About me
I’m Aaditya Adhikari. I work on software and applied research, and I’m drawn to problems where how a system behaves matters as much as what it produces.
That looks different in each project. ResQNet asks what an emergency app can still do when the network is gone. Trading Lab asks whether a trading idea survives realistic fill timing and costs. The biomedical work looks at how data is processed and split before anyone reports a machine-learning score.
The tools change, but the question stays the same. What would have to be true for this result to be trusted?
What interests me
I like seeing how the parts of a system fit together. On a phone, the interface, the hardware, and the state of the network all affect each other. In a research project, I want to follow the data from raw input to feature, check how the experiment was divided, and ask what the final metric measures.
I’m also exploring databases through LogstoneDB, an early project in Go that starts from Raft and deterministic simulation.
Research experience
My summer research internship at NIT Rourkela focused on EEG-based machine learning. The project covered signal preprocessing, feature extraction, and a comparison of classifiers, with wearable use as a longer-term direction.
Work in detail
The project pages explain the methods, the trade-offs, and the limits behind each piece of work. They also separate what has been shown to work from what is only a prototype or a proposal.