# Home

I'm currently a (soon-to-be-graduating) PhD student in computer science at Stanford University, working with Percy Liang and the Stanford NLP group. If you'd ever like to reach out to me, just shoot me an email at chaganty@cs.stanford.edu. Also, because it's a thing, you can find me on GitHub here.

Resume: [pdf], [template],

## Research Interests

I am interested in studying how natural language processing can be tooled to make it easier for people to understand and consume information. On a related note, I care about how we can bring greater transparency, accountability and fairness in voice through information summarization. I believe that both these goals need us to rethink how we evaluate our models and my more recent work seeks to address this problem using a mix of statistics, crowdsourcing and natural language processing.

In the past, I have worked on providing guarantees for learning latent variable models, probabilistic programming, statistical relational learning and hierarchical reinforcement learning.

## Publications

• Chaganty*, Mussmann*, Liang; The price of debiasing automatic metrics in natural language evaluation.; ACL 2018 [pdf][poster][data,code][arxiv]
• Chaganty*, Paranjape*, Liang, Manning; Importance sampling for unbiased on-demand evaluation of knowledge base population.; EMNLP 2017 [pdf][code][website]
• Chaganty, Liang; How Much is 131 Million Dollars? Putting Numbers in Perspective with Compositional Descriptions.; ACL 2016 [pdf][data,code]
• Werling, Chaganty, Liang, Manning; On the Job Learning with Bayesian Decision Theory; NIPS 2015 [arxiv][poster]
• Wang, Chaganty, Liang; Estimating Mixture Models via Mixtures of Polynomials; NIPS 2015. [paper][poster]
• Kuleshov*, Chaganty*, Liang; Tensor Factorization via Matrix Factorization; AISTATS 2015. [arxiv][slides]
• Chaganty, Liang; Estimating Latent Variable Graphical Models with Moments and Likelihoods; ICML 2014. [paper][slides]
• Chaganty, Liang; Spectral Experts for Estimating Mixtures of Linear Regressions; ICML 2013. [paper][slides][poster]
• Chaganty, Lal, Nori, Rajamani; Combining Relational Learning with SMT Solvers using CEGAR; CAV 2013. [paper]
• Chaganty, Nori, Rajamani; Efficiently Sampling Probabilistic Programs via Program Analysis; AISTATS 2013. [paper]
• Chaganty, Gaur, Ravindran; Learning in a Small World; AAMAS 2012. [paper]
• Chaganty; Inter-Task Learning with Spatio-Temporal Abstractions; Master's Thesis (IIT Madras). [thesis]