School of Computational Science and Engineering
College of Computing
Georgia Institute of Technology
(before @) da.kuang
(after @) cc.gatech.edu
I am a fifth-year PhD student in Computational Science and Engineering, Georgia Institute of Technology, advised by Prof. Haesun Park.
I am working on nonnegative matrix factorization and associated topics such as clustering methods, spectral algorithms, dimension reduction, and semi-supervised learning.
Broadly speaking, I am interested in numerical methods and high-performance computing for efficient large-scale machine learning, as well as applications in web search, recommender systems, and sentiment analysis. I also put much emphasis in my research on writing fast numerical codes and performance tuning towards specific architectures.
I obtained my Bachelor degree in computer science at Tsinghua University in Beijing, China. I started my college years in the Department of Mathematics, and later transferred to the Department of Computer Science and joined Yao Class. I worked with Prof. Min Zhang on learning to rank algorithms for information retrieval.
2014.8 I will be co-teaching CSE 6040 -- Computing for Data Analysis: Methods and Tools for the new MS Analytics Program in Fall 2014.
2013.5 I will be joining eBay research lab as a research intern for this summer.
2012.5 I will be joining Amazon.com as a software engineer intern in the recommendations team for this summer.
Da Kuang, Jaegul Choo, and Haesun Park, Nonnegative matrix factorization for interactive topic modeling and document clustering (book chapter), in Partitional Clustering Algorithms, Springer, 2015.
Nicolas Gillis, Da Kuang, and Haesun Park, Hierarchical clustering of hyperspectral images using rank-two nonnegative matrix factorization, IEEE Transactions on Geoscience and Remote Sensing (to appear). [Arxiv]
Da Kuang, Sangwoon Yun, and Haesun Park, SymNMF: Nonnegative low-rank approximation of a similarity matrix for graph clustering, Journal of Global Optimization (to appear).
Da Kuang, Nonnegative matrix factorization for clustering, PhD Dissertation, Georgia Institute of Technology, 2014. [pdf]
Da Kuang and Haesun Park, Fast rank-2 nonnegative matrix factorization for hierarchical document clustering, Proceedings of the 19th ACM SIGKDD International Conference on Knowledge, Discovery, and Data Mining (KDD '13), pp. 739-747, Chicago, IL, 2013. [pdf]
Da Kuang, Chris Ding, and Haesun Park, Symmetric nonnegative matrix factorization for graph clustering, Proceedings of 2012 SIAM International Conference on Data Mining (SDM '12), pp. 106-117, Anaheim, CA, 2012. [pdf] [slides]
Min Zhang, Da Kuang, Guichun Hua, Yiqun Liu, and Shaoping Ma, Is learning to rank effective for web search?, SIGIR 2009 Workshop on Learning to Rank for Information Retrieval, Boston, MA, 2009. [pdf]
Da Kuang and Raffay Hamid, piCholesky: Polynomial Interpolation of Multiple Cholesky Factors for Efficient Approximate Cross-Validation. [Arxiv]
kmeans3: Accelerating Matlab K-means with Simple Patches
CSE 6643 Numerical Linear Algebra
CSE 6740 Foundations of Machine Learning and Data Mining
MATH 6644 Iterative Methods for Systems of Equations
ISYE 6416 Computational Statistics
CSE 6140 Computational Science and Engineering Algorithms
CSE 6230 High Performance Parallel Computing
CSE 6220 High Performance Computing
CSE 8001 Solvers for Scientific Computations
BIOL 7111 Molecular Evolution
BIOL 4755 Introduction to Systems Biology
About the efficiency of growing and shrinking sparse matrices
Bug-fix for kmeans in the statistics toolbox
Last modified: Sep. 5, 2014