Ph.D. candidate at École Polytechnique Fédérale de Lausanne (EPFL)

        I'm a second-year Ph.D. candidate at EPFL, focusing on low-power digital/mixed-signal integrated circuits for closed-loop neural interfaces. My academic journey began with a B.Tech. in electronics engineering from the University of Petroleum and Energy Studies, India (2017), followed by an M.Sc. in computer engineering from Delft University of Technology, Netherlands (2019). I've worked as an Experimental Physicist in quantum computing at Lawrence Berkeley National Laboratory and UC Berkeley, and later as a Fellow at CERN, Geneva. Since 2024, I've been pursuing my doctorate at the Integrated Neurotechnologies Laboratory under Prof. Mahsa Shoaran.

Alongside my PhD, I'm actively building Hackframe and sometimes I find time to write for Periodic Perplexities. Earlier, I also created FLORA: Reading tracking made simple, a reading statistics platform I originally built to monitor my own reading progress and now maintain as a public tool.

Beyond academics, I enjoy white hat hacking on networks, playing the piano, and making art — check out my work of Cosmic Genesis. In my free time, I take up reading and am passionate about running, swimming, and hiking, combining my love for nature and exploring historic cities.

A quirky personal goal of mine is to live to at least 106 — making me one of the rare few to have lived and experienced three centuries! yayy!

News

March 28, 2025	A 32-Channel 196μW Logarithmic SoC for Brain Network Connectivity Extraction and Adaptive Psychiatric Symptom Classification (paper ↗) is accepted at VLSI Symposium 2025 in Kyoto, Japan!
December 23, 2024	MiBMI Brain-to-Text SoC is featured among top 10 research at EPFL in 2024 ↗
August 01, 2024	A 2.46mm2 Miniaturized Brain-Machine Interface (MiBMI) Enabling 31-Class Brain-to-Text Decoding (paper ↗) is accepted at Journal of Solid-State Circuits.
October 11, 2023	MiBMI: A 192/512-Channel 2.46mm2 Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion through Distinctive Neural Codes (paper ↗) is accepted at ISSCC Conference 2024 in San Francisco, U.S.A.