I am a Research Scientist at Adobe Research San Jose Lab. I am broadly interested in representation learning and natural language processing. More specifically, I am interested in developing deep algorithms for small LLMs and agentic tools. During my PhD, I have focused on building different novel unsupervised deep models using representation learning, which can be applied to diverse low-resource languages and applications across language families. One of my works is to enhance the performance of the multilingual sentence embedding models for low-resource domains in zero-shot settings by injecting better word representations. My significant contributions during PhD include the invention of different unsupervised loss functions, including the Maximum Likelihood Clustering (MLC) loss function and the designing of a new machine learning framework called the Anchor-Learner framework. I have also worked on instructional learning using prompt engineering for few-shot model training, to enhance decoder based model performance like T5. During PhD, I have also had internship stints at different Industry research labs. My published research papers appeared in various NLP conferences including ACL, EMNLP, COLING, ICCV, EACL, LREC, IEEE, Frontier Journal, NAACL etc.

Previously, I was a PhD candidate in the Insight Centre for Data Analytics lab at the University of Galway .

My publications can be found here

For more details please visit my Linkedin page.