J.**, Bazant, M., and Burch, D. Corrections to the Central Limit
Theorem for Heavy-tailed Probability Densities. To appear in *Journal of Theoretical Probability*.
(Available through on-line first since September 17, 2011.) (S)

17. **Blanchet, J.**, Leder,
K., Shi, Y. Analysis of a Splitting
Estimator for Rare Event Probabilities in Jackson Networks. *Stochastic Systems, *1, (2011), pp
306-339.* *(P, S)

18. **Blanchet, J.**, and Rojas-Nandayapa, L. Efficient Simulation of Tail
Probabilities of Sums of Dependent Random Variables.* Journal of Applied
Probability, Special Vol. 48A, *(2011), 147-165.

19. **Blanchet, J**., and Sigman,
K. On
Exact Sampling of Stochastic Perpetuities. *Journal of Applied
Probability, Special Vol. 48A, *(2011), 165-183. (C)

20. **Blanchet, J.**, and Lam, H. State-dependent Importance Sampling for Rare
Event Simulation: An Overview and Recent advances. *Surveys in Operations Research and Management Sciences, *17*, *(2012), 38-59 (S)

21. Adler,
R., **Blanchet, J.**, and Liu, J. C. Efficient Simulation of High
Excursions of Gaussian Random Fields. *Annals
of Applied Probability*, 22, (2012), 1167-1214. (S)

22. **Blanchet, J. **and Pacheco-Gonzales, C. Uniform Convergence to a Law
Containing Gaussian and Cauchy Distributions. *Probability in the Engineering and Informational Sciences,* 26,* *(2012), 437-448

23. **Blanchet, J.**, and Stauffer, A. Characterizing Optimal Sampling of Binary
Contingency Tables via the Configuration Model. *Random Structures and Algorithms,* 42, 159-184 (2012). (See also http://arxiv.org/abs/1007.1214.)

*24. ***Blanchet, J.**, Glynn, P., and Leder, K. On Lyapunov Inequalities and Subsolutions for Efficient Importance Sampling. *ACM TOMACS* *Transactions in Modeling and Computer Simulation, *22, (2012).
Article No. 13.

**25. ****Blanchet, J.**, Lam, H., and Zwart, B. Efficient
Rare Event Simulation for Perpetuities.*
Stochastic Processes and their Applications, *122, (2012), 3361-3392. (S)

26. **Blanchet, J.** Optimal Sampling of Overflow Paths in Jackson
Networks. * Mathematics of Operations Research, *38,** **(2013**)**, 698-719.

27. **Blanchet, J.**, and Liu, J. C. Efficient Simulation and Conditional Functional Limit
Theorems for Ruinous Heavy-tailed Random Walks. *Stochastic Processes and their Applications*, 122, (2012),
2994-3031. (S)

28. **Blanchet, J. **and Shi, Y. Strongly Efficient Algorithms via
Cross Entropy for Heavy- tailed Systems. *Operations Research Letters*, 41, (2013), 271-276. (S)

29. **Blanchet, J.**, and Liu, J. C. Total Variation Approximations for
Multivariate Regularly Varying Random Walks Conditioned on Ruin. To appear
in *Bernoulli*. (S) http://www.e-publications.org/ims/submission/index.php/BEJ/user/submissionFile/10999?confirm=27b27289
%

30. **Blanchet, J.**, Glynn, P., and Meyn, S. Large
Deviations for the Empirical Mean of an M/M/1 Queue. *Submitted to Queueing Systems: Theory and
Applications, *73, (2013), 425-446.

31. **Blanchet, J. **and Lam, H. A Heavy Traffic Approach to Modeling Large Life
Insurance Portfolios. *Insurance:
Mathematics and Economics*, 53, (2013), 237-251. (S)

32. **Blanchet, J.** and Mandjes,
M. Asymptotics of the
Area under the Graph of a Lévy-driven Workload
Process. Accepted in *Operations
Research Letters*, 41, (2013), 730-736.

33. **Blanchet, J.**, Hult,
H., and Leder, K. Rare-event simulation for stochastic
recurrence equations with heavy-tailed innovations. *ACM TOMACS Transactions on Modeling and Computer Simulations. *(Supplement), 23,
(2013), Article No. 22.

34. **Blanchet, J**., and Lam, H. Rare-event Simulation for Many Server
Queues. To appear in *Mathematics of
Operations Research*. (S)

35. **Blanchet, J.**, Chen, X., and Lam, H. Two-parameter Sample Path Large Deviations for Infinite
Server Queues. To appear in *Stochastic
Systems.*

36. **Blanchet, J. **and Lam, H. Uniform Large Deviations for Heavy-Tailed
Queues under Heavy-Traffic. *Bulletin
of the Mexican Mathematical Society*, Bol. Soc. Mat. Mexicana (3) Vol. 19,
2013 Special Issue for the International Year of Statistics.

