As of March 2015, I'm a software engineer at Google. Previously, I was a PhD student at the University of Washington, where I worked with Pedro Domingos on Statistical Relational Learning and other things. Earlier still, I was an undergrad at the University of Illinois.
- Learning Tractable Probabilistic Models for Fault Localization. A. Nath and P. Domingos. StaR-AI 2015.
- Learning and Exploiting Relational Structure for Efficient Inference. A. Nath. PhD Dissertation, University of Washington, 2015.
- Learning Relational Sum-Product Networks. A. Nath and P. Domingos. AAAI 2015.
- Earlier version: Learning Tractable Statistical Relational Models. StaR-AI 2014.
- Automated Debugging with Tractable Probabilistic Programming. A. Nath and P. Domingos. StaR-AI 2014. (Position paper.)
- Approximate Lifting Techniques for Belief Propagation. P. Singla, A. Nath and P. Domingos. AAAI 2014.
- Counting-MLNs: Learning Relational Structure for Decision Making. A. Nath and M. Richardson. AAAI 2012.
- Related patent: Relational Learning for System Imitation. US 8,862,523 B2.
- Learning Multiple Hierarchical Relational Clusterings. A. Nath and P. Domingos. SRL 2012.
- Markov Logic: A Language and Algorithms for Link Mining. P. Domingos, D. Lowd, S. Kok, A. Nath, H. Poon, M. Richardson and P. Singla. Chapter in P. Yu, C. Faloutsos, and J. Han (eds.), Link Mining: Models, Algorithms and Applications, 2010.
- Efficient Belief Propagation for Utility Maximization and Repeated Inference. A. Nath and P. Domingos. AAAI 2010.
- Efficient Lifting for Online Probabilistic Inference. A. Nath and P. Domingos. AAAI 2010.
- A Language for Relational Decision Theory. A. Nath and P. Domingos. SRL 2009.