Priyal Trivedi

Hi! I’m Priyal Trivedi, a software engineer, currently working as a Full Stack Developer at Analytics Vidhya. Previously, I've worked as a Project Assistant in IDeaS Lab, Centre for Product Design and Manufacturing at Indian Insitute of Science, Bangalore, a premier research institute in India. At IDeaS Lab, I was involved in building a life-cycle assessment tool to compute, analyze and visualize the environmental impact that the product has during various life-cycle phases.

I am a problem solver, curious and a quick learner!

Take a look at my Research page to know more about my work or download my resume.

I have a strong theoretical background in Computer Science, so Java and C/C++ are my weapons of choice for solving problems. My areas of interests include Healthcare Data Analytics and Android Application Development..


Analytics and Innovation Department, Caterpillar

I have had the opportunity to work as a Java Developer at Analytics and Innovation department, Caterpillar India Pvt. Ltd.

I was majorly involved in software development using Advanced Java frameworks like Spring and Hibernate. The most interesting part was managing the Operational Data Store which acts a middleware to store the real-time data received from a remote user using Telematics Technology(proprietary technology of Caterpillar). It was fascinating to see how data was used as a tool to analyze the status, fuel usage and the location of the heavy machines manufactured by Caterpillar. As a part of the team, I had developed APIs using JavaScript for user interaction. I also performed unit testing using JUnits for the various modules in the application.

Undergraduate Thesis

My undergraduate thesis was focused on image compression. Given a high resolution image to represent the landscape of device and its surroundings along with the location details transferred through radio communication at Caterpillar, the objective was to perform lossy compression while stream data over the network. Using unsupervised learning technique, k-means clustering, we could reduce the size of the image to represent the image. This compressed image essentially forms the basis for the image class thorough lossy compression by reducing the colors. This would eventually provide the image of the surroundings in reduced size on the portal from which the customer (of Caterpillar) would access the information.


Amity University     Caterpillar     Ideas lab iisc     iisc