Research Team

Current Members

Yongseok Jeon

Yongseok is a third-year Ph.D. student in the ISE department at North Carolina State University. He received his B.S. and M.S. degrees in Industrial Engineering from Sungkyunkwan University (SKKU), Korea. His research interests lie in the field of simulation, optimization and data science. Currently, he is working on the digital twin calibration problem and simulation output analysis. Publications:

  1. Jeon, Y., and S.S., Digital Twin Calibration with Root Finding and Bayesian Optimization, Accepted in Proceedings of the 2024 Winter Simulation Conference.
  2. Jeon, Y., Pasupathy, P., and S.S., Statistical Inference on Simulation Output: Batching as an Inferential Device, Under Second Review at Journal of Simulation. arXiv preprint arxiv.org/abs/2311.04159
  3. Houser, E., S.S., Harrysson, O., and Jeon, Y., Predicting Additive Manufacturing Defects with Feature Selection for Imbalanced Data, IISE Transactions, 2023. doi.org/10.1080/24725854.2023.2207633
  4. Jeon, Y.S. and Lim, D.J., 2020. “PSU: Particle stacking undersampling method for highly imbalanced big data”. IEEE Access, 8, pp.131920-131927.
  5. Jeon, Y.S., Yang, D.H. and Lim, D.J., 2019. “FlexBoost: A flexible boosting algorithm with adaptive loss functions”. IEEE Access, 7, pp.125054-125061.

Ethan Houser

Ethan is a second year Ph.D. student in the department of Industrial & Systems Engineering at NC State. He completed his B.S. and M.S. in Industrial & Systems Engineering with a minor in Statistics from NC State as well. His research focus encompasses simulation optimization and machine learning while working with imbalanced datasets. His current work involves developing new feature selection algorithms that are convergent and fast. Publications:

  1. Houser, E., S.S., “Rapid Screening and Nested Partitioning for Feature Selection”, Accepted at Proceedings of 2024 Winter Simulation Conferences.
  2. Houser, E., S.S., Harrysson, O., and Jeon, Y., Predicting Additive Manufacturing Defects with Feature Selection for Imbalanced Data, IISE Transactions, 2023. doi.org/10.1080/24725854.2023.2207633

Hyungkhee Eun

Hyungkhee (HK) is a second-year Ph.D. student in the ISE department at North Carolina State University. He received his B.S. degree in Civil Engineering from Handong Global University (HGU), and M.S. degree in Development Policy from KDI School of Public Policy and Management, Korea. HK’s research interests includes stochastic simulation and optimization. His current research focuses on bias correction and novel techniques for rapid identification of worst-case performance in distributionally robust optimization. Publications:

  1. Eun, H.K., S.S., and Barton, R.R., Identifying Input Uncertainty Induced Bias Using Wasserstein and Kingman Metrics, Accepted in Proceedings of the 2024 Winter Simulation Conference.
  2. Vahdat, K., S.S., and Eun, H.K., Robust Output Analysis with Monte-Carlo Methodology, Expected Submission Aug 2024, arXiv preprint arxiv.org/abs/2207.1361

Nicole Felice

Nicole is an incoming Operations Research Ph.D. student at North Carolina State University. She worked with Dr. Shashaani during and after her B.S. degree in Industrial Engineering from NCSU. Her research interests lie computational optimization. She currently works on data farming within simulation optimization for rapid solver turning and She also develops new tools for the SimOpt library. Publications:

  1. Felice, N., S.S., Eckman, D.E., and Sanchez, S.S., Data Farming for Repeated Simulation Optimization Experiments, Accepted at Proceedings of the 2024 Winter Simulation Conference.

 

Research Group Alumni

Yunsoo Ha

Yunsoo graduated with a Ph.D. degree in Industrial and System Engineering at NC State University in December 2023. Prior to that, he completed my B.S. and M.S. in Logistics from the Korea Aerospace University. His research interests lie in algorithmic development of the stochastic (simulation) optimization methods and quantum computing. Currently, he is a postdoctoral fellow at the National Laboratory of Renewable Energy. [Linkedin, Github]. Publications:

  1. Ha, Y., S.S., and Menickelly, M., “Two-stage Sampling and Variance Modeling for Variational Quantum Algorithms”, Accepted at
    INFORMS Journal of Computing, arXiv preprint arxiv.org/abs/2401.08912
  2. Ha, Y., S.S., and Pasupathy, R., Complexity of Zeroth- and First-order Stochastic Trust-Region Algorithms, Under Review at SIAM Journal of Optimization, arXiv preprint arxiv.org/abs/2405.20116
  3. Ha, Y., S.S., “Iteration Complexity and Finite-Time Efficiency of Adaptive Sampling Trust-Region Methods for Stochastic Derivative-Free Optimization”, IISE Transactions, doi.org/10.1080/24725854.2024.2335513
  4. Y. Ha, S.S., “Towards Greener Stochastic Derivative-free Optimization with Trust Regions and Adaptive Sampling”. In Proceedings of the 2023 Winter Simulation Conference, edited by C.G. Corlu, S.R. Hunter, H. Lam, B. S. Onggo, J. Shortle, and B. Biller. San Antonio, Texas: Institute of Electrical and Electronics Engineers, Inc.
  5. Menickelly, M., Ha, Y.,  Otten, M., “Latency considerations for stochastic optimizers in variational quantum algorithms”. Quantum. 2023 Mar 16;7:949.
  6. Ha, Y., S.S., and Tran-Dinh, Q. “Improved Complexity of Trust-region Optimization for Zeroth-order Stochastic Oracles with Adaptive Sampling”. In Proceedings of the 2021 Winter Simulation Conference, edited by S. Kim, B. Feng, K. Smith, S. Masoud, Z. Zheng, C. Szabo, and M. Loper. Orlando, Florida: Institute of Electrical and Electronics Engineers, Inc.
  7. Ha, Y., and Chae, J., “A decision model to determine the number of shuttles in a tier-to-tier SBS/RS”, International Journal of Production Research, Vol.  57(4), pp 963-984, 2019.
  8. Ha, Y., and Chae, J., “Free balancing for a shuttle-based storage and retrieval system”, Simulation Modeling Practice and Theory, Vol.  82, pp 12-31, 2018.
  9. Ha, Y., and Chae, J., “Dwell Point Policies for Shuttles on Shuttle-Based Storage/Retrieval System(SBS/RS)”, Journal of the Society of Korea Industrial and Systems Engineering, Vol.  39, pp 30-38, 2016.

