Computer Engineering student at the University of Tennessee, Knoxville, working on embedded systems, PCB design, and edge AI.
I build low-power sensing platforms, custom PCBs, and machine learning models small enough to run on a microcontroller. Currently, I am an undergraduate research assistant for EPIC Lab under Dr. Sai Swaminathan. I specalize in designing the hardware necessary to understand the impact of AI in resource constrained enviroments.
I'm motivated to push the envelope of technology and see what computers are capable of doing. As transistors are nearing atom-level size, it is increasingly harder to shrink the transistors to boost performance. I want to explore different modes of computing, rethink the architecture of chips, all while making them more energy efficient.
Low-power handheld edge AI platform that lets users build, train, and deploy custom models entirely on the device. Designed for non-technical users, making model training a hands-on learning experience rather than a cloud abstraction.
Runs inference in 3 MB of memory on a 5 W power budget.
Low-cost, energy-efficient, fully autonomous buoy that monitors water pollutants including nitrates, phosphates, heavy metals, and industrial contaminants. Uses colorimetric assay strips combined with on-device edge AI to estimate pollutant concentration within 96% confidence.
Deployed with rural farming communities from Tennessee to Hawaii.
Performed root cause analysis on an active production line to reduce painting defects during manufacturing, then deployed vision-based AI cameras that flag errors before a machine operator would catch them.
Cut cost of lost product by 79%.
C, C++, Python, Java, Linux, Git, MATLAB
KiCad, Cadence Virtuoso, Verilog, Xilinx FPGA, ARM, Fusion 360, LTspice, Keysight ADS
Oscilloscope, waveform generator, logic analyzer, spectrum analyzer
Relevant coursework: VLSI, Microwave Circuits, Embedded Systems, Operating Systems, System on Chip, Computer Architecture, Digital Systems Design.