Email: I am a research scientist at Netflix. I completed my Ph.D. in the Electrical Engineering Department at Columbia University, as part of the LabROSA, working with Professor Dan Ellis and Professor David Blei. My CV is available here, as of Sep 2020. Prior to that, I completed my master's degree in Music and Technology program at Carnegie Mellon University, working with Professor Roger Dannenberg in the School of Computer Science, where I was part of the Computer Music Group. I received my bachelor's degree in Computer Science at Fudan University. |
Ph.D., Electrical Engineering, Columbia University, June 2016
M.S., Music and Technology, Carnegie Mellon University, May 2012
B.S., Computer Science, Fudan University, Jun 2010
Probabilistic latent variables models and applications to recommender systems.
Bandit, reinforcement learning, and causal inference.
Research Scientist, Product Machine Learning Research, Netflix, July 2016 – present
Recommendation Systems Scientist Intern, Playlist Team, Pandora, May 2015 – Aug 2015
Investigate hybrid approaches to collaborative filtering. Mentors: Erik Schmidt and Keki Burjorjee.
Research Intern, Adobe Creative Technology Laboratory, Adobe Inc., May 2013 – Aug 2013, May 2014 – Aug 2014
Bayesian hierarchical model of audio. Mentors: Matt Hoffman and Gautham Mysore.
For the most up-to-date information please see my Google Scholar page.
Preprints
Dawen Liang and Nikos Vlassis, 2022.
Da Tang, Dawen Liang, Nicholas Ruozzi, and Tony Jebara, 2019.
Yixin Wang, Dawen Liang, Laurent Charlin, and David M. Blei, 2018.
Peer-reviewed Journal Articles
Harald Steck, Linas Baltrunas, Ehtsham Elahi, Dawen Liang, Yves Raimond, Justin Basilico, AI Magazine, 42(3), 7-18, 2021.
Roger B. Dannenberg, Nicolas E. Gold, Dawen Liang, Guangyu Xia, Computer Music Journal, 38(2):36-50, 2014, MIT Press.
Roger B. Dannenberg, Nicolas E. Gold, Dawen Liang, Guangyu Xia, Computer Music Journal, 38(2):51-62, 2014, MIT Press.
Peer-reviewed Conference Papers and Workshop Contributions
Harald Steck and Dawen Liang, ACM Conference on Recommender Systems (RecSys), 2021.
Yixin Wang, Dawen Liang, Laurent Charlin, and David M. Blei, ACM Conference on Recommender Systems (RecSys), 2020.
Da Tang, Dawen Liang, Tony Jebara, Nicholas Ruozzi, International Conference on Machine Learning (ICML), 2019.
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara, The Web Conference (WWW), 2018.
Rahul G. Krishnan, Dawen Liang, Matthew D. Hoffman, International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.
Dawen Liang, Laurent Charlin, David M. Blei, UAI Workshop on Causation: Foundation to Application, 2016.
Dawen Liang, Jaan Altossar, Laurent Charlin, David M. Blei, ACM Conference on Recommender Systems (RecSys), 2016.
Dawen Liang, Laurent Charlin, James McInerney, David M. Blei, International Conference on World Wide Web (WWW), 2016.
Dawen Liang, Minshu Zhan, and Daniel P. W. Ellis, International Society for Music Information Retrieval (ISMIR), 2015.
Dawen Liang and John Paisley, International Conference on Machine Learning (ICML), 2015.
Brian McFee, Colin Raffel, Dawen Liang, Daniel P. W. Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto, Proceedings of the 14th Python in Science Conference (SciPy), 2015.
Dawen Liang, Matthew D. Hoffman, and Gautham J. Mysore, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015.
Dawen Liang and Matthew D. Hoffman, NIPS Workshop on Advances in Variational Inference, 2014.
Dawen Liang, John Paisley, and Daniel P. W. Ellis, International Society for Music Information Retrieval (ISMIR), 2014.
Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, and Daniel P. W. Ellis, International Society for Music Information Retrieval (ISMIR), 2014.
Dawen Liang, Daniel P. W. Ellis, Matthew D. Hoffman, and Gautham J. Mysore, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014.
Dawen Liang, Matthew D. Hoffman, and Gautham J. Mysore, International Conference on Learning Representations (ICLR), 2014.
Dawen Liang, Matthew D. Hoffman, and Daniel P. W. Ellis, International Society for Music Information Retrieval (ISMIR), 2013.
Guangyu Xia, Dawen Liang, Roger B. Dannenberg, and Mark J. Harvilla, International Society for Music Information Retrieval Conference (ISMIR), 2011.
Dawen Liang, Guangyu Xia, and Roger B. Dannenberg, New Interfaces for Musical Expression (NIME), 2011.
PhD Thesis
Dawen Liang, Department of Electrical Engineering, Columbia University, 2016 (DOI: 10.7916/D8TH8MZP)
Here are some notes regarding some topics I found interesting. I wrote these just for fun (and I think they will be useful sometime in the future). Therefore, there might be mistakes/errors in these notes. If you find any problem, please let me know.
A proof that Maximum Likelihood Estimation (MLE) is biased for variance estimator.
A note that shows some useful properties for matrix derivative.
Some technical details for the Expectation Maximization (EM) algorithm.