Edutation

Work Experience

  • Hanyang University (Ansan, Gyeonggi, South Korea)
    • Assistant Professor in the School of Business Administration (Sep. 2022 - present)
  • Argonne National Laboratory (Lemont, IL, USA)
    • Postdoctoral Appointee in the Mathematics and Computer Science Division (Jul. 2020 - Jul. 2022)
  • Nokia Bell Labs (Naperville, IL, USA)
    • Summer Research Intern (Jun. 2018 - Aug. 2018)
  • Hyundai Mobis, Hyundai Motor Group (Seoul, South Korea)
    • Specialist in Quality Department (Dec. 2011 - Jan. 2013)

Publications

Working Papers

  • Deep Learning Based KRW/USD Exchange Rate Prediction
  • Efficient Scheduling Strategy for Semiconductor Manufacturing based on Reinforcement Learning
  • Multi‑Objective Optimization for Efficient and Clean Power‑Train Systems using the Non Dominated Sorting Genetic Algorithm
  • Model Order Reduction of EFIT Equilibrium Reconstruction with Deep Neural Networks
  • Improved Future Wildfire Danger Prediction using Combined Convolutional and Recurrent Neural Networks

Refereed Journal Articles

  • J. Koo, D. Klabjan, J. Utke, An inverse classification framework with limited budget and maximum number of perturbed samples, Expert Systems With Applications, vol. 212, 2023. 10.1016/j.eswa.2022.118761
  • L. Lao, S. Kruger, C. Akcay, P. Balaprakash, T Bechtel, E. Howell, J. Koo, J. Leddy, M. Leinhauser, Y. Liu, S. Madireddy, J. McClenaghan, D. Orozco, A. Pankin, D. Schissel, S. Smith, X. Sun, S. Williams, and the EFIT-AI Team, Application of Machine Learning and Artificial Intelligence to Extend EFIT Equilibrium Reconstruction, Plasma Physics and Controlled Fusion, vol. 64 (7), 2022. 10.1088/1361-6587/ac6fff
  • J. Koo, S. Hwang, A Unified Defect Pattern Analysis of Wafer Maps Using Density-Based Clustering, IEEE Access, vol. 9, 2021, pp. 78873-78882. 10.1109/ACCESS.2021.3084221

Refereed Proceedings

  • T. Randall*, J. Koo*, B. Videau, M. Kruse, X. Wu, P. Hovland, M. Hall, R. Ge, and P. Balaprakash*, Transfer-Learning-Based Autotuning Using Gaussian Copula, ACM International Conference on Supercomputing (ICS), 2023, pp. 37-49. 10.1145/3577193.3593712 (*Authors contributed equally to this work)
  • X. Wu, P. Balaprakash, M. Kruse, J. Koo, B. Videau, P. Hovland, V. Taylor, B. Geltz, S. Jana, and M. Hall, ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales, Cray User Group Conference (CUG), 2023. 10.48550/arXiv.2303.16245
  • M. Dorier, R. Egele, P. Balaprakash, J. Koo, S. Madireddy, A. D. Malony, S. Ramesh, R. Ross, HPC Storage Service Autotuning using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization, IEEE International Conference on Cluster Computing (CLUSTER), 2022, pp. 381-393. 10.1109/CLUSTER51413.2022.00049
  • J. Koo, P. Balaprakash, M. Kruse, X. Wu, P. Hovland, M. Hall, Customized Monte Carlo Tree Search for LLVM/Polly’s Composable Loop Optimization Transformations, 12th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), 2021, pp. 82-93. 10.1109/PMBS54543.2021.00015 [code]
  • A. Yanguas-Gil, J. Koo, S. Madireddy, P. Balaprakash, J. W. Elam, A. U. Mane, Neuromorphic Architectures for Edge Computing under Extreme Environments, IEEE Space Computing Conference (SCC), 2021, pp. 39-45. 10.1109/SCC49971.2021.00012
  • J. Koo, D. Klabjan, J. Utke, Combined Convolutional and Recurrent Neural Networks for Hierarchical Classification of Images, IEEE International Conference on Big Data, 2020, pp. 1354-1361. 10.1109/BigData50022.2020.9378237 [code] [slide]
  • J. Koo, D. Klabjan, Improved Classification Based on Deep Belief Networks, 29th International Conference on Artificial Neural Networks (ICANN), Lecture Notes in Computer Science. Springer, vol. 12396, 2020, pp 541–552. 10.1007/978-3-030-61609-0_43
  • J. Koo, V. B. Mendiratta, M. R. Rahman, A. Walid, Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics, 15th International Conference on Network and Service Management, 2019. 10.23919/CNSM46954.2019.9012702
  • B. Yum, J. Koo, S. Kim, Analysis of Defective Patterns on Wafers in Semiconductor Manufacturing: A Bibliographical Review, IEEE International Conference on Automation Science and Engineering, 2012, pp. 86-90. 10.1109/CoASE.2012.6386471

