I am an Assistant Professor in the Graduate School of AI and the School of Electrical Engineering (Computer Division) at KAIST.
I received my Ph.D. in Computer Science from Carnegie Mellon University, where I was advised by Prof. Christos Faloutsos and supported by the KFAS Scholarship and the Siebel Scholar Fellowship.
I received my B.S. in Computer Science and Engineering and my B.A. in Economics from Seoul National University. My research interests include data mining, graph mining, and scalable machine learning.
At KAIST, I lead the Data Mining Lab.
Ph.D. in Computer Science
M.S. in Computer Science •Dec. 2017
B.S. in Computer Science and Engineering•Aug. 2015
B.A. in Economics (Double Major) •Aug. 2015
Assistant Professor • Feb. 2019 - Present
Research Intern • May. 2017 - Aug. 2017 & May. 2018 - Aug. 2018
Associate Researcher • Jan. 2011 - Dec. 2013
Instructor • Spring 2020 [ link ]
Instructor • Fall 2019 [ link ]
Instructor • Spring 2019 [ link ]
Teaching Assistant • Fall 2017 [ link ]
Teaching Assistant • Spring 2017 [ link ]
Se-eun Yoon, Hyungseok Song, Kijung Shin, and Yung Yi
TheWebConf 2020 (formerly WWW) (Short Paper) [ paper | appendix | code | bib ]
Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
ICDM 2016 [ paper | appendix | longer ver. [J4] | slides | code and datasets | bib ]
Selected as one of the best papers of ICDM 16 and invited for potential publication at the KAIS Journal
Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos
KDD 2016 [ paper | longer ver. [J3] | code | bib ]
KDD 2016 Best Paper Award [link], CogX 2017 Award for Best Student Paper in AI [link]
Media: NSF [link], WESA [link], TechXplore [link], Stanford Scholar [link], Crain's [link]
Kijung Shin, Jinhong Jung, Lee Sael, and U Kang
SIGMOD 2015 [ paper | longer ver. [J1] | slides | code and datasets | bib ]
Samsung Humantech Paper Award (1st in Computer Science) [link], Taught in courses: UMich (EECS 598), SIGMOD Student Travel Award
NetMiner is an application software for exploratory analysis and visualization of large network data based on SNA (Social Network Analysis). This tool allows researchers to explore their network data visually and interactively, helps them to detect underlying patterns and structures of the network.
[ web | wiki | free trial ] • Participation: Jan. 2011 - Dec. 2013