Research Team

Yunsoo Ha

I am a fifth-year Ph.D. student in Industrial and System Engineering at NC State University. I completed my B.S. and M.S. in Logistics from the Korea Aerospace University. My research interests lie in algorithmic development of the stochastic (simulation) optimization methods and quantum computing. Currently, I am working on improving the adaptive sampling trust region optimization algorithm (ASTRODF) for various problem settings such as quantum problems and data augmentation (adversarial attack). [Linkedin, Github]

My publications:
  1. Ha, Y., S.S., “Consistency and Complexity of Adaptive Sampling based Trust-region Optimization”, In preparation.
  2. Ha, Y., S.S., “Refined Adaptive Sampling Trust-region Optimization For Stochastic Derivative-free Optimization using Direct Coordinate Search”, Under Review.
  3. 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.
  4. Menickelly, M., Ha, Y.,  Otten, M., “Latency considerations for stochastic optimizers in variational quantum algorithms”. arXiv preprint arXiv:2201.13438, 2022.
  5. 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.
  6. 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.
  7. 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.
  8. 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.

Kimia Vahdat

I am a fifth-year Ph.D. student at the ISE department of NCSU. My primary research combines uncertainty quantification methods within the Monte-Carlo methodology with data science problems. Mainly, I focus on how better function evaluations can help with the model selection task. Feel free to check out my LinkedIn, Homepage, and GitHub to get to know me better!

My publications:

  1. Alizadeh, N., Vahdat, K., S.S., Ozaltin, O., and Swann, J., “Interpretable Prediction Preventable Hospitalization due to Urinary Tract Infection”, In preparation.
  2. K. Vahdat and S.S. “Robust Model Output Analysis using Monte Carlo Methodology”, Under Review.
  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.

Pranav Jain

I am a fourth year Ph.D. student at the department of Industrial and Systems Engineering at NCSU. My research interests include simulation optimization, data-driven optimization, machine learning, and renewable energy. I am currently working on data-driven simulation optimization and its application in the field of renewable energy. My work focuses on using variance reduction techniques like stratified sampling within the simulation optimization framework to improve the performance of simulation optimization algorithms. Currently I am also working as 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. Feel free to check out my GitHub and connect with me on LinkedIn.

My publication:

  1. Jain, P., S.S., and Byon, E., “Stratified Adaptive Sampling with Trust-region Optimization”, In preparation.
  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”, Accepted in 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.

Ethan Houser

I am a second year MSIE student in the department of Industrial & Systems Engineering at NC State. I completed my B.S. in Industrial & Systems Engineering with a minor in Statistics from NC State as well. My research focus encompasses simulation optimization and machine learning while working with imbalanced datasets. My current work involves employing a simulation optimization based feature selection algorithm to train a predictive model with an imbalanced dataset produced during the Additive Manufacturing process of Electron Beam Melting (EBM). The goal for this model is to accurately predict when defects will occur and to provide manufacturing experts insight as to why these defects are occurring.

My publication:

  1. Houser, E., Sadeghi, A., S.S., “Robust Nested Partitioning for Feature Selection”, In preparation.
  2. Houser, E., S.S., Harrysson, O., “Predicting Defects in Additive Manufacturing with Simulation Optimization based Feature Selection for Imbalanced Data”, Under review at IISE Transactions.

Yongseok Jeon

Hi, I am a second-year Ph.D. student in the ISE department at North Carolina State University, joining Dr. Shashaani’s research group this semester (Fall 2022). I got my B.S. and M.S. degrees in Industrial Engineering from Sungkyunkwan University (SKKU), Korea. My research interests lie in the field of data science, machine learning, and optimization. To be specific, they focus on efficiency for big data problems. Currently, I am working on undersampling methods and their integration with stochastic optimization areas.

My publication:

  1. Jeon, Y.S. and Lim, D.J., 2020. “PSU: Particle stacking undersampling method for highly imbalanced big data”. IEEE Access, 8, pp.131920-131927.
  2. 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.