Documentation of research experiments, technical explorations, and learning notes from my journey in computer vision and machine learning.
An ongoing write-up documenting the architecture of a modular, experimental face detection and recognition pipeline: detector options, preprocessing, enrollment and a Gradio-based UI, with observations framed as personal experimental results.
Personal study notes exploring how adversarial examples affect machine learning models, summarizing existing literature and small-scale hands-on experiments as part of an ongoing learning journey.
Technical documentation of a full-stack platform for running and presenting personal ML experiments: REST APIs, experiment pages, and demo integration built with Next.js, Node.js, and Python services.
Write-ups documenting hands-on experiments and how the prototypes are built.
Architecture and implementation notes for the systems behind the experiments.
Personal study notes from reading about and experimenting with new concepts.
How computer vision and machine learning experiments grew alongside development work
Pursuing an MCA at IGNOU alongside work, with self-directed computer vision and ML projects.
Face detection and recognition prototypes, in-browser object detection, and study notes on adversarial ML.
Building web applications, from personal projects in late 2024 to professional work since January 2025.
As an early-stage researcher pursuing my MCA, this section documents my learning journey through experiments, technical explorations, and research notes. These are not formal publications, but honest documentation of my research development process.
Current Status: Learning and experimenting • Goal: Building research foundations • Focus: Combining development skills with research