About
Curiosity, mathematics, and building things
I’m an undergraduate in Mathematics and Computing at Manipal Institute of Technology (2024–2028, expected). I’m drawn to problems that demand both theoretical reasoning and practical experimentation.
Mathematics gives me a language for describing patterns and relationships; computing makes it possible to test those ideas. Machine learning interests me because learned systems can acquire useful behavior without being explicitly programmed with every rule—and because that raises hard questions about optimization, generalization, and internal representations.
I learn best by implementing ideas, debugging them, measuring what happens, and revising my understanding. I want to become someone who can formulate a difficult question, design a convincing experiment, implement it carefully, and communicate what the evidence supports.
Research interests
AI safety and mechanistic interpretability. I want to understand the mechanisms inside neural networks and the limits of inferring behavior from outputs alone. This is a developing research interest, not a claim of established expertise.
Representation learning and foundation models. I’m interested in how models acquire reusable features and how learned representations transfer between tasks.
Computer vision and generative models. My hands-on work includes detection, segmentation, diffusion models, and variational autoencoders.
Reinforcement learning and optimization. I’m interested in how agents learn through interaction and how optimization choices shape their behavior.
How I work
- Understand, don’t just use.
- Study the mathematical and conceptual foundations behind the tools.
- Evidence over hype.
- Describe what experiments demonstrate, including limitations and failures.
- Build to learn.
- Use implementations as a way to test and deepen understanding.
- Stay intellectually honest.
- Distinguish reproductions, exploratory studies, and novel contributions.
- Keep exploring.
- Treat this site as an evolving record rather than a fixed professional identity.
Outside coursework
I enjoy competitive programming, algorithmic problem-solving, philosophy, and broader questions about intelligence and the relationship between humans and AI. I’m especially interested in ideas that cross disciplinary boundaries and can be turned into testable models or working prototypes.
Where I’m headed
I’m seeking meaningful research internships and collaborations, and I’m considering graduate study in AI. I’m interested in environments where mathematical reasoning, empirical investigation, and strong engineering all matter.