Students

• Neissrien Alhubieshi (2018- Fall 2022)
  Thesis title: Global Sensitivity Analysis with Surrogate Modeling
  Current position: King Khalid University, Abha, Asir, Saudi Arabia
• Isabel Corona Guevara (Spring 2022-)
  Research interest: Sparse Bayesian Learning, Uncertainty Quantification
• Michael Schmidt (Spring 2022-)
  Research interest: Markov Chain Monte Carlo Methods, Global Sensitivity Analysis
• Andrew Kitterman (Fall 2023-)
  Research interest: Machine Learning, Surrogate Modeling, Uncertainty Quantification
• Evan Shapiro (co-advised with Erin Austin) (Summer 2021-)
  Research interest: COPD Project, Surrogate Modeling

• Hanbyul Lee (to graduate in Fall 2023)
  Project topic: machine learning interpretability
• Anne Gumina (to graduate in Fall 2023)
  Project topic: importance sampling, surrogate modeling, reliability engineering
• Nhat Pham (graduated Summer 2023)
  Project topic: Reinforcement Learning, Q learner and Dyna
• Siu Yin Lee (graduated Spring 2022, Outstanding Master's Student)
  Project topic: Burned Area Mapping in Alaska: by Improving the Machine Learning Algorithm Established by USGS for Conterminous US in 2020
• Nick Koprowicz (co-advised with Erin Austin, graduated Spring 2021, CLAS Outstanding MS Graduate for Spring 2021)
  Project title: Building Computer Vision Models Efficiently: Transfer Learning with ImageNet Pre-trained Models
• Lu Vy (graduated Spring 2020 with Summa Cum Laude)
  Project title: Variance Reduction Methods Based on Multilevel Monte Carlo for Option Pricing
• Siyuan Lin (graduated Summer 2020)
  Project title: Application of Machine Learning Models to Microeconomic Analysis
• Dingxuan Zhang (graduated Spring 2020)
  Project title: Variance Reduction Methods Based on Multilevel Monte Carlo for Option Pricing
• Malik Odeh (graduated Spring 2020)
  Project title: Neuronal Bursting in Slow-Fast Systems