Marvin Nakayama
Marvin Nakayama
Professor, Computer Science
4312 Guttenberg Information Technologies Center (GITC)
About Me
Marvin Nakayama is a professor in the computer science department at NJIT. He has a Ph.D. and M.S. in operations research from Stanford University, and a B.A. in mathematics-computer science from U.C. San Diego. A recipient of a CAREER award from NSF, he served as the simulation area editor for INFORMS Journal on Computing, and is on the editorial board of ACM Transactions on Modeling and Computer Simulation.
Education
Ph.D.; Stanford University; Operations Research; 1991
M.S.; Stanford University; Operations Research; 1988
B.A.; University of California-San Diego; Mathematics-Computer Science; 1986
M.S.; Stanford University; Operations Research; 1988
B.A.; University of California-San Diego; Mathematics-Computer Science; 1986
Website
2024 Fall Courses
IS 726 - INDEPENDENT STUDY II
CS 701B - MASTER'S THESIS
CS 725 - INDEPENDENT STUDY I
CS 726 - INDEPENDENT STUDY II
CS 790A - DOCT DISSERTATION & RES
IS 488 - INDEPENDENT STUDY IN INFO
CS 488 - INDEPENDENT STUDY IN CS
CS 792 - PRE-DOCTORAL RESEARCH
IS 700B - MASTER'S PROJECT
IS 776 - IS RESEARCH STUDY
IS 725 - INDEPENDENT STUDY I
IS 701B - MASTER'S THESIS
CS 700B - MASTER'S PROJECT
IS 489 - INFO UNDERGRAD THESIS RESEARCH
IS 790A - DOCT DISSERTATION & RES
IS 792 - PRE-DOCTORAL RESEARCH
CS 701B - MASTER'S THESIS
CS 725 - INDEPENDENT STUDY I
CS 726 - INDEPENDENT STUDY II
CS 790A - DOCT DISSERTATION & RES
IS 488 - INDEPENDENT STUDY IN INFO
CS 488 - INDEPENDENT STUDY IN CS
CS 792 - PRE-DOCTORAL RESEARCH
IS 700B - MASTER'S PROJECT
IS 776 - IS RESEARCH STUDY
IS 725 - INDEPENDENT STUDY I
IS 701B - MASTER'S THESIS
CS 700B - MASTER'S PROJECT
IS 489 - INFO UNDERGRAD THESIS RESEARCH
IS 790A - DOCT DISSERTATION & RES
IS 792 - PRE-DOCTORAL RESEARCH
Teaching Interests
Theory of Computation, Stochastic Simulation and Modeling
Past Courses
CS 103: COMPUT SCI-BUSINESS PROB
CS 341: FOUND OF COMPUTER SCIENCE II
CS 341: FOUNDATIONS OF COMPUTER SCIENCE II
CS 661: SYSTEMS SIMULATION
IE 651: INDUSTRIAL SYM & MODEL
CS 341: FOUND OF COMPUTER SCIENCE II
CS 341: FOUNDATIONS OF COMPUTER SCIENCE II
CS 661: SYSTEMS SIMULATION
IE 651: INDUSTRIAL SYM & MODEL
Research Interests
Stochastic modeling, Monte Carlo, randomized quasi-Monte Carlo, discrete-event simulation, variance reduction, output analysis, statistics, fault-tolerant computing, risk analysis, energy.
Journal Article
Nakayama, Marvin K., & Tuffin, Bruno (2024). Sufficient Conditions for Central Limit Theorems and Confidence Intervals for Randomized Quasi-Monte Carlo Methods. ACM Transactions on Modeling and Computer Simulation, 34(3), 1–38.
Li, Yajuan K, & Kaplan, Zachary T., & Nakayama, Marvin K. (2024). Monte Carlo Methods for Economic Capital. INFORMS Journal on Computing, 36(1), 266–284.
Blanchet, Jose, & Li, Juan, & Nakayama, Marvin K. (2019). Rare-Event Simulation for Distribution Networks. Operations Research, 67(5), 1383–1396.
Dong, Hui, & Nakayama, Marvin K. (2017). Quantile Estimation With Latin Hypercube Sampling. Operations Research, 65(6), 1678-1695.
Alban, Andres, & Darji, Hardik, & Imamura, Atsuki, & Nakayama, Marvin K. (2017). Efficient Monte Carlo Methods for Estimating Failure Probabilities. Reliability Engineering and System Safety/Elsevier, 165, 376-394.
Li, Yajuan K, & Kaplan, Zachary T., & Nakayama, Marvin K. (2024). Monte Carlo Methods for Economic Capital. INFORMS Journal on Computing, 36(1), 266–284.