Pranav Jain

Pranav is graduating with a Ph.D. degree from the department of Industrial and Systems Engineering at NCSU in Summer 2024. His research interests include simulation optimization, data-driven optimization, machine learning, and renewable energy. His work focuses on using variance reduction techniques like stratified sampling within the simulation optimization framework to improve the performance of simulation optimization algorithms. He was a data science consultant with Data and Visualization Services at NCSU Libraries and a data science graduate assistant with the Data Science Academy at NCSU. He is currently an intern at the National Renewable Energy Laboratory. Feel free to check out his GitHub and connect with him on LinkedIn.

My publication:

  1. Jain, P., S.S., and Byon, E., “Simulation Model Calibration with Dynamic Stratification and Adaptive Sampling, Under Second Review at Journal of Simulation. arXiv preprint arxiv.org/abs/2401.14558
  2. Jain, P., S.S., “Post-Stratified Adaptive Sampling with Concomitant Variables for Simulation Optimization”. In Proceedings of the 2023 Winter Simulation Conference, edited by C.G. Corlu, S.R. Hunter, H. Lam, B. S. Onggo, J. Shortle, and B. Biller. San Antonio, Texas: Institute of Electrical and Electronics Engineers, Inc.
  3. Jain, P., S.S., and Byon, E., “Wake Effect Parameter Calibration with Large-Scale Field Operational Data using Stochastic Optimization”, Applied Energy 2023.
  4. Jain, P., S.S., and Byon, E., “Robust Simulation Optimization with Stratification”, In Proceedings of the 2022 Winter Simulation Conference, edited by B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, and P. Lendermann. Orlando, Florida: Institute of Electrical and Electronics Engineers, Inc.
  5. Jain, P., S.S., and Byon, E., “Parameter Calibration with Stratified Adaptive Stochastic Trust-region Optimization”, 2021 INFORMS Workshop on Quality, Statistics, and Reliability 2021.
  6. Jain, P., S.S., and Byon, E. “Wake Effect Calibration in Wind Power Systems with Adaptive Sampling based Optimization”, Energy Systems Division of 2021 IISE Annual Conference and Expo Proceedings.

Kimia Vahdat

Kimia graduated from the ISE department of NCSU with a Ph.D. degree in Summer 2023. Her primary research combines uncertainty quantification methods within the Monte-Carlo methodology with data science problems. Mainly, she focuses on how better function evaluations can help with the model selection task. Currently she is a data scientist at Liberty Mutual Insurance. Feel free to check out her LinkedIn, Homepage, and GitHub to get to know her better! Publications:

  1. Alizadeh, N., Vahdat, K., S.S., Ozaltin, O., and Swann, J., “Interpretable Prediction Preventable Hospitalization due to Urinary Tract Infection”, Accepted in PLOS ONE.
  2. K. Vahdat and S.S. “Robust Model Output Analysis using Monte Carlo Methodology”, Expected Submission June 2024.
  3. K. Vahdat, S.S., “Adaptive Ranking and Selection Based Genetic Algorithms For Data-driven Problems”. In Proceedings of the 2023 Winter Simulation Conference, edited by C.G. Corlu, S.R. Hunter, H. Lam, B. S. Onggo, J. Shortle, and B. Biller. San Antonio, Texas: Institute of Electrical and Electronics Engineers, Inc.
  4. S.S., K. Vahdat, “Simulation Optimization based Feature Selection”. Optimization and Engineering (OPTE). 2022.
  5. K. Vahdat and S.S. “Nonparametric Uncertainty Bias and Variance Estimation Via Nested Bootstrapping and Influence Functions”. In Proceedings of the 2021 Winter Simulation Conference, edited by S. Kim, B. Feng, K. Smith, S. Masoud, Z. Zheng, C. Szabo, and M. Loper. Orlando, Florida: Institute of Electrical and Electronics Engineers, Inc.
  6. K. Vahdat and S.S. “Simulation optimization based feature selection, a study on data-driven optimization with input uncertainty”. In Proceedings of the 2020 Winter Simulation Conference, edited by K.-H. Bae, B. Feng, S. Kim, S. Lazarova-Molnar, Z. Zheng, T. Roeder, and R. Thiesing, 2149–2160. Orlando, Florida: Institute of Electrical and Electronics Engineers, Inc.
  7. L. Mao, K. Vahdat, S.S., J. Swann. “Personalized Predictions for Unplanned Urinary Tract Infection Hospitalizations with Hierarchical Clustering”. In Proceedings of the 2020 INFORMS Conference on Service Science.