I am a Data Science professional with experience in advocating for analytics/data products. Currently I work as a Technical Advocate at Dremio, where I focus on Dremio's datalake platform along with open-source projects such as Apache Arrow, Apache Iceberg, Arrow Flight & Project Nessie. Previously, I led the Technical Evangelism team at Qlik R&D working on key developer-strategies and educating the worldwide Qlik developer community in the areas of Data Visualization & Machine Learning.
I am also a Visual Analytics researcher and my research is focused towards developing visualization techniques to understand Machine Learning models as well as multivariate data sets. Some of the tools & programming languages that I use in my work and research are: Python, SQL, NodeJS, D3.js, Qlik Sense, Plotly, Amazon SageMaker.
I completed my MSc in Computer Science at Dalhousie University, where I wrote my thesis on "Interpretability of Random Forest models"
A full-stack Visual Text Analytics app developed using Qlik Sense OSS - Nebula.js and Picasso.js. The application leverages techniques such as Word Embeddings(Word2Vec) to derive textual insights from a Cannabis Effects dataset.
This work shows how the customer churn problem can be effectively analyzed using Qlik Sense by integrating it with SageMaker. This also lays the foundation for integrating Qlik with other AutoML platforms.
Focus of this work is to effectively visualize high-dimensional multivariate dataset in a novel radial visualization. Experiments were carried out using the Spotify's song features dataset.