Hey! I am a PhD student at the MURGe-Lab at the University of North Carolina - Chapel Hill, where I work with Prof. Mohit Bansal. My research goal is to make deep learning models and their evaluation procedures more scalable, robust, interpretable and reusable. I like to understand and exploit structures and invariances in the data to develop efficient models with limited supervision. Previously, I have worked on a diverse set of topics, (1) deep learning methods for Graph and Hypergraph structured data and their application to NLP, (2) estimated and controlled for uncertanity in the learned representations from these methods, (3) worked on Bayesian modeling of temporal data and (4) model based Reasoning in NLP.
Over the past few years, I have been fortunate to work with Prof. Partha Talukdar at MALL-Lab at Indian Institute of Science (IISc) Bangalore, with Dr. Prateek Jain at Microsoft Research India and with Prof. Arun Rajkumar at Indian Institute of Technology, Madras. I also worked for a year with some amazing people at LinkedIn AI Bangalore. Before all this, I completed my undergraduate degree in pure mathematics in 2018 from IISc Bangalore where I was supervised by Prof. Partha Talukdar.
Check out my publication page to know more about my current research. I am also actively involved in improving the way science is taught to students, through simple experiments and conducting workshops via Notebook Drive.
|Oct 2021||Pre-print for our paper Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions is now available!|
|Aug 2021||ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning is accepted at EMNLP 2021, Punta Cana, Dominican Republic.|
|Mar 2021||multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning is accepted at NAACL 2021.|
|Jan 2021||Pre-print for our paper Discrete Time Latent Hawkes Processes for Modeling Multidimensional Temporal Event Streams is now available!|
|Nov 2020||Our paper Rank Refinement: An Algorithmic framework with applications to diversity aware influence maximization has been accepted at GCLR workshop at AAAI 2020.|
Pre-printLow-Cost Algorithmic Recourse for Users With Uncertain Cost Functions2021
EMNLP [ORAL]ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense ReasoningIn Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2021
NAACL [ORAL]multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule ReasoningIn Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2021
NeurIPSHyperGCN: A New Method For Training Graph Convolutional Networks on HypergraphsIn Advances in Neural Information Processing Systems 2019
AISTATSLovasz Convolutional NetworksIn Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics 2019
ACLIncorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional NetworksIn Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019