Hey! 👋 I'm a Computer Science graduate with an interest in Machine Learning, Computer Vision, and AI.
Most of my work has been around building ML projects and experimenting with different ways of solving problems, from computer vision and NLP to time-series prediction and graph-based systems.
I also enjoy working on the engineering side of ML, including APIs, Docker, experiment tracking, and data/model pipelines.
Languages
Machine Learning & Deep Learning
Computer Vision
NLP & Information Retrieval
Data & Scientific Computing
MLOps & Deployment
Backend & Databases (Have experience)
Tools & Platforms
An end-to-end ML project for predicting taxi pickup demand across NYC zones.
I worked on the data processing and feature engineering, trained Random Forest and XGBoost models, and used Optuna for tuning. The project also uses MLflow for experiment tracking and DVC for data and pipeline versioning. The final setup is containerized with Docker.
Python · Pandas · scikit-learn · XGBoost · Optuna · MLflow · DVC · Docker
Spatial Visual Reasoning
A personal project I'm currently working on around computer vision and graph-based reasoning.
The idea is to generate graph representations from images and use them to answer questions about relationships between nodes, whether two nodes are connected, paths between nodes, and shortest distances.
I'm experimenting with combining visual models with graph and transformer-based approaches for this.
Python · PyTorch · NetworkX · Computer Vision · Transformers
Intelligent Vehicle Recognition System
My Master's thesis project, focused on building a vehicle recognition pipeline using several computer vision models together.
The system uses YOLOv8 for vehicle detection, EfficientNet for vehicle classification, and PaddleOCR for license plate recognition. I also worked on vehicle color classification and handling multiple vehicles in the same image.
The work was published at Springer in 2025.
Python · PyTorch · YOLOv8 · EfficientNet · OpenCV · PaddleOCR
A semantic search project for finding related scientific papers using document embeddings.
I used MiniLM-L6-v2 to generate embeddings and FAISS for similarity search. The system was evaluated using metrics such as MRR and Recall@K.
Results included 0.8895 MRR, 0.855 Recall@1, and 0.932 Recall@5.
Python · Sentence Transformers · FAISS · NLP
One of my earlier computer vision projects, focused on estimating disease severity in plant leaves.
I used K-means clustering and image processing techniques to separate diseased regions and estimate the severity of the disease.
The work was published in Expert Systems in 2023.
Python · OpenCV · K-means · Image Processing
M.Sc. Computer Science, AI Stream University of Windsor · 2023–2026
B.Tech. Computer Science Manipal University Jaipur · 2018–2022
- Machine Learning
- Computer Vision
- Deep Learning
- NLP and Information Retrieval
- Graph-based ML
- Multimodal AI
- MLOps
