Abhijeet Awasthi
PhD Student
Computer Science and Engineering
Indian Institiute of Technology Bombay

I am a final-year PhD Student in Computer Science and Engineering at IIT Bombay, advised by Prof. Sunita Sarawagi.
My research focuses on building data-efficient Natural Language and Speech Processing models and spans diverse applications like Semantic Parsing, Text Editing, Data Programming, Speech Recognition, and Keyword Spotting. I am generally excited about working on areas involving structured prediction, compositional generalization and weak supervision for learning with less data.
My research is supported by Google PhD Fellowship.
During my PhD, I have interned twice at Google Research. Before joining IIT Bombay, I worked as a Software Engineer in the GPS and Sensors team at Samsung Research Institute. I received my B.Tech. in Electronics and Electrical Communication Engineering from IIT Kharagpur in 2016.
news
Nov 23, 2022 | Two papers on adapting Text-to-SQL semantic parsers accepted at EMNLP-22, and AAAI-23. |
April 25, 2022 | Research Internship with the NLU team at Google. I'll be working with Partha Talukdar, Nitish Gupta, Bidisha Samanta , and Shachi Dave on improving multilingual natural language understanding. More details to follow soon! |
June 4, 2021 | Will be presenting our paper Error-Driven Fixed-Budget ASR Personalization For Accented Speakers at ICASSP-2021. |
June 3, 2021 | Our paper on Keyword Spotting accepted in Interspeech 2021 🥳. This work was done during my research internship at Google. |
Mar 18, 2020 | Our paper Learning from Rules Generalizing Labeled Exemplars will be presented as a Spotlight at ICLR 2020 . Code available on GitHub. |
Nov 2, 2019 | Will be in Hong Kong to present our paper on Parallel Iterative Edit (PIE) models at EMNLP 2019. [Poster] |
Oct 25, 2019 | Our work on Grammatical Error Correction got accepted as a long paper at EMNLP-IJCNLP 2019, Hong Kong. Check out the arXiv preprint and code. |
Jul 11, 2018 | I have been awarded Google PhD Fellowship in Machine Learning. Thanks Google! :) |