Blanchet, Jose, & Li, Juan, & Nakayama, Marvin K. (2019). Rare-Event Simulation for Distribution Networks. Operations Research, 67(5), 1383–1396.
Dong, Hui, & Nakayama, Marvin K. (2017). Quantile Estimation With Latin Hypercube Sampling. Operations Research, 65(6), 1678-1695.
Alban, Andres, & Darji, Hardik, & Imamura, Atsuki, & Nakayama, Marvin K. (2017). Efficient Monte Carlo Methods for Estimating Failure Probabilities. Reliability Engineering and System Safety/Elsevier, 165, 376-394.
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Conference Paper
Efficiency of Estimating Functions of Means in Rare-Event Contexts
IEEE, December 2023
Confidence Intervals for Randomized Quasi-Monte Carlo Estimators
IEEE, December 2023
Density Estimators of the Cumulative Reward up to a Hitting Time to a Rarely Visited Set of a Regenerative System
IEEE, December 2022
Sufficient Conditions for a Central Limit Theorem to Assess the Error of Randomized Quasi-Monte Carlo Methods
IEEE, December 2021
Comparing Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set
IEEE, December 2020
IEEE, December 2023
Confidence Intervals for Randomized Quasi-Monte Carlo Estimators
IEEE, December 2023
Density Estimators of the Cumulative Reward up to a Hitting Time to a Rarely Visited Set of a Regenerative System
IEEE, December 2022
Sufficient Conditions for a Central Limit Theorem to Assess the Error of Randomized Quasi-Monte Carlo Methods
IEEE, December 2021
Comparing Regenerative-Simulation-Based Estimators of the Distribution of the Hitting Time to a Rarely Visited Set
IEEE, December 2020
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Chapter
Nakayama, Marvin K., & Tuffin, Bruno (2022). Array-RQMC to Speed up the Simulation for Estimating the Hitting-Time Distribution to a Rare Set of a Regenerative System, Zdravko Botev, Alexander Keller, Christiane Lemieux, Bruno Tuffin (Eds.), Springer. (pp. 333-352). Springer
Conference Proceeding
A Tutorial on Quantile Estimation via Monte Carlo
Springer Proceedings in Mathematics & Statistics, January (1st Quarter/Winter) 2020
Efficient Estimation of the Mean Hitting Time to a Set of a Regenerative System
IEEE, December 2019
Randomized Quasi-Monte Carlo for Quantile Estimation
IEEE, December 2019
On the Estimation of Mean Time to Failure by Simulation
2017 Winter Simulation Conference, IEEE, December 2017
Quantile Estimation Using Conditional Monte Carlo and Latin Hypercube Sampling
2017 Winter Simulation Conference, IEEE, December 2017
Springer Proceedings in Mathematics & Statistics, January (1st Quarter/Winter) 2020
Efficient Estimation of the Mean Hitting Time to a Set of a Regenerative System
IEEE, December 2019
Randomized Quasi-Monte Carlo for Quantile Estimation
IEEE, December 2019
On the Estimation of Mean Time to Failure by Simulation
2017 Winter Simulation Conference, IEEE, December 2017
Quantile Estimation Using Conditional Monte Carlo and Latin Hypercube Sampling
2017 Winter Simulation Conference, IEEE, December 2017
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Conference Abstract
Quantile Estimation via a Combination of Conditional Monte Carlo and Latin Hypercube Sampling
13th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing, INRIA, July (3rd Quarter/Summer) 2018
13th International Conference in Monte Carlo & Quasi-Monte Carlo Methods in Scientific Computing, INRIA, July (3rd Quarter/Summer) 2018
Other
Efficient Monte Carlo Methods for Estimating Risk of Nuclear Power Plants
BOOK OF ABSTRACTS Eighth International Undergraduate Summer Research Symposium, July (3rd Quarter/Summer) 2015
Efficient Algorithms for Analyzing Cascading Failures in a Markovian Dependability Model
Book of Abstracts: Seventh International Summer Research Symposium, July (3rd Quarter/Summer) 2014
BOOK OF ABSTRACTS Eighth International Undergraduate Summer Research Symposium, July (3rd Quarter/Summer) 2015
Efficient Algorithms for Analyzing Cascading Failures in a Markovian Dependability Model
Book of Abstracts: Seventh International Summer Research Symposium, July (3rd Quarter/Summer) 2014
Technical Report
Characterization and Quantification of Safety Margin
Argonne National Laboratory, Department of Energy,
Argonne National Laboratory, Department of Energy,