Le Song

Professor of Machine Learning

Research interests

Professor Song’s research interests are in machine learning methods and algorithms for complex and dynamic data including structured prediction, neuro-symbolic integration, and AI for healthcare and drug design.

Email

Prior to joining MBZUAI, Professor Song was an associate professor of computational science and engineering and the associate director of Center for Machine Learning at the Georgia Institute of Technology in the USA.

He spent several years at various institutes such as Georgia Institute of Technology, Google Research, Carnegie Mellon University and National ICT Australia.

Professor Song’s remarkable works won several best paper awards at the ACM Conference on Recommendation System (Recsys) in 2016, Artificial Intelligence and Statistics (AISTATS) in 2016, IEEE International Parallel and Distributed Processing Symposium (IPDPS) in 2015, Neural Information Processing Systems (NeurIPS) in 2013, and International Conference on Machine Learning (ICML) in 2010.

Song is a chair of the 39th International Conference on Machine Learning (ICML 2022).

Ph.D. in computer science from the University of Sydney and National ICT, Australia.

Best paper award at the ACM Conference on Recommendation System (Recsys) in 2016.
Best paper award at Artificial Intelligence and Statistics (AISTATS) in 2016.
Best paper award at IEEE International Parallel and Distributed Processing Symposium (IPDPS) in 2015.
Recipient of the National Science Foundation CAREER Award in 2014.
Outstanding Junior Faculty Research Award in 2014.
Lockheed Martin Inspirational Young Faculty Award in 2014.
Best paper award at Neural Information Processing Systems (NeurIPS) in 2013.
Best paper award at International Conference on Machine Learning (ICML) in 2010.

Publication Le Song

Song has published more than 160 papers in peer-reviewed, top machine learning conferences and journals such as NeurIPS, ICML, ICLR, AISTATS and JMLR.

Molecule Generation for Drug Design: a Graph Learning Perspective. N Yang, H Wu, J Yan, X Pan, Y Yuan, L Song. arXiv preprint arXiv:2202.09212. 2022.
Method and apparatus for processing user interaction sequence data. X Chang, J Wen, X Liu, L Song, Y Qi. US Patent 11,250,088. 2022.
Learning Temporal Rules from Noisy Timeseries Data. K Samel, Z Zhao, B Chen, S Li, D Subramanian, I Essa, L Song. arXiv preprint arXiv:2202.05403. 2022.
Sphereface: Deep hypersphere embedding for face recognition. W Liu, Y Wen, Z Yu, M Li, B Raj, L Song. Proceedings of the IEEE conference on computer vision and pattern. 2017.
Learning combinatorial optimization algorithms over graphs. E Khalil, H Dai, Y Zhang, B Dilkina, L Song. Advances in neural information processing systems 30, 2017.
A Hilbert space embedding for distributions. A Smola, A Gretton, L Song, B Schölkopf. International Conference on Algorithmic Learning Theory, 13-31, 2007.

Google Scholar

Selected works on MBZUAI eCommons