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Machine Learning and Artificial Intelligence

Automated reasoning and the applications of decision making

We pursue research on automated reasoning, adaptation, and the theories and applications of decision making and learning. Our research goals include learning from data and data mining. By building software that automatically learns from data, we design applications that have new functions and flexibility. Our research focuses on using statistical methods for the development of more advanced, intelligent computer systems.


Mohammad Raza, Sumit Gulwani, and Natasa Milic-Frayling, Compositional Program Synthesis from Natural Language and Examples, March 2015.

Andrew Brown, Zhihao Ding, Ana Viñuela, Dan Glass, Leopold Parts, Tim Spector, John Winn, and Richard Durbin, Pathway Based Factor Analysis of Gene Expression Data Produces Highly Heritable Phenotypes that Associate with Age, in G3: Genes | Genomes | Genetics, March 2015.

Nathan Wiebe, Ashish Kapoor, and Krysta M. Svore, Quantum Nearest-neighbor Algorithms for Machine Learning, in Quantum Information and Computation, vol. 15, no. 3&4, pp. 0318-0358, Rinton Press, March 2015.

Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig, Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, IEEE – Institute of Electrical and Electronics Engineers, March 2015.

Asta Roseway, Yuliya Lutchyn, Paul Johns, Elizabeth Mynatt, and Mary Czerwinski, BioCrystal: An Ambient Tool for Emotion and Communication , in IJMHCI, March 2015.

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