Projects

  • 2023-2026, Principal Investigator, Explainable and interpretable AI systems based on deep learning for classification, National Research Foundation of Korea (한국연구재단 기초연구사업-기본연구)
  • 2021-2022, Postdoc Researcher, EFIT‐AI: ML/AI Assisted Tokamak Equilibrium Reconstruction, DOE FES Project
  • 2020-2022, Postdoc Researcher, PROTEAS‐TUNE: Programming Toolchain for Emerging Architectures and Systems, DOE ASCR Exascale Computing Project
  • 2020-2021, Postdoc Researcher, Circuit AI, Laboratory Directed Research and Development program at Argonne National Laboratory
  • 2016-2020, Researcher, Projects with Allstate Insurance Company, University-Industry collaborative projects at Northwestern University

Talks

  • 사회적경제와 인공지능–심층학습중심으로, 사회적경제혁신리더 과정 at Hanyang University, Ansan, South Korea, Nov. 2023
  • Topics in Deep Learning Classification, Brown-Bag Seminar at Hanyang University Management Research Institute, Ansan, South Korea, Nov. 2022
  • AI for Performance: Customized Monte Carlo Tree Search for LLVM/Polly’s Composable Loop Optimization Transformations, Seminar at RAPIDS2 Project group, Virtual. Apr. 2022
  • Model Order Reduction of EFIT with Deep Neural Networks, Invited talk to ML working group at Plasma Science and Fusion Center, Massachusetts Institute of Technology (MIT), Virtual. Apr. 2022
  • Customized Monte Carlo Tree Search for LLVM/Polly’s Composable Loop Optimization Transformations, Postdoctoral Research and Career Symposium, Argonne National Lab, Virtual. Nov. 2021
  • Ytop/SuRF: Machine-Learning-Based Search for Autotuning, Exascale Computing Project Annual Meeting, Virtual. Apr. 2021
  • Mixed Integer Optimization for Prescriptive Analytics, Analytics Center of Excellence brown-bag event, Allstate, Northbrook, IL, USA. Feb. 2020
  • Deep Belief Network for Classification and Hierarchical Classification of Images, Candidate Presentation at Mathematics and Computer Science Division, Argonne National Lab, Lemont, IL, USA. Jan. 2020
  • Improved Classification Based on Deep Belief Networks, Midwest Machine Learning Symposium, Chicago, IL, USA. Aug. 2017
  • Models for Classification with Deep Belief Networks, INFORMS Optimization Society Conference, Princeton, NJ, USA. Mar. 2016

Teaching

Hanyang University (Ansan, Gyeonggi, South Korea)

  • Instructor, BUS5033 Operations Management Simulation (Spring 2024)
  • Instructor, BAS3003 Operations Analytics (Spring 2023, 2024)
  • Instructor, KDI0003 Digital Production Management and Manufacturing (Spring 2023)
  • Instructor, BUS2013 Operations Management (Fall 2022, 2023)
  • Instructor, BUS1008 Advanced Statistics for Business (Fall 2022, 2023)

Northwestern University (Evanston, IL, USA)

  • TA/lab instructor, MSIA-423 Analytics Value Chain (Spring 2020)
  • TA/lab instructor, MSIA-432 Deep Learning (Spring 2018)
  • TA, IEMS-393-2 IE Design Project (Winter 2020)
  • TA, IEMS-310 Operations Research (Spring 2017)
  • TA, IEMS-385 Introduction to Health Systems Management (Fall 2015)

Skills

  • Programming Languages
    • Python, C++, MATLAB, R, AMPL
  • Tools
    • Git, Linux, Vim, LaTex, Docker, Kubernetes, Spack
  • ML-Software/libraries
    • PyTorch, Tensorflow, Keras, Caffe, Theano, Scikit-Learn, Gym, Ytopt, DeepHyper, GPTune