Computational Science and Engineering
College of Computing
Georgia Institute of Technology
1340 Klaus Building
266 Ferst Drive
Atlanta, GA 30332, USA
I am heading the Machine Learning Group at Georgia Institute of Technology.
My principal research interests lie in the development of efficient algorithms and systems which can learn from a massive volume of complex (high dimensional, nonlinear, multi-modal, skewed, and structured) data arising from both artificial and natural systems, reveal trends and patterns too subtle for humans to detect, and automate decision making processes in uncertain and dynamic possible world.
I am also interested in developing machine learning algorithms to address interdisciplinary problems. Projects I've worked on range from the modeling of robotic systems based on sensor measurements, to the discovery of evolving gene regulatory networks based on microarray time-series, to the management of information diffusion based on temporal sequences of social events, to the understanding of disease progression based on longitudinal medical records, to the extraction of topics based on online document feeds, and to the prediction of materials properties based on computational and lab experiments.
More specifically, my current projects involve
- developing core machine learning methodology, including kernel methods, scalable algorithms, graphical models, probabilistic and stochastic modeling, optimization, and deep learning.
- developing algorithms for applications arising from social science, healthcare analytics, biology, neuroscience, chemistry and materials science
Tutorials and Workshops
- Machine Learning Summer School 2016 tutorial Dynamic Processes over Networks: Representation, Modeling, Learning, and Inference.
- KDD 2015 tutorial Diffusion in Social and Information Networks: Research Problems, Probabilistic Models & Machine Learning Methods (with Manuel Gomez Rodriguez)
- ICML 2015 workshop Large-Scale Kernel Learning: challenges and new opportunities
- WWW 2015 tutorial Diffusion in Social and Information Networks: Research Problems, Probabilistic Models & Machine Learning Methods (with Manuel Gomez Rodriguez)
- WWW 2015 workshop Diffusion, Activity and Events in Networks: Models, Methods & Applications (with Manuel Gomez Rodriguez and Hongyuan Zha)
- NIPS 2014 workshop Modern Nonparametrics 3: Automating the Learning Pipeline (with Arthur Gretton, Mladen Kolar, Samory Kpotufe and Han Liu and Zoltan Szabo and Andrew Wilson and Eric Xing)
- NIPS 2012 workshop on the Confluence between Kernel Methods and Graphical Models (with Arthur Gretton and Alex Smola)
- NIPS 2012 workshop on Spectral Algorithms for Latent Variable Models (with Ankur Parikh and Eric Xing)
- ICML 2012 tutorial on Spectral Approaches to Learning Latent Variable Models (with Geoffery Gordon and Byron Boots)
Selected Recent Publications
- H. Dai, Y. Wang, R. Trivedi and L. Song. Recurrent Coevolutionary Feature Embedding Processes for Recommendation, Recsys Workshop on Deep Learning for Recommendation Systems, 2016. PDF (BEST PAPER)
- Research Highlight: Deep learning for graph data and materials science. link
- B. Dai, N. He, H. Dai and L. Song. Provable Bayesian Inference via Particle Mirror Descent, Artificial Intelligence and Statistics (AISTATS), 2016. PDF (BEST Student PAPER)
- H. Dai, B. Dai and L. Song. Discriminative Embeddings of Latent Variable Models for Structured Data, International Conference on Machine Learning (ICML), 2016. PDF
- N. Balcan, Y. Liang, L. Song, D. Woodruff and B. Xie. Distributed Kernel Principal Component Analysis, Knowledge Discovery and Data Mining (KDD), 2016. PDF
- M. Farajtabar, Y. Wang, M. Rodriguez, S. Li, H. Zha and L. Song (2015). COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution. Advances in Neural Information Processing Systems 25 (NIPS 2015). PDF
- You, Y., Demmel, J., Czechowski, K., Song, L., and Vuduc, R. CA-SVM: Communication-Avoiding Support Vector Machines on Clusters, IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2015. (BEST PAPER)
- Dai, B., Xie, B., He, N., Liang, Y., Raj, A., Balcan, M., and Song, L. Scalable Kernel Methods via Doubly Stochastic Gradients. Neural Information Processing Systems (NIPS 2014). PDF
- Farajtabar, M., Du, N., Rodriguez, M., Valera, I., Zha, H., and Song, L. Shaping Social Activity by Incentivizing Users. Neural Information Processing Systems (NIPS 2014). PDF
- L. Song, A. Anandkumar, B. Dai and B. Xie. Nonparametric estimation of multi-view latent variable models. International Conference on Machine Learning (ICML 2014). PDF
- N. Daneshmand, M. Rodriguez, L. Song and B. Scholkopf (2014). Estimating diffusion network structure: recovery conditions, sample complexity, and a soft-thresholding algorithm. International Conference on Machine Learning (ICML 2014). PDF
- Du, N., Song, L., Rodriguez, M., and Zha, H., Scalable Influence Estimation for Continuous-Time Diffusion Networks, Neural Information Processing Systems (NIPS), 2013. PDF (BEST PAPER)
- Song, L., Fukumizu, K., and Gretton, A., Kernel Embedding of Conditional Distributions, IEEE Signal Processing Magazine, 2013. PDF
- Fukumizu, K., Song, L., and Gretton, A., Kernel Bayes' Rule, Journal of Machine Learning Researches, 2013. PDF
- E. Khalil, B. Dilkina and L. Song. CuttingEdge: Influence minimization in networks. NIPS Workshop on Frontiers of Network Analysis: Methods, Models, and Applications, 2013. PDF (BEST PAPER)
- Song, L., Gretton, A., Bickson, D., Low, Y., and Guestrin, C., Kernel Belief Propagation, International Conference on Artifical Intelligence and Statistics (AISTATS), 2011. PDF
- Song, L., Boots, B., Siddiqi, S., Gordon, G., and Smola, A., Hilbert Space Embeddings of Hidden Markov Models, International Conference on Machine Learning (ICML), 2010. PDF (BEST PAPER)
- Kolar, M., Song, L., Ahmed, A., and Xing, E., Estimating Time-Varying Networks, Annals of Applied Statistics, 2010. PDF
- Song, L., Kolar, M., and Xing, E., KELLER: Estimating Time-Evolving Interactions between Genes, Bioinformatics (ISMB), pp.i128--i136, 2009. PDF
- Smola, A., Gretton, A., Song, L., and Scholkopf, B. A Hilbert Space Embedding for Distributions, Algorithmic Learning Theory (ALT 2007). PDF
I obtained B.S. degree in computer science from the South China University of Technology, Guangzhou, China in 2002. After that, I received my Master's degree in 2004, and Ph.D. degree in 2008 both in computer science from the University of Sydney, Australia. I was also a Ph.D. student with the Statistical Machine Learning Program at NICTA, and my thesis advisor is Alex Smola. Since Summer 2008, I was a postdoc fellow at Carnegie Mellon Univeristy, working on machine learning and computational biology projects with Eric Xing, Carlos Guestrin, Geff Gordon and Jeff Schneider. Right before I joined Georgia Tech, I spent some time as a research scientist at Fernando Pereira's group at Google Research.