I am a Research Scientist at ASUS-AICS. I am also a part-time fifth-year Ph.D. student advised by Prof Min-Yen Kan. I closely collaborate with Devamanyu Hazarika and Soujanya Poria. My research focuses on Natural Language Processing.
Specifically I make NLP models robust under different domains, also called Domain Adaptation. I am interested in using unsupervised data from different domains for domain adaptation. My recent interest focuses on using parameter efficient learning methods for domain adaptation of Large Language Models.
In AICS, I work on Clinical Natural Language Processing. If you want to know more about this reserach and for collaborations, email me.
I have done an internship at Amazon, with the Alexa AI team. I completed my Master’s degree from the National University of Singapore. His master’s thesis involved extractive summarization specific to restaurant reviews. I was part of the Sensor Enhanced Social Media(SeSaMe) lab with Prof. Mohan Kankanhalli
|Jan 10, 2023||
Our paper “UDAPTER: Efficient Domain Adaptation using Adapters” has been accepted for EACL’23. This paper aims to make domain adaptation more efficient. This is a joint work with Bhavitvtya Malik. Code, paper and everything nice, coming soon.
|Jan 10, 2023||
Our Survey paper “Scientific Document Processing: Challenges for Modern Learning Methods” is accepted at IJDL.
|Oct 17, 2022||
Excited to join ASUS-AICS. I will be working on Clinical NLP.
|Aug 31, 2022||
Passed my Thesis Proposal at NUS
|May 25, 2022||
Presented our Paper “So Different Yet So Alike! Constrained Unsupervised Text Style Transfer” at ACL’22 as an oral presentation
|May 13, 2022||
Presented our paper “So Different Yet So Alike! Constrained Unsupervised Text Style Transfer” at SSNLP’22
|Apr 22, 2022||
Recognized as an Outstanding Reviewer for ICLR 2022
|Apr 1, 2022||
Wrapped up my internship at Amazon.
|Feb 24, 2022||
Our work “So Different Yet So Alike! Constrained Unsupervised Text Style Transfer” is accepted at ACL.
|Oct 1, 2021||
Started as an Applied Scientist Intern @ Amazon Science. Excited to work with Mahdi Namazifar