Yuvraj Pai Khot

ABOUT

I am a final year undergraduate pursuing a Bachelor of Technology (B.Tech) in Electronics and Communication Engineering at VIT Vellore.

As a skilled multitasker with a strong work ethic, I excel in teamwork, problem-solving, and organizational skills. My dedication and reliability make me a valuable team player, always willing to take on any task to support the team.

With a hardworking and resourceful approach, I am committed to contributing effectively and efficiently to any project or challenge.

EDUCATION

Vellore Institute of Technology

2021 - Present

CGPA : 8.92

Mushtifund Aryaans HSS

2019 - 2021

HSSC (12th) : 78.83%

Sharada Mandir School

2012 - 2019

SSC (10th) : 93.5%

Sunshine Worldwide School

2009 - 2012

Primary School

SKILLS

FlutterFlow

MySQL

Java

HTML

CSS

BootStrap

JavaScript

LANGUAGES

English

हिन्दी

(Hindi)

कोंकणी

(Konkani)

मराठी

(Marathi)

EXPERIENCE

CodeChef - VIT

(Clubs & Chapters)

Led the doubt management team for CookOff 7.0, the largest competitive coding event at VIT with over 500 attendees

Agaaz 6.0

(Event at Riviera)

Spearheaded crowd management for Agaaz 6.0, a marquee event at Riviera ’23, which featured performances by 4 renowned stand-up comedians and attracted over 2,000 attendees

Digisol Systems Ltd.

(Summer Internship)

Engineered an automated PCB defect detection system by training an efficient YOLO11n model which achieved 0.94 mAP@50 for identifying 6 distinct defect types. The project involved preprocessing XML and image data and culminated in a Streamlit web application for interactive visual inspection, significantly enhancing quality control capabilities.

PROJECTS & PUBLICATIONS

SPEAK IT OUT

(Research Article on Voice Assistants)

Yuvraj Pai Khot (2022) SPEAK IT OUT ”VOICE ASSISTANTS FUTURE OF INTERACTION”, (pp: 406-413) Royal Book Publishing, ISBN : 9789391131197

Deepfake Detection using LSTM & ResNeXt

(Capstone Project)

Developed a deepfake detection system using ResNeXt for spatial feature extraction and LSTM for temporal analysis on the FaceForensics++ dataset. Designed a preprocessing pipeline to extract and align facial features from video frames.

Implemented transfer learning and data augmentation to improve model robustness against variations in deepfake manipulations.

Optimized the architecture through hyperparameter tuning and dropout regularization, achieving high accuracy in distinguishing real and fake videos.

Download Resume

PROFILES