The rapid advancements in digital and information technology have barred older, and technologically disadvantaged populations from acquiring information and getting necessary tasks done. This digital divide handicaps marginalized people and exacerbates pre-existing inequities. I intend to tackle this inequality with NLP and IR to improve accessibility, with a particular focus on providing everyone with equal access to information. This will not only benefit individuals, but also help introduce more informed, active, and diverse populations into society.

My work so far has primarily been focused on efficient and more controllable language analysis, IR and IE for more accessible and robust question answering and task-oriented dialogue systems.

I have spent a considerable amount of time working on language analyzers, including rule-based part-of-speech taggers, named entity recognition models, and speech-act analyzers whose high throughput and controllability make them suitable for search engines and in high-stakes domains. I also worked on deep learning-based models for more robustness, and a hybrid model that kept the customizability and robustness of either method.

In IR and IE, I have worked on MRC, summarization, and recommendation systems. For many of these projects, having to process millions of documents in a short time often required me to make optimizations on all fronts, including data processing, and the use of system resources and databases. I find this a useful experience because making small and efficient systems can be more important than making them bigger and more powerful, especially in making more accessible systems.

My papers were focused on improving the performance of models using linguistic features. The first paper was on classifying speech-acts using neural networks and rule-extracted features, and for my master's thesis, I worked on MRC which utilizes features like part-of-speech tags and wh-words. I expected these reliable features, with minimal overhead, would help improve accuracy while maintaining efficiency.

Building upon my past work, I plan to work on processing spoken language and providing target knowledge in a useful way for the user. In pursuit of these objectives, I aim to emphasize efficiency to build more deployable systems and provide reliable, grounded information. I also intend to explore linguistic theories and statistical methods for more efficient modeling and gain a deeper understanding of the latest and traditional NLP techniques.

As a member of the LGBTQ+ community who grew up in a monocultural and monoethnic society that is hostile to marginalized people, I strive for inclusivity and diverse representations in society and at work. I believe that diversity and inclusivity lead to better ideas and innovation. I am driven to create positive change, not only within academic settings but also in the broader community in this belief.