8125 Paint Branch Dr
College Park, MD 20742
I am a first year Computer Science PhD student at the University of Maryland, College Park, currently working with Jordan Boyd-Graber. I am broadly interested in Natural Language Understanding and Representation; particularly in Question Answering (QA), Semantic structure understanding, Model Robustness and Interpretability.
Prior to UMD, I spent two years working at Google Research where I collaborated with several Language Research teams, with focus on Model Interpretation and Analysis for Question Answering, and Semi-Structured Text Understanding for QA and NLI.
In the past, I have also worked on some of the Computer vision and Machine Learning problems like: Human motion sequence modeling, Generative and Representation Learning and Adversarial Machine Learning.
I am quite excited about learning and applying Visually grounded contexts for Language Understanding tasks as well.
|May 23, 2022||I am spending my summer at X, the moonshot factory (formerly Google X) as PhD Research Resident, and will be working on Program Synthesis.|
|Apr 15, 2022||I am reviewing at NeurIPS 2022|
|Nov 22, 2021||I will be serving as a Program Committee member for SUKI: Workshop on Structured and Unstructured Knowledge Integration and DADC: Workshop on Dynamic Adversarial Data Collection at NAACL 2022|
|Sep 22, 2021||Our work “MATE: Multi-view Attention for Table Transformer Efficiency” selected for an oral presentation at EMNLP 2021.|
|Aug 26, 2021||Our work “Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy” is accepted at EMNLP 2021.|
- EMNLP 2021MATE: Multi-view Attention for Table Transformer EfficiencyIn Empirical Methods in Natural Language Processing 2021
- EMNLP 2021Toward Deconfounding the Influence of Entity Demographics for Question Answering AccuracyIn Empirical Methods in Natural Language Processing 2021
- ICCV 2019GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data DistributionsIn Proceedings of the IEEE/CVF International Conference on Computer Vision 2019