Kijung Shin (신기정)

PhD Student, Computer Science Department
Carnegie Mellon University


About Me

I am a third-year Ph.D. student majoring in Computer Science at Carnegie Mellon University.
My research interests include large-scale data mining, social network analysis, and big data analytics systems. I am fortunate to be advised by Prof. Christos Faloutsos and supported by
KFAS Scholarship.

I received B.S. in Computer Science and Engineering and B.A. in Economics at Seoul National University. My undergraduate research was advised by Prof. U Kang and Prof. Byung-Gon Chun.

Contact Details

Email: kijungs (at) cs.cmu.edu
WWW: http://kijungshin.com
Address:
Dept. of Computer Science, GHC 9005
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213

Education

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

Carnegie Mellon University

Research Assistant Aug. 2015 - Present

LinkedIn

Machine Learning and Relevance Engineer Intern May. 2017 - Aug. 2017

Seoul National University

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

Korea Advanced Institute of Science and Technology (KAIST)

Research Intern Jan. 2014 - Aug. 2014

CYRAM

Associate Researcher Jan. 2011 - Dec. 2013

Publications

[ Google Scholar | DBLP | Research Gate]

2017 or Later

[C13]
WRS: Waiting Room Sampling for Accurate Triangle Counting in Real Graph Streams

Kijung Shin
IEEE International Conference on Data Mining (ICDM) 2017, New Orleans, USA
[paper | appendix | slides | www (code and datasets) | bib]

[C12]
ZooRank: Ranking Suspicious Entities in Time-Evolving Tensors

Hemank Lamba, Bryan Hooi, Kijung Shin, Christos Faloutsos, and Jürgen Pfeffer
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
(ECML-PKDD) 2017, Skopje, Macedonia
[paper | code and datasets | bib]

[C11]
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams

Kijung Shin, Bryan Hooi, Jisu Kim, and Christos Faloutsos
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2017, Halifax, Canada
[paper | appendix | poster | video | www (code and datasets) | bib]

[C10]
Why You Should Charge Your Friends for Borrowing Your Stuff

Kijung Shin, Euiwoong Lee, Dhivya Eswaran, and Ariel D. Procaccia
26th International Joint Conference on Artificial Intelligence (IJCAI) 2017, Melbourne, Australia
[paper | slides | bib] Featured in "New Scientist"

[C9]
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

[C8]
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]

[J5]
Fast, Accurate and Flexible Algorithms for Dense Sub-Tensor Mining

Kijung Shin, Bryan Hooi, and Christos Faloutsos
ACM Transactions on Knowledge Discovery from Data (TKDD) (To Appear)
[paper | shorter ver. [C6] | www (code and datasets) | bib]

[J4]
Patterns and Anomalies in k-Cores of Real-World Graphs with Applications

Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
Knowledge and Information Systems (KAIS) (To Appear)
[paper | shorter ver. [C7] | www (code and datasets) | bib]

[J3]
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), vol. 11, no. 4, pp. 44:1-44:26, June 2017
[paper | shorter ver. [C5] | code | bib] Special Issue on the Best Papers from KDD 2016

[J2]
Fully Scalable Methods for Distributed Tensor Factorization

Kijung Shin, Lee Sael, and U Kang
IEEE Transactions on Knowledge and Data Engineering (TKDE), vol. 29, no. 1, pp. 100-113, January 2017
[paper | appendix | shorter ver. [C2] | www (code and datasets) | bib]

[O2]
Patterns and Anomalies in k-Cores of Real-world Networks

Kijung Shin, Tina Eliassi-Rad, and Christos Faloutsos
International School and Conference on Network Science (NetSci) 2017, Indianapolis, USA (Abstract)
[abstract | longer ver. [C7] | longest ver. [J4]]


2016

[C7]
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 | longer ver. [J4] | slides | www (code and datasets) | bib]
Invited to Knowledge and Information Systems

[C6]
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 | longer ver. [J5] | slides | www (code and datasets) | bib]

[C5]
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 | longer ver. [J3] | code | bib] KDD Best Research Paper Award (Winner)

[C4]
Incorporating Side Information in Tensor Completion

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

[J1]
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), vol. 41, no. 2, pp. 12:1-12:43, June 2016
[paper | shorter ver. [C3] | www (code and datasets) | bib]


2015

[C3]
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 | longer ver. [J1] | slides | www (code and datasets) | bib]
Samsung Humantech Paper Award (1st in Computer Science), SIGMOD Student Travel Award

[O2]
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


2014

[C2]
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 | longer ver. [J2] | slides | www (code and datasets) | bib] ICDM Student Travel Award

[C1]
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]

Software

[ GitHub]

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

TA
of
10-601 Introduction to Machine Learning,
Fall 2017 [www]
TA
of
15-780 Graduate Artificial Intelligence,
Spring 2017 [www]

Graduate Coursework

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