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Computer scientist working in machine learning — happiest with a hard problem, an open road, and a good origin story.
I'm a computer scientist and PhD scholar at NIT Silchar, drawn to AI and machine learning — and to the messy, real-world data that makes them useful.
I like building things that work and explaining them clearly. Away from the desk you'll find me on a motorcycle, or rewatching anything with a cape and a brooding skyline.
Turning real-world sensor data into useful, robust decisions.
My doctoral work brings machine learning together with sensor data to turn messy, real-world signals into useful decisions — with an eye toward robust, deployable systems.
A deep learning classifier that detects aggressive, biased and unjust content across Hindi, Bangla, Meitei and English — moving past the usual English-only focus. Built on the TRAC-3 dataset, modelling severity of violence, role of speech, gender bias, injustice, racism and ethnicity, toward safer online communities.
IoT home automation for urban and rural settings — a cloud-supported web interface for remote control, and an offline Android + Bluetooth setup where connectivity is scarce. Built on Arduino and sensor modules.
Worked across IoT home automation — hardware integration, interface development, control-logic implementation and systematic testing and troubleshooting.
There's a clarity that only shows up past the city limits. Long rides are how I think — about research, about everything. The road doesn't take shortcuts, and neither do good ideas.
The most compelling hero isn't the strongest — he's the one who shows up anyway, prepared and relentless. Discipline over flash, preparation over luck. That mindset travels well into the lab.
Have a question, an idea, or just want to talk shop (or bikes)? Drop me a line — it lands straight in my inbox.
