About Me

I research on machine learning. I design, analyze, and implement machine learning algorithms to extract actionable insights and to build personalized data products. I enjoy algorithms designed with mathematical rigor and realized with principled software engineering. I worked at Faire Wholesale, Netflix, and Nokia on personalizations and search. I received M.S. and Ph.D. in Computer Science from the College of Computing at Georgia Tech, advised by Haesun Park, where I studied machine learning and numerical methods. I spent a year at the Imaging Media Research Center of the Korea Institute of Science and Technology after completing B.S. in Computer Science and Engineering at Seoul National University. I grew up in South Korea and currently live in San Jose. I enjoy running, yoga, reading history, and playing go in my free time.

안녕하세요. 저는 기계학습 분야 연구자입니다. 기계학습 알고리즘을 설계, 구현, 분석하고 실험을 통해 개선하는 일을 합니다. 수학적으로 엄밀한 알고리즘을 소프트웨어 공학을 통해 튼튼하게 구현하는 것을 추구합니다. 페어, 넷플릭스, 노키아에서 개인화와 검색분야 연구및 제품구현 일을 했습니다. 조지아 공대에서 박혜선 교수님 지도아래 컴퓨터 과학 박사/석사 학위를, 서울대 컴퓨터공학부에서 학사 학위를 받았습니다. 현재 산호세에 살고 있습니다. 

 Publications
See also Google Scholar page

2021

• Fairness among New Items in Cold Start Recommender Systems.
  Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee
  In Proceedings of the 44th International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 767-776, 2021
  [PDF] [URL]

2015

• Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization.
  Hannah Kim, Jaegul Choo, Jingu Kim, Chandan Reddy, and Haesun Park
  In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 567-576, 2015
  [PDF] [URL] 

 2014

• Conditional Log-linear Models for Mobile Application Usage Prediction.
  Jingu Kim and Taneli Mielikäinen.
  In Proceedings of the 2014 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), LNCS Volume 8724, pp. 672-687, 2014.
  [PDF] [URL]

• Algorithms for Nonnegative Matrix and Tensor Factorizations: A Unified View Based on Block Coordinate Descent Framework.
  Jingu Kim, Yunlong He, and Haesun Park.
  Journal of Global Optimization, 58(2), pp. 285-319, 2014.
  [PDF] [URL]

2013

•  Regularization Paths for Sparse Nonnegative Least Squares Problems with Applications to Life Cycle Assessment Tree Discovery.
  Jingu Kim, Naren Ramakrishnan, Manish Marwah, Amip Shah, and Haesun Park.
  In Proceedings of the 2013 Thirteenth IEEE International Conference on Data Mining (ICDM), pp. 360-369, 2013.
  [PDF]

2012

•  Command Generation Techniques for a Pin Array Using the SVD and the SNMF.
  Ryder C. Winck, Jingu Kim, Wayne J. Book, and Haesun Park.
  In Proceedings of the 10th IFAC Symposium on Robot Control (SYROCO), Dubrovnik, Croatia, 2012

• A Control Loop Structure Based on Semi-Nonnegative Matrix Factorization for Input-Coupled Systems.
  Ryder C. Winck, Jingu Kim, Wayne J. Book, and Haesun Park.
  In Proceedings of the 2012 American Control Conference (ACC), Montreal, Canada, 2012

• Group Sparsity in Nonnegative Matrix Factorization.
  Jingu Kim, Renato Monteiro, and Haesun Park.
  In Proceedings of the 2012 SIAM International Conference on Data Mining (SDM), pp. 851-862, 2012
  [PDF] 

• Fast Variational Mode-Seeking.
  Bo Thiesson and Jingu Kim.
  In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2012, JMLR: W&CP 22, pp. 1230-1242, La Palma, Canary Islands, Apr. 21-23, 2012.
  [PDF] 

