About

My professional interests lie in RF, analog, and mixed-signal hardware, particularly at the point where idealized circuit behavior meets physical implementation. This work spans discrete RF and microwave circuit synthesis, high-speed data conversion and clocking, and the parasitic, coupling, and layout effects that ultimately determine whether a design succeeds in hardware.

Central to these interests is the gap between simulation and measurement. Closing that gap requires more than tuning a circuit until it meets specification; it requires understanding why the circuit behaves as it does. That means tracing performance from device physics and architectural decisions through physical implementation and bench verification, then using measured discrepancies to refine both the design and the models behind it.

Alongside my role as a Principal Engineer at Northrop Grumman, I am pursuing a PhD in Electrical Engineering at Texas A&M University. My research focus is applied machine learning for RFIC design — accelerating design-space exploration, surrogate modeling, optimization, and verification of high-frequency integrated circuits.