3.
__Articles in Journals (accepted up to minor revision)__

37. **Blanchet, J. **and Chen, X. Steady-state Simulation for Reflected
Brownian Motion and Related Networks. Accepted up
to minor revisions in *Annals of Applied
Probability. *(S)

38. **Blanchet, J.**, Gallego,
G. and Goyal, V. A
Markov Chain Approximation to Choice Modeling. Submitted to *Operations Research*.

39. **Blanchet, J. **and Wallwater,
A. Exact Sampling for the Steady-state Waiting Time of a Heavy-tailed Single
Server Queue.

4.
__Conference Proceedings (published or accepted for
publication)__

Note: + represents a paper for which there is journal version listed above, otherwise there is no overlap in content with papers that have appeared in journals.

40. **Blanchet, J.**, and Glynn, P. Strongly-efficient Estimators for
Light-tailed Sums. ACM: Proc Valuetools’06, Article 18, (2006). (S)

41. **Blanchet, J.**, Liu, J. C. and Glynn, P. Importance Sampling and Large
Deviations. Proc. Valuetools’06, Article 20, (2006). (S)

42. **Blanchet, J.**, and Liu, J. C. Efficient Simulation of
Large Deviation Probabilities for Sums of Heavy-tailed Increments. Proc.
Winter Simulation Conference (2006), pp. 757-764. (S) +

43. **Blanchet, J.**, and Zwart,
B. Importance Sampling of
Compounding Processes. Proc. Winter Simulation Conference (2007), pp.
372-379. (I) +

44. **Blanchet, J.**, and Liu, J. C. Rare-event
Simulation of Multidimensional Random Walks with t-distributed Increments.
Proc. Winter Simulation Conference (2007), pp. 395-402. (S) (I) +

45. **Blanchet, J.**, and Liu, J. C. Path-sampling for State-dependent
Importance Sampling. Proc. Winter Simulation Conference (2007), pp.
380-388. (S) (I)

46. Zhang,
X., **Blanchet, J.**, and Glynn, P. Efficient Suboptimal
Rare-event Simulation. Proc. Winter Simulation Conference (2007), pp.
389-394. (I)

47. **Blanchet, J.**, Rojas-Nandayapa,
L., and Juneja, S. Fast Simulation of Sums of Correlated
Lognormals. Proc. Winter Simulation Conference (2008), pp. 607-614. +

48. Adler,
R., **Blanchet, J**. and Liu, J. C. Efficient Simulation for Tail
Probabilities of Gaussian Random Fields. Proc. Winter Simulation Conference
(2008), pp 328-336. (S) (I) +

49. **Blanchet, J.**, Liu, J. C., and Zwart, B. A Large
Deviations Perspective to Ordinal Optimization of Heavy-tailed Systems.
Proc. Winter Simulation Conference (2008), pp. 489-494. (S) (I)

50. **Blanchet, J.**, Leder,
K. and Glynn, P. Efficient
Simulation for Light-tailed Sums: An Old Folk Song Sung to a Faster New Tune.
Springer volume for MCQMC 2008 edited by Pierre L’Ecuyer
and Art Owen. (2009), pp. 227-248. (P)

51. **Blanchet, J.**, and Glynn, P. Efficient Rare Event Simulation of
Continuous Time Markovian Perpetuities. Proc. Of
the Winter Simulation Conference (2009), pp. 444-451. (I)

52. Zhang,
X., Glynn, P., Giesecke, K., **Blanchet, J.** Rare Event
Simulation of a Generalized Hawkes Process. Proc.
Of the Winter Simulation Conference (2009), pp. 1291-1298. (I)

53. **Blanchet, J.**, Liu, J. C., and Xang, X. Monte
Carlo for Large Credit Portfolios with Potentially High Correlations. Proc.
of the Winter Simulation Conference (2010), pp. 328-336. (S) (I)

54. **Blanchet, J.**, and Lam, H. Rare Event Simulation Techniques.
Proc. Winter Simulation Conference
(2011). (S) (I)

55. **Blanchet, J.**, and Shi, Y. Strongly Efficient Cross Entropy
Method for Heavy-tailed Simulation, Winter Simulation Conference (2011).
(S) (I)

56. **Blanchet, J.**, Li, Juan, and Nakayama,
M. A Conditional Monte Carlo
for Estimating the Failure Probability of a Network with Random Demands
(2011). (S) (I)

57. **Blanchet, J.**, Hult,
H., and Leder, K. Efficient Importance Sampling for
Affine Regularly Varying Markov Chains (2011).

