Pranav Bhasin
About Pranav Bhasin
Pranav Bhasin is a Software Engineer with a strong background in machine learning and data science. He has worked at various organizations, including Fortress Information Security and the University of Central Florida, where he contributed to significant improvements in data processing and system reliability.
Work at Fortress Information Security
Currently, Pranav Bhasin works at Fortress Information Security as a Software Engineer. He has been in this role since 2021, contributing to various projects aimed at enhancing software solutions. His responsibilities include devising an automated questionnaire matching system using Sentence Bert, Elasticsearch, and MongoDB, which is exposed as an asynchronous RESTful API. Additionally, he deployed a Software Bill of Materials tool that integrates with 32 package managers and GitHub APIs to ensure regulatory compliance.
Education and Expertise
Pranav Bhasin holds a Master of Science in Computer Science from the University of Central Florida, where he studied from 2019 to 2021. Prior to this, he earned a Bachelor of Technology in Computer Science from Vellore Institute of Technology, completing his degree from 2015 to 2019. His academic background provides a strong foundation in software engineering and machine learning, which he has applied throughout his internships and current role.
Background
Pranav Bhasin has a diverse background in software engineering and data science. He began his career as an intern at Crux Creative Solutions Private Limited, where he worked as a Frontend Web Developer in 2016. He later transitioned to machine learning roles, including internships at Haptik and WhatsBusy, where he gained experience in data science and machine learning applications. His role as a Graduate Teaching Assistant at the University of Central Florida further solidified his expertise in the field.
Achievements
Pranav Bhasin has made significant contributions during his internships and current position. He revolutionized the data breach reporting process, achieving a 99% recall rate and saving 30% in full-time equivalent hours annually by utilizing HuggingFace Transformers and TensorFlow. He also constructed a read/write cache using Go and Redis, which reduced database CPU usage by 20% and read latency by 40%. Additionally, he refined risk ranking algorithms, leading to a 30% reduction in computation times.