Intern          2015.12    ~   2016.03                                Internship, Winter, 2015-2016             - Efficient graph partitioning on a distributed graph engine

                                                                                      Oracle Labs, Belmont, CA, USA            - Performance analysis and tuning of graph analytics and

  Scholarship

  Publications

- Kim, K., Kim, H., Fletcher, G., and Han, W., "Combining Sampling and Synopses with Worst-Case Optimal Runtime and Quality Guarantees for Graph Pattern Cardinality Estimation," In Proc., 47th Int'l Conf. on Management of Data, ACM SIGMOD, Xian, Shaanxi, China, June 2021

- Kim, H., So, B., Han, W., and Lee, H., "Natural language to SQL: Where are we today?," In 46th Int'l Conf. on Very Large Data Bases (VLDB) / Proc. the VLDB Endowment (PVLDB), Vol. 13, No. 10,  August 2020 (ISSN: 2150-8097).

- Kim, H., Lee, J., Bhowmick, S., Han, W., Lee, J., Ko, S., and Jarrah, M., "DualSim: Parallel Subgraph Enumeration in a Massive Graph on a Single Machine," In Proc. 42nd Int'l conf. on Management of Data, ACM SIGMOD, San Francisco, USA, June 2016.

-      Development of disk-based gradient descendant method (course project)

   In this project, I have developed a disk-based, gradient descendant algorithm on top of TurboGraph, which can be used for disk-based recommendation. During this project, I have analyzed important parts of source codes of TurboGraph.

-      Development of algorithms for a distributed, disk-based graph database engine

  In this project, I have proposed and developed a graph partitioning algorithm and a distributed and disk-based triangle counting algorithm on top of TurboGraph++. 

-      Development of a deep learning-based method for transforming large-scale unstructured data to structured data

  In this project, I have proposed and developed a technique for transforming unstructured web data (e.g., job posting) to structured data by using CNN and RNN.