Research Team

Current Members

Giovanni Amici

Giovanni is a postdoctoral scholar who started with the group in October 2025. He received his PhD degree in Pure and Applied Mathematics at Politecnico di Torino (Italy) in April 2025.  His research interests include quantitative finance, stochastic processes, and optimization. Currently, he is working on adaptive sampling strategies to increase the efficiency of trust region algorithms, both with and without the presence of constraints. Publications:

  1. Amici, G., S.S., and Jain, P., Stratified adaptive sampling for trust‐region optimization: complexity and applications. Working paper.
  2. Amici, G., Fusai, G., and Gambaro, A., Functional principal component analysis for risk neutral densities in Bayes spaces. Working paper.
  3. Amici, G., Brandimarte, P., and Fadda, E., A review of operations research models for hedging financial derivatives. Under review.
  4. Amici, G., Fusai, G., Gambaro, A., and Marazzina, D., Navigating supply shocks: sector resilience and production prices through dynamic input‐output modeling. Under review.
  5. Zhang, C., Amici, G., and Morandotti, M., Calibrating the Heston model with deep differential networks. Decisions in Economics and Finance (2025): 1-23.
  6. Amici, G., Brandimarte, P., Messeri, F., and Semeraro, P., Multivariate Lévy models: calibration and pricing. OR Spectrum (2025): 1-42.
  7. Amici, G., Ballotta, L., and Semeraro, P., Multivariate additive subordination with applications in finance. European Journal of Operational Research 321, no. 3 (2025): 1004-1020.

Yongseok Jeon

Yongseok is a fifth-year Ph.D. candidate 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., S.S., Byon, E., and Jain, P., Dynamic Digital Twin Calibration with Stochastic Simulation. Working Paper.
  2. Jeon, Y., and S.S., Rapid Calibration with Root Finding. Under Review.
  3. Jeon, Y., S.S., Byon, E., and Jain, P., Dynamic Calibration of Digital Twins via Stochastic Simulation: A Wind Energy Case Study, Proceedings of the 2025 Winter Simulation Conference.
  4. Jeon, Y., and S.S., Digital Twin Calibration with Root Finding and Bayesian Optimization, Proceedings of the 2024 Winter Simulation Conference.
  5. Jeon, Y., Pasupathy, P., and S.S., Statistical Inference on Simulation Output: Batching as an Inferential Device, Journal of Simulation (2025)
  6. 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
  7. Jeon, Y.S. and Lim, D.J., 2020. “PSU: Particle stacking undersampling method for highly imbalanced big data”. IEEE Access, 8, pp.131920-131927.
  8. 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.

Hyungkhee Eun

Hyungkhee (HK) is a fourth-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., Mendelson, G., and Vahdat, K., Robust Output Analysis with Monte-Carlo Methodology. Working Paper.
  2. Eun, H.K., S.S., and Barton, R.R., Robust Decision-making in Queuing Systems. Working Paper.
  3. Eun, H.K., S.S., and Barton, R.R., Worst-case Approximations for Robust Analysis in Multiserver Queues and Queuing Networks, Proceedings of the 2025 Winter Simulation Conference.
  4. Eun, H.K., S.S., and Barton, R.R., Identifying Input Uncertainty Induced Bias Using Wasserstein and Kingman Metrics, Proceedings of the 2024 Winter Simulation Conference.

Nicole Felice

Nicole is a second-year 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., and Roberts, L., Derivative-free Stochastic Optimization with Deterministic Constraints. Working Paper.
  2. Felice, N., S.S., Eckman, D.E., and Henderson, S., Evaluation and Comparison of Stochastically Constrained Optimization Solvers. Under Review.
  3. Felice, N., S.S., Eckman, D.E., and Sanchez, S.S., Data Farming for Repeated Simulation Optimization Experiments, 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 Cornell University. [Linkedin, Github]. Publications:

  1. Ha, Y., S.S., and Tran-Dinh, Q., Adaptive Quadratic Regularization with Stochastic Trust Region Optimization. Working Paper.
  2. Ha, Y., and Mueller, J., Adaptive Sampling-Based Bi-Fidelity Stochastic Trust Region Method for Derivative-Free Stochastic Optimization, Under Review.
  3. Ha, Y., and Mueller, J., Multi-Fidelity Stochastic Trust Region Method with Adaptive Sampling, In Proceedings of the 2025 Winter Simulation Conference.
  4. Ha, Y., S.S., and Menickelly, M., “Two-stage Sampling and Variance Modeling for Variational Quantum Algorithms”, INFORMS Journal of Computing (2025).
  5. Ha, Y., S.S., and Pasupathy, R., Complexity of Zeroth- and First-order Stochastic Trust-Region Algorithms, SIAM Journal of Optimization (2025).
  6. Ha, Y., S.S., “Iteration Complexity and Finite-Time Efficiency of Adaptive Sampling Trust-Region Methods for Stochastic Derivative-Free Optimization”, IISE Transactions (2005).
  7. Y. Ha, S.S., “Towards Greener Stochastic Derivative-free Optimization with Trust Regions and Adaptive Sampling”. In Proceedings of the 2023 Winter Simulation Conference.
  8. Menickelly, M., Ha, Y.,  Otten, M., Latency Considerations for Stochastic Optimizers in Variational Quantum Algorithms. Quantum (2023).
  9. 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.
  10. 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 (2019).
  11. Ha, Y., and Chae, J., Free Balancing for a Shuttle-based Storage and Retrieval System”, Simulation Modeling Practice and Theory (2018).
  12. 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 (2016).

Pranav Jain

Pranav graduated with a Ph.D. degree from the department of Industrial and Systems Engineering at NCSU in Fall 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 a Data Scientist at the Duke Energy in Charlotte, NC. Feel free to check out his GitHub and connect with him on LinkedIn. Publications:

  1. Jain, P., S.S., and Byon, E., Simulation Model Calibration with Dynamic Stratification and Adaptive Sampling, Journal of Simulation (2025).
  2. Jain, P., S.S., Dynamic Stratification and Post-stratified Adaptive Sampling for Simulation Optimization. In Proceedings of the 2023 Winter Simulation Conference.
  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.
  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.
  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.

Ethan Houser

Ethan graduated with a Master’s degree from 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 work involved developing new feature selection algorithms that are convergent and fast. Publications:

  1. Houser, E. and S.S., Rapid Screening and Nested Partitioning for Feature Selection, In Proceedings of 2024 Winter Simulation Conference.
  2. Houser, E., S.S., Harrysson, O., and Jeon, Y., Predicting Additive Manufacturing Defects with Feature Selection for Imbalanced Data, IISE Transactions (2023).

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, PLoS ONE (2024).
  2. Vahdat, K. and S.S., Adaptive Ranking and Selection Based Genetic Algorithms For Data-driven Problems. In Proceedings of the 2023 Winter Simulation Conference.
  3. S.S., K. Vahdat, Simulation Optimization based Feature Selection. Optimization and Engineering (2022).
  4. Vahdat, K. and S.S., Nonparametric Uncertainty Bias and Variance Estimation Via Nested Bootstrapping and Influence Functions”. In Proceedings of the 2021 Winter Simulation Conference.
  5. Vahdat, K. 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.
  6. 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.