Nabha, Punjab, India
Young enthusiastic mind.. Exploring and Learning... Already Explored Corporate... And now.. exploring research.. something hatke than traditional 🐑chal of Corporate 🙂
Jan to May 2026: CSL 111 DS Lab 6 Hours/Week
MeitY sponsored Project on Edge Cache Security
Research Scholar @IIITG
Working from Beginning on this project from last year as Software Engineer in logistics arm of LMEL to digitalize whole logistics, Beginning with reseach, Analysizing Historical Data, Visualizing data Using Tableau and PowerBi Designing Algorithms, Designing modules for Software Successfully Engineered Phase 1 of Software Working on Branding and Website A whole of Software Development Journey from scratch
As a part of Company Transition by Lloyds Group. Currently, I am working on optimizing logistics operations at Lloyds Surya Pvt. Ltd., where I utilize Power BI, and Tableau to uncover insights and present data through impactful visualizations. In my team, we've designed a relay-based truck-driver model that reduced trip turnaround time by 40%, increased truck utilization to 90%, and delivered 30% cost savings. Additionally, I designed a performance-based driver salary model using statistical analysis to ensure fair compensation, leading to improved driver satisfaction and enhanced operational efficiency.
As a Data Analyst Intern at Lloyds Logistics Pvt Ltd, an unlisted company backed by Lloyds Metals and Energy Limited, I am actively involved in developing a comprehensive salary model and designing driver scheduling frameworks. By leveraging data analysis, my work focuses on optimizing logistics operations, ensuring efficient workflows, and contributing to the company's research phase towards building a scalable logistics solution
This project involves the development of an optimized data forwarding and cache management system for a network of edge servers. The system is designed to improve responsiveness and efficiency by handling concurrent service requests using multithreading and managing resources with statistical methods and the Landlord algorithm. Our goal is to minimize latency while keeping cost as a constraint this is implemented using Landlord Algorithm. it produces better latency (upto 6.24%) and reduced cost (upto 83.05%) relative to online algorithm available