Literature review
Mapping existing methods, evidence, and limitations before narrowing the research question.
Research · Current direction
Intelligence within
a physical universe.
I study machine learning and deep learning while following larger questions across physics, space, and civilization.
My current academic work is grounded in careful literature, defensible methodology, reproducible experiments, and clear evaluation.
Current academic work
M.Tech Dissertation · Phase I
The formal title and detailed findings will be shared when the work is mature enough to represent accurately.
2025—27Graphic Era UniversityComputer Science & Engineering
Building a defensible foundation.
Problem formulation
Defining a focused objective that can be evaluated with clear and defensible criteria.
Methodology
Planning datasets, preprocessing, baselines, candidate models, metrics, and reproducible experiments.
Initial experiments
Implementing baselines and using early results to refine assumptions and scope.
Where I'm focusing my attention.
Machine and deep learning
Model design, representation learning, evaluation, and the path from experiments to dependable intelligent systems.
Computer vision
Classification, detection, segmentation, and robust visual feature learning.
Physics-informed intelligence
Learning systems shaped by physical constraints, scientific priors, and measurable structure.
Space and civilization
Long-horizon questions around intelligence, complex life, technological civilizations, and our place in the universe.
Research meets implementation