Hi, I’m Namrata Mukhija
I’m currently an Applied Scientist at JPMorgan Chase and Co. working in the Machine Learning Center of Excellence. I’m a M.S. in Computer Science graduate from New York University, Courant School of Mathematical Sciences. My primary research area is in Natural Language Processing. I previously interned at Arthur as a Research Intern where I worked on assessing stereotype detection capability in large language models. I have also previously interned at J.P. Morgan, Machine Learning Center of Excellence, New York as an Applied NLP Scientist. My work was published at ACL’23 and NeurIPS’22 Workshop and was centered at assessing the effect of paraphrasing as a data augmentation tool in improving Named Entity Recognition task performance in low versus high resource scenarios. I also interned at Microsoft Research, India under Dr. Kalika Bali and Dr. Monojit Choudhury in developing a framework for prioritizing research for low-resource language communities, understanding different dilemmas faced by technologists, their origin and complexity, and involving low-resource language communities in the development of language technologies. The work was published at ACM COMPASS. Before my Masters, I was a Software Engineer 2 at Microsoft. I was involved in developing various products such as PowerPoint, Fluid Framework, and Unified Service Desk.
I received my B.E. in Information Technology from Netaji Subhas University of Technology.
I was awarded the Microsoft Experiences+Devices India Leadership Award 2020 across 2500+ employees for generating energy in the team, demonstrating ”All for One, One for All” mindset, and championing diversity and inclusion.
I actively work and volunteer for non-profits working towards closing the gender gap across industries - Women in AI where I lead the Women in AI NY and NJ, USA (previously India) chapter, Thousand Eyes On Me where I work as the Program Director. If you have an idea that could help reduce the gender gap, please reach out to me!
Featured Projects
A causal analysis of whether personal beliefs towards one marginalized group perpetuate to other marginalized groups.
Developed a biLSTM-based model for grammar and essay scoring. Implemented augmented C&W word embeddings which would treat grammatical errors as informative.
An abstractive text summarizer model which has a encoder for keyword representation, an only attention-based pointer-generator network to generate summaries, and a value estimator network.