**Funding from NSF under
grants DMS-0595595, DMS-0806145 / 0902075, CAREER award CMMI-0846816,**
**CMMI-1069064, DMS-1320550, CMMI-1436700, is gratefully acknowledged.**

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

__Articles
in Journals (published or accepted for publication)__

__Articles in
Journals (submitted)__

__Conference
Proceedings (published or accepted for publication)__

__Conference
Proceedings (accepted up to a minor revision)__

__Book Chapters
and Encyclopedia Articles__

__Articles Submitted
for Publication__

**Many of the papers below take you to a link which includes a brief
explanation of the paper in an informal style. (All of them should have such
explanation soon.)**

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. *Bernoulli*, 20, (2014),
416-456. (S)

30. **Blanchet, J.**, Glynn, P., and Meyn, S. Large
Deviations for the Empirical Mean of an M/M/1 Queue. *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. *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. *Mathematics of Operations
Research*, 39, (2014), 1142–1178. (S)

35. **Blanchet, J.**, Chen, X., and Lam, H. Two-parameter Sample Path Large
Deviations for Infinite Server Queues. *Stochastic
Systems,* 4, (2014), 206-249*.*

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.

37. **Blanchet, J. **and Chen, X. Steady-state
Simulation for Reflected Brownian Motion and Related Networks. To appear *Annals of Applied Probability.*__ __

38. Murthy,
K.,** **Juneja,
S., and **Blanchet, J. **State-independent Importance Sampling for Random
Walks with Regularly Varying Increments. To appear *Stochastic Systems.*

39. **Blanchet, J.** and Dong, J. Perfect Sampling for Infinite
Server and Loss Systems. *Advances in
Applied Probability*, 47, (2015).

40. **Blanchet, J. **and Wallwater,
A. Exact Sampling for the
Steady-state Waiting Time of a Heavy-tailed Single Server Queue. To appear
in *ACM TOMACS Transactions on Modeling
and Computer Simulations*.

41. **Blanchet, J.**, Gallego,
G. and Goyal, V. A
Markov Chain Approximation to Choice Modeling. To appear in *Operations Research*.

42. Zhang, X., **Blanchet, J.**, Giesecke,
K., and Glynn, P. Affine Point Processes:
Approximation and Efficient Simulation. To appear in *Mathematics of Operations Research.*

43. **Blanchet, J.,** and Murthy, K. Tail Asymptotics
for Large Delays in a Half-Loaded GI/GI/2 Queue with Heavy-Tailed Job Sizes.
To appear in *Queueing Systems: Theory and
Applications.*

44. Bienstock, D., Li, J., and **Blanchet, J. **Stochastic
Models and Control for Electrical Power Line Temperature. To appear in *Energy Systems*.** **

3.
__Articles in Journals (submitted for publication)__

45. **Blanchet, J.** and Ruf,
J. A Weak Convergence Criterion
Constructing Changes of Measure. Submitted to *Stochastic Models*.

46. **Blanchet, J.**, Chen, X., and Dong,
J. ε-Strong Simulation of
Multidimensional Stochastic Differential Equations via Rough Path Analysis.
Submitted to *Annals of Applied
Probability*. http://arxiv.org/abs/1403.5722

47. **Blanchet, J.** and Murthy, K. __Exact Simulation of Multidimensional
Reflected Brownian Motion__. Submitted to
*Advances in Applied Probability*.

48. **Blanchet, J.**, Li, J., and Nakayama, M. __Efficient Monte Carlo Methods
for Estimating Failure Probabilities of a Distribution Network with Random
Demands__. Submitted to *Operations
Research*.

49. **Blanchet, J., **Glynn, P., and Zheng, J. __Theoretical Analysis of a Stochastic
Approximation Approach for Computing Quasi-stationary Distributions__.
Submitted to *Journal of Applied
Probability*.

50. **Blanchet, J., **Dong, J., and Pei, Y. __Perfect Sampling of G/G/c Queues.__ *Submitted to Stochastic Systems.*

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.

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

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

53. **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) +

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

55. **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) +

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

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

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

59. 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) +

60. **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)

61. **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)

62. **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)

63. 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)

64. **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)

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

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

67. **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)

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

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

70. **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)

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

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

73. **Blanchet, J.** and Shi, Y. Efficient Rare Event Simulation via
Particle Methods for Heavy-tailed Sums. Proc. Winter Simulation Conference
(2013), pp. 724-735.

74. **Blanchet, J.**, Murthy, K., and Juneja, S. Optimal Rare
Event Monte Carlo for Markov Modulated Regularly Varying Random Walks.
Proc. Winter Simulation Conference (2013), 564-576.

75. Bienstock, D., **Blanchet,
J.**, and Li, J. __Stochastic
Models and Control for Electrical Power Line Temperature.__ Proc. 51^{st}
Annual Allerton Conference on Communication, Control, and Computing (2013),
1344-1348.

76. **Blanchet, J.**, Dolan, C., and Lam, H. __Robust Rare-Event Performance Analysis with
Natural Non-Convex Constraints.__ Proc. Winter Simulation Conference
(2014), pp. 595-603.

77. Shanbhag, U., and **Blanchet,
J.** __Budget-Constrained Stochastic
Approximation__. Proc. Winter Simulation Conference (2015), to appear.

78. **Blanchet, J.**, Chen, N., and Glynn, J. __Unbiased Monte Carlo
Computation of Smooth Functions of Expectations via Taylor Expansions__.
Proc. Winter Simulation Conference (2015), to appear.

79. **Blanchet, J.**, and Glynn, J. __Unbiased Monte Carlo for Optimization and
Functions of Expectations via Multilevel Randomization__. Proc. Winter
Simulation Conference (2015), to appear.

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

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

80. **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.

81. **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.

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

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

84. **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 under Review__

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

86. **Blanchet, J.**, and Glynn, P. Approximations
for the Distribution of Perpetuities with Small Interest Rates. Under
revision.

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 it is not uploaded.__

87. **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.

88. **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.

89. **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).

90. **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.

91. **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.

92. **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.

93. **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, I hope to report on that in
the not-so-distant future.

__Other manuscripts in progress__

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

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