MASTER OF SCIENCE
Computer Science (Big Data Concentration)
Arizona State University, Tempe
GPA: 3.8/4.0
BACHELOR OF TECHNOLOGY
Computer Science
SRM Institute of Science and Technology, Kattankulathur
GPA: 4.0/4.0
CISCE BOARD
La Martiniere for Boys, Kolkata
ICSE Exam [10th]: 95.0%
ISC Exam [12th]: 95.5%
Kolkata, WB, India
DevOps Engineer II
Network Transformation and Service Assurance
Aug 2022 – Present
DevOps Intern
Network Transformation and Service Assurance
Jun 2022 – Aug 2022
Instructional Design Assistant
Quality Assurance Team
Jan 2021 – May 2022
The Stock Market is a challenging forum for investment. This project aims at processing large volumes of data and running comprehensive regression algorithms on the dataset to predict the future value of a stock. The purpose of this paper is to analyze the shortcomings of the current system and build a time-series model that would mitigate most of them. Using this model, anyone can monitor the preferred stock that they want to invest in and maximize profit by purchasing volume at the lowest price and liquidating the stock when it’s at its highest.
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Twitch is one of the leading live-streaming platforms for E-Sports today. In a week around 70-80 hours of gameplay per streamer is streamed and uploaded on Twitch. Since the average attention span of a viewer is not more than a couple of minutes, the streamer needs to spend hours on end editing and clipping his videos. These clips are called Montages and include game highlights like high-intensity battles and clutch plays. Using Twitch API to source videos and EasyOCR to identify target montages, GAE-enabled Montage Wizard helps to automate this process, thereby conserving precious time and resources.
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Led the design, implementation, and testing of an AWS auto-scalable cloud application that does MNIST image classification and provides results through real-time web-sockets (Socket.io) utilizing AWS S3, SQS, EC2, HTML, CSS, JavaScript, Bootstrap, Python, Flask, and other technologies. The ML-based image classifier processes thousands of images and returns the result as a tuple (Filename, Classification), displaying results as they are calculated by the different EC2 instances. Depending on the traffic of incoming user requests, the system is able to autoscale the number of app tier instances.
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With the advent of Machine Learning, OTT platforms can accurately recommend movies to watch based on our watching habits. This project aims to compare such recommendation engines using Collaborative Filtering: Matrix Factorization and Item-based KNN, and test it on the 20M MovieLens dataset to predict users' movies to watch. This study inferred that systems built on a Matrix Factorization engine have a lower error rate than KNN based engines.
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This system has been developed in 3 phases by our project group. In the 1st phase, we ran a few tests on the MiniBase architecture. In the 2nd phase, we modified the underlying structure from Tuples to Quadruples (Object-Relational). We also incorporated a few functions into this architecture, namely: BatchInsert, Query, and Report. In the final phase, we implemented Sort-Merge join functionality into our RDF Database.
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🌟 Hey there! I’m a passionate go-getter with a knack for leadership and a keen eye for detail. Since Spring 2021, I’ve been on an exciting adventure at Arizona State University, where I’m diving headfirst into the world of Computer Science, specializing in Big Data Systems. 🚀 By day, I’m lucky enough to collaborate with an amazing team at Lumen Technologies as a Network Operations Engineer 2 in the vibrant Service Assurance Transformation team right here in sunny Tempe, AZ! My daily grind is all about DevOps, Network Automation, and having a blast tinkering with chat ops and GPTs. 💻✨ When I’m not immersed in tech, you’ll find me revving up my FL5 Civic Type R, cruising to the peaceful lakeside on Sundays, soaking in the beauty of the open road. 🚗💨 I’m also a die-hard soccer fan and love hitting the poker table for some friendly competition. Let’s connect and share some good vibes! 🎉⚽️🃏
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Copyright © 2020 Sharanya. All rights reserved.
The things mentioned here are true to the best of my knowledge.
Image Source: Google Images