58. **Blanchet, J.,** and Lam, H. Importance Sampling for Actuarial Cost
Analysis under a Heavy Traffic Model. Proc.
Winter Simulation Conference (2011). (S) (I)

59. **Blanchet, J.,** and Dong, J. Sampling point processes on stable
unbounded regions and exact simulation of queues. Proc. Winter Simulation
Conference (2012): 11 (S) (I)

60. **Blanchet, J.,** Glynn, P., and Zheng, S. Empirical
Analysis of a Stochastic Approximation Approach for Computing Quasi-stationary
Distributions. EVOLVE 2012: 19-37

61. **Blanchet, J.,** Gallego,
G., and Goyal, G. A
Markov chain approximation to choice modeling. ACM Conference on Electronic
Commerce 2013: 103-104

62. **Blanchet, J.** and Shi, Y. Efficient Rare Event Simulation via
Particle Methods for Heavy-tailed Sums. Proc. Winter Simulation Conference
(2013).

63. **Blanchet, J.**, Murthy, K., and Juneja, S. Optimal Rare
Event Monte Carlo for Markov Modulated Regularly Varying Random Walks.
Proc. Winter Simulation Conference (2013).

5.
__Conference Proceedings (accepted up to a minor
revision)__

6.
__Chapters in Books (published or accepted for
publication)__

64. **Blanchet, J.**, and Rudoy,
D. Rare-event Simulation and Counting Problems. In Rare-event Estimation using
Monte Carlo Methods, Rubino, G. and Tuffin, B. Eds. Wiley, 2009.

65. **Blanchet, J.**, and Mandjes,
M. Rare-event Simulation for Queues. In Rare-event Estimation using Monte Carlo
Methods, Rubino, G. and Tuffin,
B. Eds. Wiley, 2009.

66. **Blanchet, J.** and Pacheco-Gonzales C.
Large Deviations and Applications to Quantitative Finance. Encyclopedia of
Quantitative Finance, Edited by Rama Cont. Wiley 2009.

67. **Blanchet, J.**, and Mandjes,
M. Rare-event Simulation for Queues (2007). *Queueing** Systems: Theory and Applications*. Vol. 57 Numbers 2 and 3.
Editorial.

68. **Blanchet, J.**, and Roberts, G. Simulation
of Stochastic Networks and related topics (2012). *Queueing** Systems: Theory and Applications*. Vo. 73 Numbers 4. Editorial.

8.
__Journal Articles Submitted for Publication__

69. **Blanchet, J.** and Shi, Y. Modeling and
Efficient Rare Event Simulation of Systemic Risk in Insurance-Reinsurance
Networks. Under revision*.* (S)

70. **Blanchet, J.** and Ruf,
J. A Weak Convergence Criterion Constructing Changes of Measure. Under
revision.

71. **Blanchet, J.** and Dong, J. Exact
Simulation of Loss Networks and Running Time Analysis in Heavy Traffic. Under
revision.

72. **Blanchet, J.**, and Glynn, P. Approximations
for the Distribution of Perpetuities with Small Interest Rates. Submitted

73. Murthy, K.,** **Juneja, S.,
and **Blanchet, J. **State-independent Importance Sampling for Random
Walks with Regularly Varying Increments. Submitted.

74. **Blanchet, J. **and Dong, J. Rare-event
Simulation for the Steady-state Queue Length Process of Many Server Queues. Submitted.

75. Zhang, X., Blanchet, J., Giesecke, K., and Glynn, P. Affine Point Processes: Approximation and Efficient Simulation. Submitted.

9.
__Some Preprints and Technical Reports. BEWARE the presentation
requires polishing, but the math should be fine – however, I’d appreciate
comments if you see any problem. Also, please, email me if you’re interested in
any of the preprints below and is not uploaded.__

75. **Blanchet, J.**, Liu, J. C., and Glynn, P.
Efficient Rare Event Simulation for Regularly
Varying Multi-server Queues. To be submitted to *Queueing** Systems Theory and Applications.* Summary: This paper provides the first
asymptotically optimal (in fact we show strong optimality) algorithm for
estimating the tails of the steady-state delay in a multi-server queue with
heavy-tailed increments. The technique is the one introduced in “Fluid Heuristics, Lyapunov Bounds and Efficient Importance Sampling for a
Heavy-tailed G/G/1 Queue (with P. Glynn, and J. C. Liu), 2007.
QUESTA, 57, 99-113”. The construction in the multidimensional case is
interesting because of the way in which fluid heuristics need to adapt to
accommodate the boundaries.

