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Jangho Park

JanghoPark
Jangho Park
Postdoctoral Scholar

Affiliation and Research Interests

I am a postdoctoral researcher in the Center for Computational Sciences and Engineering (CCSE) in the Computational Research Division of the Computing Sciences Directorate at the Lawrence Berkeley National Laboratory

Broadly, my research is on Decision Making under Uncertainty. More specifically, I work on various aspects of Stochastic Programming and Distributionally Robust Optimization and their applications to groundwater management, water allocation, and energy problems. As a part of the research, I work on Feedforward Neural Networks such as multilayer perceptron and Convolutional Neural Network to generate future scenarios for future decision making. I am involved in two projects Enabling Water-Energy Decision Support Using Watershed-scale Surrogate Models (LRDR) and Institute for the Design of Advanced Energy Systems (IDAES). The latter project develops python application with pyomo (pyomo: a collection of Python software packages for formulating optimization models) 

Previous

I received my PhD in Operations Research from the Ohio State University in May 2019. My dissertation research studied stochastic optimization, particularly variance reduction for sequential sampling procedure in stochastic programming and data-driven distributionally robust optimization with applications in water resources management.

 

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Manuscripts

  • J. Mueller, J.Park, R. Sahu, C. Varadharajan, B. Arora, B. Faybishenko, and D. Agarwal. Surrogate optimization of deep neural networks for groundwater predictionsSubmitted for publication, 2019

  • J. Park, R. Stockbridge, and G. Bayraksan. Variance reduction for sequential sampling in stochastic programming. Under revision, 2019 

  • J. Park, and G. Bayraksan. A multistage distributionally robust optimization approach to water allocation under climate uncertainty. Submitted for publication2019

  • J. Park, and G. Bayraksan. Simple estimators of value of data and price of data for data-driven distributionally robust optimization. Working Paper, January 2019

Journal Articles

Conference Talks

  • Simple estimators of value of data and price of data for data-driven distributionally robust optimization. In INFORMS Annual Meeting, Seattle, WA, October 20-23, 2019.

  • Variance reduction for sequential sampling in stochastic programming. In INFORMS Annual Meeting, Phoenix, AZ, November 4-7, 2018.

  • Data-driven distributionally robust water allocation under climate uncertainty. In INFORMS Annual Meeting, Houston, TX, October 22-25, 2017.

  • Variance reduction for sequential sampling in stochastic programming. In INFORMS Computing Society Conference, Austin, TX, January 15-17, 2017.

  • Variance reduction for sequential sampling in stochastic programming. In INFORMS Annual Meeting, Nashville, TN, November 13-16, 2016.

  • Variance reduction techniques for sequential sampling methods in stochastic programming. In INFORMS Annual Meeting, Philadelphia, PA, November 1-4, 2015.

  • An exponentially weighted moving average chart using multiple hypothesis testing. In 17th Conference on The Korean Institute of Plant Engineering, Pohang, South Korea, November 15-16, 2012.

  • A multivariate process control scheme using multiple hypothesis testing. In The Joint Conference of Korean Operations Research and Management Science Society and Korean Institute of Industrial Engineers, Gyeongju, South Korea, May 10-11, 2012.