Andrew Maas

I am a fifth-year Computer Science PhD student at Stanford University advised by Andrew Ng. I also build machine learning applications for online education as a software engineer at Coursera, and machine learning tools for enterprise as a co-founder at Roam Insight. My research is supported by a National Science Foundation graduate research fellowship. In May 2009 I completed my Bachelors in Computer Science and Cognitive Science at Carnegie Mellon with a minor in computational neuroscience.

Email: amaas [a t} cs . stanford dot edu

Research Interests

I work at the intersection of machine learning, robotics, and cognitive science. Human perception and learning are remarkable when we consider the complex data entering our senses. Developing algorithms to automatically find structure in audio, text, images, and other data will enable autonomous systems to better integrate into everyday life.

My CV (out of date)

Publications (Google Scholar Listing)

Andrew L. Maas, Awni Y. Hannun, Daniel Jurafsky, and Andrew Y. Ng. (2014). First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs. ArXiv:1408.2873 [cs.CL].

Andrew L. Maas, Awni Y. Hannun, Christopher T. Lengerich, Peng Qi, Daniel Jurafsky, and Andrew Y. Ng. (2014). Increasing Deep Neural Network Acoustic Model Size for Large Vocabulary Continuous Speech Recognition. ArXiv:1406.7806 [cs.CL].

Moritz Sudhof, Andrés Gomés Emilsson, Andrew L. Maas, and Christopher Potts. (2014). Sentiment Expression Conditioned by Affective Transitions and Social Forces. To appear in Proceedings of 20th Conference on Knowledge Discovery and Data Mining (KDD 2014).

Andrew Maas, Chris Heather, Chuong (Tom) Do, Relly Brandman, Daphne Koller, and Andrew Y. Ng. (2014). Offering Verified Credentials in Massive Open Online Courses. ACM Ubiquity Symposium: MOOCs and Technology to Advance Learning and Learning Research. January 2014.

Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. (2013). Rectifier Nonlinearities Improve Neural Network Acoustic Models. ICML Workshop on Deep Learning for Audio, Speech, and Language Processing (WDLASL 2013).

Andrew L. Maas, Tyler M. O'Neil, Awni Y. Hannun, and Andrew Y. Ng. (2013). Recurrent Neural Network Feature Enhancement: The 2nd CHiME Challenge. The 2nd International Workshop on Machine Listening in Multisource Environments (CHiME 2013). [ DRDAE Code]

Andrew L. Maas, Quoc V. Le, Tyler M. O'Neil, Oriol Vinyals, Patrick Nguyen, and Andrew Y. Ng. (2012). Recurrent Neural Networks for Noise Reduction in Robust ASR. Interspeech 2012. [ DRDAE Code]

Andrew L. Maas, Stephen D. Miller, Tyler M. O'Neil, Andrew Y. Ng, and Patrick Nguyen. (2012). Word-level Acoustic Modeling with Convolutional Vector Regression. ICML 2012 Representation Learning Workshop.

Andrew L. Maas, Andrew Y. Ng, and Christopher Potts. (2011). Multi-Dimensional Sentiment Analysis with Learned Representations. Technical Report, April 2011. [Supplementary Figure]

Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011). [Dataset]

Richard Socher, Andrew Maas, and Christopher D. Manning. (2011). Spectral Chinese Restaurant Processes: Nonparametric Clustering Based on Similarities. AISTATS 2011. [ Project page ]

Andrew L. Maas and Andrew Y. Ng. (2010). A Probabilistic Model for Semantic Word Vectors. NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. [Code]

Andrew L. Maas and Charles Kemp. (2009). One-Shot Learning with Bayesian Networks. Proceedings of The 31st Annual Meeting of The Cognitive Science Society.

Brian D. Ziebart, Andrew Maas, Anind K. Dey, and J. Andrew Bagnell. (2009). Human Behavior Modeling with Maximum Entropy Inverse Optimal Control. AAAI Spring Symposium on Human Behavior Modeling.

Brian D. Ziebart, Andrew Maas, Anind K. Dey, and J. Andrew Bagnell. (2008). Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior. Proceedings of the 10th International Conference on Ubiquitous Computing.

Brian D. Ziebart, Andrew Maas, J. Andrew Bagnell, and Anind K. Dey. (2008). Maximum Entropy Inverse Reinforcement Learning. Proceedings of the 23rd AAAI Conference on Artificial Intelligence.


Demo of a Personalized Navigation Device which Predicts User Behavior Mapprentice Project. 2009.

Dynamically Adjusting Suggested Route as Hazards Change. Mapprentice Project. 2008.

Destination Prediction Route so far shown in black, log probability of destination shown in varying red intensities. Mapprentice Project. 2008.

Predicting Route During Travel Destination is known. Mapprentice Project. 2008.