76. **Blanchet, J. **and Glynn, P.** **Large
Deviations and Sharp Asymptotics for Perpetuities
with Small Discount Rates . Summary: This paper relates to
Approximations for the Distribution of Perpetuities with Small Discount Rates
(with P. Glynn). Instead of concentrating on the central limit theorem region
we develop large deviations asymptotics. The paper
contains characterizations of exponential tightness in a suitable (and useful
for the analysis of perpetuities) class of topologies. It also develops exact
tail asymptotics for discrete and continuous
perpetuities and it shows qualitative differences arising from the discrete and
continuous nature of perpetuities. I think this line of research is
particularly interesting these days in which the interest rates are small.

77. **Blanchet, J. **and Glynn, P. Corrected Diffusion Approximations for the Maximum of Random
Walks with Heavy-tails (with P. Glynn). *(Please, refer to Chapter 3 of my
dissertation for more details).* Summary: This paper continues the study of
corrected diffusion approximations for first passage times of random walks with
a small negative drift $mu$. The paper “Complete Corrected Diffusion
Approximations for the Maximum of Random Walk (2006) Ann. of App. Prob., (with
P. Glynn)” assumes finite exponential moments. Here we show that if one has
$alpha + 2$ moments then one can add $alpha$ correction terms resulting in an
approximation with an error of size o(mu^alpha).

78. **Blanchet, J.,** and Lam, H. K. Corrected
Diffusion Approximations for Moments of the Steady-state Waiting Time in a
G/G/1 Queue. Summary: This paper revisits Complete Corrected
Diffusion Approximations for the Maximum of Random Walk (with P.
Glynn), 2006. Ann. of App. Prob., 16, p. 951-953. We now concentrate on moments
rather than the tail of the distribution. Recently Janssen and van Leeuwaarden (2007), Stoch. Proc.
and their Appl., 117, 1928-1959, obtained complete asymptotic expansions for
the Gaussian case. Here we obtain expansions for any strongly non-lattice
distribution exponentially decaying tails.

79. **Blanchet, J.,** Glynn, P. and Zheng, J.. A Stochastic Approximations
Algorithm for the Quasi-stationary Distribution of a Markov chain. Summary: This
paper provides the analysis of a very nice algorithm that Peter Glynn told me
about. He reports that he knew this from a faculty in the Biostatistics
Department at Harvard. Here is how it goes. You wish to compute “lambda” > 0
and a probability vector “mu” such that mu*P = lambda*mu, where P is strictly
sub-stochastic (and irreducible). So, you add a cemetery state to make P
stochastic. At state k you have a current estimate mu(k,y) for mu(y) (“y” is any non-cemetery state of the chain)
and lambda(k) for lambda. You start iteration k+1 by picking a point according
to mu(k). Run the chain until it hits the cemetery
state. Record the time tau(k+1) and the number of
times, N(k+1,y), the chain visits state “y” in the k+1 iteration prior to
absorption. Then, let lamda(k+1) =
[k*lambda(k)+tau(k+1)]/(k+1) and mu(k+1,y)=[mu(k,y)*lambda(k)+N(k+1,y)]/lambda(k+1).
The paper uses the ODE stochastic approximations method to prove the validity
of the algorithm and analyze its rate of convergence.

80. **Blanchet, J.,** and Lam, H. K. Rare-event Simulation
for Markov Modulated Heavy-tailed Random Walks. Summary: This
paper considers rare-event simulation for first passage time probabilities of
Markov modulated regularly varying random walks. We use the Lyapunov-bound
technique to design the importance sampling estimator. What is interesting is
that the Lyapunov bound, instead of being tested in
one step of the underlying process, as we typically do, it must be tested after
K steps (for K large enough) or at regeneration times of the underlying Markov
modulation.

81. **Blanchet, J.,** and Meng,
X. L. Exact Sampling, Regeneration and Minorization
Conditions. Summary: This report builds on a paper by Asmussen, Glynn and Thorisson
(1992) TOMACS. Here we note that if one has a Harris chain with regeneration
time “tau” and if one can compute a constant C>0 such that E(tau^p)<=C for p>1 (or Eexp(delta*tau)<C) then one can generate exact samples
from the steady-state distribution of
chain in question in finite time almost surely, provided that one can identify
the regeneration times of the chain. Unfortunately, although the expected
termination time will typically be infinite. HOWEVER! I recently figured out
how to fix the problem for a large class of chains! So, I’ll get back to this
problem soon!

__Some work in Progress__

82. **Blanchet, J.** and Dupuis, P. Fast
Simulation of Brownian Motion Avoiding Random
Obstacles.

83. **Blanchet, J. **and Kirkpatrick, K. Asymptotic
Analysis of a Dynamic Quantum Curie-Weiss-type model.