• Fast Nonnegative Tensor Factorization with an Active-set-like Method.
  Jingu Kim and Haesun Park.
  In High-Performance Scientific Computing: Algorithms and Applications, Springer, pp. 311-326, 2012.
  [PDF] [URL] [SOFTWARE] 

2011

•  Nonnegative Matrix and Tensor Factorizations, Least Squares Problems, and Applications.
  Jingu Kim.
  Ph.D. Thesis, Georgia Institute of Technology, 2011.
  [PDF] [URL] 

• Fast Nonnegative Matrix Factorization: An Active-set-like Method And Comparisons.
  Jingu Kim and Haesun Park.
  SIAM Journal on Scientific Computing (SISC), 33(6), pp. 3261-3281, 2011.
  [PDF] [URL] [SOFTWARE] 

• Statistical Optimization of Non-Negative Matrix Factorization.
  Anoop Korattikara, Levi Boyles, Max Welling, Jingu Kim, and Haesun Park.
  In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2011, JMLR: W&CP 15, pp. 128-136, Fort Lauderdale, FL, USA, Apr. 11-13, 2011.
  [PDF] 

• Sparse Nonnegative Matrix Factorization for Protein Sequence Motif Discovery.
  Wooyoung Kim, Bernard Chen, Jingu Kim, Yi Pan, and Haesun Park.
  Expert Systems with Applications, 38(10), pp. 13198-13207, 2011.
  [URL]

2010

•  Supervised Raman Spectra Estimation based on Nonnegative Rank Deficient Least Squares.
  Barry Drake, Jingu Kim, Mahendra Mallick, and Haesun Park.
  In Proceedings of the Thirteenth International Conference on Information Fusion, Edinburgh, UK , 2010.
  [PDF]  

• Fast Active-set-type Algorithms for L1-regularized Linear Regression.
  Jingu Kim and Haesun Park.
  In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, JMLR: W&CP 9, pp 397-404, Chia Laguna, Sardinia, Italy, May 13-15, 2010.
  [PDF] [POSTER]  

2008

• Toward Faster Nonnegative Matrix Factorization: A New Algorithm and Comparisons.
  Jingu Kim and Haesun Park.
  In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (ICDM), pp. 353-362, 2008.
  [PDF] [SLIDES] [SOFTWARE]  

• Sparse Nonnegative Matrix Factorization for Clustering.
  Jingu Kim and Haesun Park.
  Georgia Tech Technical Report GT-CSE-08-01, 2008.
  [PDF]

This section provides implementations of efficient nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) algorithms described in the following papers. The NTF algorithms are for the nonnegative Candecomp/PARAFAC (NCP) model. A key subroutine is a fast algorithm for nonnegativity-constrained least squares problem, which maybe of interest to many applications other than NMF or NTF.  Please email to Jingu Kim with any questions in using the code, bug reports, or comments.

MATLAB code

• See Github page or download as zip.

• Plain, regularized, and sparse NMFs are all included.

• To use nonnegative tensor factorization, installation of MATLAB Tensor Toolbox is required. The version of the toolbox with which this software was tested is 2.4.

Python code

• See Github page or download as zip for nonnegative matrix factorization.

• Please find André Panisson's Github page for nonnegative tensor factorization.

Related papers

• Fast Nonnegative Matrix Factorization: An Active-set-like Method And Comparisons.
  Jingu Kim and Haesun Park.
  SIAM Journal on Scientific Computing (SISC), 33(6), pp. 3261-3281, 2011
  [PDF] [URL] 

• Algorithms for Nonnegative Matrix and Tensor Factorizations: A Unified View Based on Block Coordinate Descent Framework.
  Jingu Kim, Yunlong He, and Haesun Park.
  Journal of Global Optimization, 58(2), pp. 285-319, 2014.
  [PDF] [URL]

• Fast Nonnegative Tensor Factorization with an Active-set-like Method.
  Jingu Kim and Haesun Park.
  In High-Performance Scientific Computing: Algorithms and Applications, Springer, pp. 311-326, 2012.
  [PDF] [URL]