I am an M.Tech student in Information and Cyber Security at ABV-IIITM Gwalior, expected to graduate in 2027. I also hold a B.Tech in Computer Science and Engineering and have a strong foundation in data structures, algorithms, object-oriented programming, DBMS, and software engineering fundamentals.
I build, test, and deploy full-stack web applications using Python, FastAPI, Django, Django REST Framework, JavaScript, TypeScript, React, Next.js, PostgreSQL, and Redis. My backend experience includes asynchronous APIs, RESTful services, SQLAlchemy, Pydantic, JWT authentication, rate limiting, and database design.
I have built production-oriented projects involving job ingestion pipelines, authentication systems, mock interview workflows, resume analysis, coding practice, and system-design evaluation. I focus on reliable implementation through automated tests, CI checks, production log monitoring, performance optimization, and debugging failed scheduled processes.
I am experienced with unit, integration, regression, and smoke testing using pytest, as well as CI workflows with GitHub Actions. I have containerized applications with Docker Compose and deployed services using Vercel, Render, Neon, and Upstash.
My research work focuses on MedSAM2-based 3D medical image segmentation and knowledge distillation. I evaluate segmentation quality, inference efficiency, and label efficiency against nnU-Net, combining my interests in machine learning, healthcare AI, and systems engineering.
I also contribute to open source, including performance optimization work for sktime and documentation improvements for PyTorch Lightning. I enjoy solving challenging algorithmic problems, reflected in my LeetCode Knight badge, 1878 rating, and more than 600 solved problems.
I develop MedSAM2-based 3D medical image segmentation approaches and knowledge-distillation methods under the guidance of Dr. Narinder Singh Punn.
I evaluate model performance using Dice score, inference efficiency, and label efficiency, benchmarking the work against nnU-Net.
I led a machine learning bootcamp at LNJPIT and trained more than 500 students through over 20 end-to-end machine learning projects.
I supported learners through practical project development and helped them gain hands-on exposure to applied machine learning workflows.
I contributed a batched least-squares fitting implementation for the HurstExponentTransformer in sktime. A local benchmark using 10,000 points measured approximately 277x faster fitting, with a tradeoff of higher temporary memory consumption.
I validated the change locally using 55 focused tests and 310 estimator checks. The pull request is open and has not yet been merged.
I updated EarlyStopping documentation examples for PyTorch Lightning through an open pull request.
I validated the documentation changes by running a local Sphinx HTML build and two page doctests.
M.Tech, Information and Cyber Security
In progress with an expected graduation year of 2027. GPA: 7.43.
B.Tech, Computer Science and Engineering
Completed Bachelor of Technology in Computer Science and Engineering. GPA: 8.06.
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