BS in Computer Science and Data Analytics at the Indian Institute of Technology, Patna. Investigating complex systems through a lens of both structure and curiosity.
Research Passion
Exploring the depths of Machine Learning, Mathematical Modeling, and Scientific Simulations. Bridging the gap between theoretical knowledge and real-world applications.
Philosophy
Driven by curiosity and guided by logic, I believe in learning through building, experimenting, and asking the right questions. Every problem is a puzzle waiting to be solved.
Education
Indian Institute of Technology, Patna
Bachelor of Science (Honours) in Computer Science and Data Analytics
CPI: 9.12
Work Experience
Research Intern
Ahmedabad University (On-site)
Project: Understanding Decision Making and Coordination in Animal Groups
Supervisor: Dr. Jitesh Jhawar — Funded by Max Planck Society, Germany
May 2025 – June 2025
Studied collective behavior in animal groups via simulations and mathematical modeling.
Configured a multi-user Linux workstation with fstab-based persistent disk mounting, user environment
management, and centralized Anaconda deployment for shared computational workflows.
Processed and annotated video data collected daily over 4 months, labeling frames with multiple bird species for behavior and identity tagging.
Adapted YOLO architecture for multi-species bird detection and species-specific behavior analysis.
Utilized Idtrackerai to track ants and spiders, rendering trajectory data to visualize movement paths and group dynamics.
Project Intern
IIT Mandi iHub and HCi Foundation (Remote)
Apr 2025 – July 2025
Developed backend infrastructure for an educational platform using Django, including role-based access
control, REST APIs, and OAuth 2.0 authentication, supporting 1,000+ users across student and instructor
workflows.
Designed and implemented interactive dashboards for students and instructors, enabling activity tracking,
course progress visualization, role-based permissions, and secure content access with separate workflows for
each user type.
Collaborated with cross-functional teams to architect scalable backend solutions and enforce security best
practices across the platform.
Designed and trained a novel hybrid architecture from scratch combining a 3-layer convolutional stem with
8-layer Vision Transformer, addressing ViT’s known data-efficiency challenges on small-scale datasets.
Achieved 66.67% validation accuracy on CIFAR-100 (100 classes, 50K training samples) – outperforming
pure Vision Transformers by 15-20% and competitive with published hybrid architectures (CvT, DeiT)
trained without ImageNet pretraining.
Implemented comprehensive training strategies including AutoAugment, Random Erasing, cosine LR scheduling,
AdamW, label smoothing, and stochastic depth for improved generalization.
Developed an object detection model using YOLOv8n to identify and locate solar panels in low resolution satellite imagery.
Processed and annotated a comprehensive dataset of 29,625 solar panel instances with high precision.
Achieved 94.27% precision and 91.77% recall, significantly improving solar infrastructure mapping capabilities.
Implemented sophisticated object detection techniques with mean Average Precision (mAP50) of 96.8%.
Designed and deployed an optimized real-time inference pipeline, hosting it on Hugging Face for seamless accessibility and large-scale solar panel detection.
Fine-tuned Microsoft’s SpeechT5 transformer model for technical speech synthesis, achieving 25% improvement in Mean Opinion Score
through custom phonetic preprocessing pipeline for technical terms, abbreviations, and acronyms.
Optimized the baseline model to generate a native Italian voice by enhancing pronunciation, prosody, and
stress patterns in line with the phonological rules of the Italian language, significantly improving speech
quality and naturalness compared to other existing models.
Implemented 8-bit dynamic quantization to linear layers using PyTorch’s native API, reducing memory
usage by 30% while maintaining inference accuracy.
Developed CareerNavigator, a Machine Learning model that evaluates candidates' employability by analyzing key attributes and predicting suitability for a job role.
Cleaned, pre-processed, and performed feature engineering on the dataset containing 70k+ datapoints.
Designed, trained, and evaluated multiple algorithms, utilizing performance metrics such as accuracy, confusion matrix, and F1-score to optimize model effectiveness.
Selected kernel support vector machine as the best-fit model with the highest accuracy of 80% and F1 score of 0.82.
Presented findings to a panel of faculty and industry experts, receiving recognition for its innovation and effectiveness.
Developed a dynamic web application combining Optical Character Recognition (OCR) and Text-to-Speech (TTS) technologies to improve accessibility.
Integrated Tesseract for multi language OCR and Web Speech API for TTS, enabling accurate text extraction from images and high-quality text-to-speech conversion.
Designed a responsive interface with intuitive navigation, ensuring a seamless user experience.
Enabled PDF export, word/character count, and keyword search for efficient document handling.
Developed functionality for managing user profiles, such as signup, login, activity tracking, and editable data.
Developed an object detection system utilizing YOLOv5 and PyTorch, designed to capture and process individual videos and photos for object detection.
Optimized the computer vision pipeline to achieve 20+ FPS processing speed for 640x640 input resolution.
Created a visualization system to render detection results with color-coded bounding boxes and confidence scores ranging from 0 to 1.
Applied non-maximum suppression with IoU threshold of 0.3 for optimal detection accuracy.
Added support for over 80 COCO dataset object types, with confidence threshold filtering at 0.3.
Extracurricular Activities
🎹 Piano
8+ years of experience in classical and contemporary styles. Actively compose original pieces that explore and innovate across diverse genres and elements of music.
🎸 Guitar
2+ years of experience in Indian and western contemporary styles. Exploring fingerstyle techniques and songwriting.