RAKSHIT VARU

Senior Quality Engineer with over 3.5 years of experience at Royal Bank of Canada, specializing in data analysis, process optimization, and testing automation. Adept at maximizing business performance through precise data analysis, innovative testing methodologies, and operational excellence. Proficient in SQL, Excel, and Tableau, with a strong background in creating custom tools and dashboards to drive efficiency and revenue growth. Known for strong problem-solving skills, meticulous attention to detail, and a collaborative approach to achieving business goals.

• AWS Cloud Practitioner Essentials, 04/2021
• Tableau Desktop(I/II/III), 03/2021
• Neural Networks & Deep Learning, 06/2020
• Machine Learning, 10/2017

• ARMatrix: Analyzing Association Rules, 08/2020, 04/2021, The thesis deals with a method to display association rules in the form of a 2D item-to-rule matrix. It provides a technique to do knowledge-focused analysis & rule comparison.
• Tableau related projects, 01/2021, 03/2021, Created interactive dashboards for different datasets and problems., https://public.tableau.com/profile/rakshit.varu#!/
• Classifier Performance Comparison (01/2020 - 04/2020): Developed an interface to visually analyze and compare basic classifiers with a custom 'parameterized AdaBoost' algorithm based on academic research.
• RadViz for Multivariate Data Representation (09/2019 - 12/2019): Created a radial visualization tool (RadViz) for clutter-free multivariate data representation, implementing clustering in the backend using Flask, and learning D3.js for frontend visualizations.
• Sarcasm Detection (09/2019 - 12/2019): Collaborated with a team to build a visual storyboard for sarcasm detection, leveraging machine learning, natural language processing, and Vue.js to explore and compare different models.
• Banking Website with Facial Recognition (08/2018 - 03/2019): Implemented facial recognition for enhanced security on banking websites, training a model with TensorFlow and Keras for accurate image classification, and integrating it into the site’s frontend and backend.

ARMatrix: An Interactive Item-to-Rule Matrix for Association Rules Visual Analytics, Rakshit Varu, Fernando V. Paulovich, Leonardo Christino, 04/23/2022, https://www.mdpi.com/2079-9292/11/9/1344, It deals with a method to display the association rules in the form of a 2-Dimensional item-to-rule matrix. The system also helps to further analyze the rules by the means of knowledge-based focused analysis, rule comparison and storing the filters for later access. The proposed system helps the general user with some knowledge of the association rules to navigate the rules and find the relevant information.