Rosen Center for Advanced Computing

I Luk Kim

I Luk Kim's Profile Photo

Research Scientist

I Luk Kim is a Research Scientist in the Scientific Solutions Group in RCAC. I Luk has extensive expertise in creating science gateways for scientific applications, including multiple web-based applications for hydrologic data and computational tools connected to high-performance computing and visualization resources.

I Luk's research interests include web and system security using program analysis techniques. In particular, his current research focus is finding security vulnerabilities in JavaScript web applications using static and dynamic analysis.

Education

  • B.E., Computer Software, Kwangwoon University, Korea
  • M.E., Information Security, Korea University, Korea
  • Ph.D., Computer Science, Purdue University

Selected Publications

  • Kim, I. L., Zhao, L., Song, C., Neo, W. S., & Kelleher, B. (2024). Japper: A Comprehensive Framework for Streamlining Jupyter-Based Scientific Web Application Development. In Practice and Experience in Advanced Research Computing 2024: Human Powered Computing (pp. 1-4).
  • Rajib, A., Kim, I. L., Ercan, M. B., Merwade, V., Zhao, L., Song, C., & Lin, K. H. (2022). Cyber-enabled autocalibration of hydrologic models to support Open Science. Environmental Modelling & Software, 158, 105561.
  • Brewer, N., Campbell, R., Kalyanam, R., Kim, I. L., Song, C. X., & Zhao, L. (2022, October). Benefits and Limitations of Jupyter-based Scientific Web Applications. In 2022 IEEE 18th International Conference on e-Science (e-Science) (pp. 542-550). IEEE.
  • Merwade, V., Rajib, A., Kim, I. L., Zhao, L., & Song, C. (2022). The Rise of Cyberinfrastructure for Environmental Applications. Resource Magazine, 29(4), 33-35.
  • Nassar, A., Torres-Rua, A., Merwade, V., Dey, S., Zhao, L., Kim, I. L., ... & Mcelrone, A. J. (2021, April). Development of high performance computing tools for estimation of high-resolution surface energy balance products using sUAS information. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI (Vol. 11747, p. 117470K). International Society for Optics and Photonics.
  • Rajib, A., Kim, I. L., Golden, H. E., Lane, C., & Kumar, S. V. (2020, December). Effect of Remotely Sensed Vegetation in Hydrology and Water Quality Predictions: New Evidence from Large-scale Watershed Modeling. In AGU Fall Meeting Abstracts (Vol. 2020, pp. H038-0011).
  • Rajib, A., Kim, I. L., Golden, H. E., Lane, C. R., Kumar, S. V., Yu, Z., & Jeyalakshmi, S. (2020). Watershed modeling with remotely sensed big data: MODIS leaf area index improves hydrology and water quality predictions. Remote Sensing, 12(13), 2148.
  • Kalyanam, R., Zhao, L., Song, C., Biehl, L., Kearney, D., Kim, I. L., ... & Merwade, V. (2019). MyGeoHub—A sustainable and evolving geospatial science gateway. Future Generation Computer Systems, 94, 820-832.
  • Zhao, L., Song, C. X., Kalyanam, R., Biehl, L., Campbell, R., Delgass, L., Kim, I. L., ... & Ellis, C. (2017, June). GABBs-Reusable Geospatial Data Analysis Building Blocks for Science Gateways. In IWSG.
  • Merwade, V., Zhao, L., Song, C. X., Tarboton, D. G., Goodall, J. L., Stealey, M., ... & Kim, I. L. (2016, December). Bridging Hydroinformatics Services Between HydroShare and SWATShare. In AGU Fall Meeting Abstracts (Vol. 2016, pp. H41B-1332).
  • Rajib, M. A., Merwade, V., Song, C., Zhao, L., Kim, I. L., & Zhe, S. (2014, December). SWATShare-A Platform for Collaborative Hydrology Research and Education with Cyber-enabled Sharing, Running and Visualization of SWAT Models. In AGU Fall Meeting Abstracts (Vol. 2014, pp. H51K-0752).

Additional information and news on I Luk's group and projects can be found at RCAC Scientific Solutions Group