AI Research Intern and Data Science postgraduate with interests spanning Deep Learning, AI Systems, Statistical Modeling, Machine Learning Pipelines, and Research Engineering.
I enjoy building intelligent systems that combine strong mathematical foundations with practical engineering workflows, ranging from deep learning experimentation and reproducible ML pipelines to full-stack AI applications and deployment-oriented systems.
- Deep Learning & Neural Networks
- AI Model Development & Evaluation
- Spatiotemporal Learning
- Machine Learning Pipelines
- Statistical Thinking in AI
- Reproducible ML & Experimentation
- Research-Oriented AI Systems
Python • PyTorch • TensorFlow • Scikit-learn
SQL • R • Statistical Modeling • Survival Analysis • Time Series Analysis
Docker • Git • DVC • MLflow • FastAPI
React • TypeScript • Tailwind CSS • MongoDB
Built an AI-driven chess engine focused on algorithmic decision-making, move evaluation, and optimization workflows.
Designed and deployed a responsive portfolio platform with frontend-backend integration and dynamic content workflows.
Worked on segmentation architectures, sequential modeling approaches, and research-driven experimentation workflows using PyTorch.
- When Models Lie: The Silent Assumptions Behind Accuracy in Data Science
- ISMRM Indian Chapter Conference Abstracts (2026)
💼 LinkedIn: https://www.linkedin.com/in/biswarup-majumdar
🌐 Portfolio: http://mr-rup.vercel.app
📫 Email: majumdarb104@gmail.com
“Strong models are not defined only by performance, but by how well their assumptions survive reality.”