Junior at Penn State studying Applied Data Science. I work on interpretability and safety — trying to figure out what's actually happening inside language models.
I started college as a mechanical engineering major before switching to Applied Data Science at Penn State's College of IST. That pivot led me toward AI research — specifically interpretability and safety, trying to understand what's actually going on inside language models.
Recently co-first authored a paper on whether geometric properties of SAE decoder vectors can predict feature steerability — currently in submission. Now working on two new threads: applying interpretability methods to understand how models handle private information, and building practical mech interp tooling for finance. Also attending BlueDot's AI Safety program — thinking carefully about alignment and what it actually takes to make these systems safe.
Outside of research, I play poker with friends, play chess, listen to a lot of music, and just hang out. Originally from Nagpur, India. I also love astronomy and astrophotography — you can see some of my shots here.
Working with Algoverse on whether geometric properties of SAE features can predict how steerable they are — before you ever run a steering experiment. We look at neighbor density, co-activation patterns, and an alpha_star metric across features on models such as Gemma, Llama and etc. and evaluated on SALADBench.
With Penn State collaborators. Post-training quantization can quietly degrade a model's safety alignment — we're trying to pin down exactly why. We introduce a V-score diagnostic and identify read-side collapse as the core failure mechanism.
Notes on what they do
and why they matter.
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