I study applications of machine learning and artificial intelligence for physical layer waveform and receiver design in wireless and optical communications.
Designing waveform bases that reorganize how symbols interact in fractional delay–Doppler channels, so a receiver can focus on a limited number of dominant interactions.
Comparing learned designs with OTFS and AFDM under matched channels and receiver budgets to understand where adaptation helps.
Jointly learning transmit and receive bases to reduce interference from residual carrier-frequency offset, alongside a model-guided receiver that refines symbol estimates.
The work explores the tradeoff between communication reliability and peak-to-average power ratio.
Learning constellation geometry and receiver decisions together under LED clipping constraints, including modulation orders beyond the usual powers of two.
This allows finer control of transmission rate in visible light communication systems.
IEEE MILCOM 2026
IEEE ICMLCN 2026
Communications & Networks Connect 2024
IEEE Communications MagazineSubmitted
IEEE ICMLCN 2026
University at Albany — SUNY Albany, NY, USA
University at Albany — SUNY Albany, NY, USA
Punjab Engineering College Chandigarh, India
University at Albany — SUNY Albany, NY, USA
Through the university’s collaboration with IBM, developing learned waveform bases for fractional delay–Doppler channels and evaluating them against OTFS and AFDM under matched receiver budgets.
IEEE Transactions on Communications
University at Albany — SUNY Albany, NY, USA
University at Albany — SUNY Albany, NY, USA
Supported student programming, communications, orientation, and student-leader development for The Honors College.
University at Albany — SUNY Albany, NY, USA
Represented the university in prospective-student webinars and supported applicants and newly admitted graduate students.
Kolligate Seattle, WA Remote
Busywizzy Gurugram, India
Quantela Bengaluru, India