About

Education, background & research focus

Indian Institute of Technology Madras

I'm a second year M.Tech student in Data Science and AI at IIT Madras, currently working as a Research Assistant under Professor Arun Rajkumar. My interests sit at the intersection of machine learning theory and practice, particularly rank aggregation and statistical learning alongside a strong hands on background in building and deploying full-stack and MLOps systems.

Education

Academic timeline

Jul 2025 — Present

M.Tech, Data Science and AI

Indian Institute of Technology Madras

Second year. Working as Research Assistant under Prof. Arun Rajkumar on machine learning, rank aggregation and statistical learning.

Aug 2024 — May 2025

M.Tech, Computer Science & Engineering

Indian Institute of Technology (ISM) Dhanbad

Completed one year before moving to pursue Data Science & AI specialization at IIT Madras.

Jul 2020 — 2024

B.Tech, Computer Science & Engineering

Kalinga Institute of Industrial Technology (KIIT)

Awarded the Study in India (SII) scholarship by the Ministry of Education, Government of India. Built the KIIT International Student Portal (MERN stack) as a major project.

2018

Higher Secondary (XII)

Govt. Ashek Mahmud College, Jamalpur
2016

Secondary (X)

Jamalpur Zilla School, Jamalpur

Coursework & Achievements

Relevant coursework

Secured an 'S' (10 out of 10) grade in Machine Learning Operations Lab.

Foundation of Machine Learning Mathematical Foundations of Data Science Data Analytics Laboratory Data Structures for Data Science Machine Learning Operations Lab Modern Computer Vision Big Data Artificial Intelligence Natural Language Processing
Hackathon

Participated in Agglomeration 1.0, organized by the CSE Society, IIT (ISM) Dhanbad — 18–19 Jan 2025.

Research Interests

What I work on

Machine Learning

Designing and evaluating learning systems that hold up on noisy, real-world data from classical models to modern pipelines.

Rank Aggregation

Combining preference and ranking signals from multiple sources into consistent, reliable orderings.

Statistical Learning

Grounding model choices in statistical theory, understanding guarantees, trade-offs and failure modes.

MLOps & Systems

Shipping ML responsibly: versioned data, tracked experiments, orchestrated pipelines and live monitoring.

Technology

Tools & stack

Languages, ML tooling, MLOps infrastructure and full-stack web technologies I work with regularly.

C / C++ Python JavaScript React.js Node.js / Express.js Scikit-learn Pandas / NumPy Matplotlib / Seaborn FastAPI PyTorch Apache Spark / PySpark Ray PyArrow MongoDB / MySQL Firebase MLflow Apache Airflow Docker Prometheus / Grafana Alertmanager DVC HTML5 / CSS3 / Tailwind REST APIs / JWT / OAuth Git / Git LFS / GitHub Jupyter Notebook Pytest Linux / VS Code