Kijung Shin (신기정)

PhD Student, Computer Science Department
Carnegie Mellon University

About Me

Hello! I am a second-year Ph.D. student majoring in Computer Science at Carnegie Mellon University. I am fortunate to be advised by Prof. Christos Faloutsos. I received B.S. in Computer Science and Engineering and B.A. in Economics at Seoul National University. My research interests include large-scale data mining, social network analysis, and big data analytics systems.

Contact Details

Email: kijungs (at)
Tel.: +1-(213)-910-5980
Dept. of Computer Science, GHC 9005
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213


Carnegie Mellon University

Ph.D. in Computer Science Aug. 2015 - Present

Seoul National University

B.S. in Computer Science and Engineering,
B.A. in Economics (double major) Mar. 2008 - Aug. 2015

Work Experience

Seoul National University

Research Intern (Part-time) Jan. 2015 - Jun. 2015

Korea Advanced Institute of Science and Technology (KAIST)

Undergraduate Researcher Jan. 2014 - Aug. 2014

Cyram Inc.

Associate Researcher Jan. 2011 - Dec. 2013


2017 or Later

Why You Should Charge Your Friends for Borrowing Your Stuff

Kijung Shin, Euiwoong Lee, Dhivya Eswaran, and Ariel Procaccia
26th International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne, Australia (To Appear)
[paper | slides | www (code and datasets) | bib]

Graph-Based Fraud Detection in the Face of Camouflage

Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, and Christos Faloutsos
ACM Transactions on Knowledge Discovery from Data (TKDD) (Accepted)
[paper | code | bib]

D-Cube: Dense-Block Detection in Terabyte-Scale Tensors

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
ACM International Conference on Web Search and Data Mining (WSDM) 2017, Cambridge, UK
[paper | appendix | slides | www (code and datasets) | bib] SIGIR Student Travel Grant

S-HOT: Scalable High-Order Tucker Decomposition

Jinoh Oh, Kijung Shin, Evangelos E. Papalexakis, Christos Faloutsos, and Hwanjo Yu
ACM International Conference on Web Search and Data Mining (WSDM) 2017, Cambridge, UK
[paper | www (code) | bib]

Fully Scalable Methods for Distributed Tensor Factorization

Kijung Shin, Lee Sael, and U Kang
IEEE Transactions on Knowledge and Data Engineering (TKDE) 2017
[paper | appendix | www (code and datasets) | bib]


CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms

Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
IEEE International Conference on Data Mining (ICDM) 2016, Barcelona, Spain
[paper | appendix | slides | www (code and datasets) | bib]
Invited to Knowledge and Information Systems

M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees

Kijung Shin, Bryan Hooi, and Christos Faloutsos
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 2016, Riva del Garda, Italy
[paper | appendix | slides | www (code and datasets) | bib]

FRAUDAR: Bounding Graph Fraud in the Face of Camouflage

Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, and Christos Faloutsos
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2016, San Francisco, USA
[paper | journal | code | bib] KDD Best Research Paper Award (Winner)

Random Walk with Restart on Large Graphs Using Block Elimination

Jinhong Jung, Kijung Shin, Lee Sael, and U Kang
ACM Transactions on Database Systems (TODS) 2016
[paper | www (code and datasets) | bib]

Incorporating Side Information in Tensor Completion

Hemank Lamba*, Vaishnavh Nagarajan*, Kijung Shin*, and Naji Shajarisales*
25th International Conference on World Wide Web (WWW) 2016, Montreal, Canada (Poster)
[paper | bib]


Scalable Methods for Random Walk with Restart and Tensor Factorization

Kijung Shin
Bachelor's Thesis, Dept of Computer Science and Engineering, Seoul National University, 2015
[paper] Excellent CSE Thesis Award

BEAR: Block Elimination Approach for Random Walk with Restart on Large Graphs

Kijung Shin, Jinhong Jung, Lee Sael, and U Kang
ACM SIGMOD International Conference on Management of Data (SIGMOD) 2015, Melbourne, Australia
[paper | journal | slides | www (code and datasets) | bib]
Samsung Humantech Paper Award (1st in Computer Science), SIGMOD Student Travel Award


Distributed Methods for High-dimensional and Large-scale Tensor Factorization

Kijung Shin and U Kang
IEEE International Conference on Data Mining (ICDM) 2014, Shenzhen, China
[paper | journal | slides | www (code and datasets) | bib] ICDM Student Travel Award

Data/Feature Distributed Stochastic Coordinate Descent for Logistic Regression

Dongyeop Kang, Woosang Lim, Kijung Shin, Lee Sael, and U Kang
23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM) 2014, Shaghai, China
[paper | appendix | bib]


NetMiner 4 - Social Network Analysis Software

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.
[www | wiki | free trial] Participation: Jan. 2011 - Dec. 2013

Dolphin - Machine Learning Platform on Top of Apache REEF

Dolphin is a machine learning platform built on top of Apache REEF. Dolphin consists of a BSP-style machine learning framework (dolphin-bsp), a deep learning framework (dolphin-dnn), and a parameter server module (dolphin-ps).
[Github repo] Participation: Jan. 2015 - Jun. 2015

Teaching Experience

Graduate Artificial Intelligence,
CMU 15-780, Spring 2017 [www]

Graduate Coursework

Spectral Graph Theory and The Laplacian Paradigm,
CMU 15-859N, Fall 2016 [www]
Types and Programming Languages,
CMU 15-814, Fall 2016 [www]
Graduate Artificial Intelligence,
CMU 15-780, Spring 2016 [www]
Multimedia Databases and Data Mining,
CMU 15-826, Spring 2016 [www]
Advanced Introduction to Machine Learning,
CMU 10-715, Fall 2015 [www]
Algorithms in the Real World,
CMU 15-853, Fall 2015 [www]

Online Coursework

Introduction to Recommender Systems,
Coursera, Aug. 2015 [certificate]
Scalable Machine Learning,
edX, Aug. 2015 [certificate]
Networks, Crowds, and Markets,
edX, May. 2014 [certificate]
Statistical Learning,
Stanford Online, Apr. 2014 [certificate]
Introduction to Parallel Programming,
Udacity, Jan. 2014 [certificate]
Introduction to Theoretical Computer Science,
Udacity, Dec. 2013 [certificate]
Introduction to Logic,
Coursera, Dec. 2012 [certificate]
Social Network Analysis,
Coursera, Nov. 2012 [certificate]
Networked Life,
Coursera, Oct. 2012 [certificate]
Udacity, Aug. 2012 [certificate]
Web Application Engineering,
Udacity, Jun. 2012 [certificate]
Intro to statistics,
Udacity, Aug. 2012 [certificate]
Programming Languages,
Udacity, Jun. 2012 [certificate]
Design of Computer Programs,
Udacity, Jun. 2012 [certificate]
Model Thinking,
Coursera, May. 2012 [certificate]
Design and Analysis of Algorithms, Part 1,
Coursera, Apr. 2012 [certificate]
Artificial Intelligence for Robotics,
Udacity, Apr. 2012 [certificate]
Intro to Computer Science,
Udacity, Apr. 2011 [certificate]
Software Engineering for Software as a Service,
Coursera, Mar. 2012 [certificate]
Introduction to Database,
Standford Engineering, Dec. 2011 [certificate]
Introduction to Artificial Intelligence,
Standford Engineering, Dec. 2011 [certificate]
Machine Learning,
Standford Engineering, Dec. 2011 [certificate]