# Gaurav Maheshwari Hi! I'm a Research Engineer specializing in NLP at Diabolocom, where I work across both text and speech domains. My role involves exploring cutting-edge techniques such as advanced prompting methods, Retrieval-Augmented Generation (RAG), and fine-tuning Large Language Models (LLMs). I also apply classical NLP approaches to tackle challenges like Named Entity Recognition (NER), Intent Recognition, Sentiment Analysis, and Topic Modeling. My work spans both the text and speech spaces, bridging the gap between language understanding and voice-driven interactions. Before starting at Diabolocom, I completed my Ph.D. from [INRIA](https://www.inria.fr/en), where I was part of the [Magnet Team](http://team.inria.fr/magnet/) (MAchine learninG in information NETworks) and affiliated with [CRIStAL](http://www.cristal.univ-lille.fr) (UMR CNRS 9189), a research center of the [University of Lille](http://www.univ-lille.fr/). I am working on fairness and privacy related topics in NLP. Previously, I worked as a dialogue research engineer at [Fraunhofer IAIS, Dresden](https://www.iais.fraunhofer.de/en/institute/dresden.html). Before that, I was a Masters student at Uni Bonn (DE), and a student researcher at [Smart Data Analytics](http://sda.cs.uni-bonn.de/) Lab and [Fraunhofer IAIS](https://www.iais.fraunhofer.de/en.html), Bonn. I worked on semantic question answering and NLP in general. There I worked on various aspects of question answering over Knowledge graph like [data set creation](http://lc-quad.sda.tech/), [solutions](https://arxiv.org/pdf/1811.01118.pdf), [industrial use cases](http://jens-lehmann.org/files/2018/jurix_qa.pdf). I have recently picked up bouldering and running. So if you don't find me devouring a pulp detective novel, you will see me doing one of these. Contact me at mail\[at the rate]gauravmaheshwari.page. [*Twitter*](https://twitter.com/__gauravm) */* [*Github*](https://github.com/saist1993) / [*Resume*](/about/research/resume.md) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://gauravm.gitbook.io/about/master.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.