I'm currently the Head of AI at Eloquent Labs, a conversational AI company building chat bots for customer service. Not that long ago, I graduated with a PhD in computer science at Stanford University, working with Percy Liang and the Stanford NLP group. If you'd like to reach out to me, just shoot me an email at
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.
- Lamm, Chaganty, Manning, Jurafsky, Liang; Textual Analogy Parsing: Identifying What's Shared and What's Compared among Analogous Facts. EMNLP, 2018. [pdf] [data,code]
- 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]