Electrical & Computer Engineering | Carnegie Mellon University
Architecting the hardware foundations for Physical AI from low-level systems programming in C to custom mixed-signal circuit design.
The Mission
I am an ECE student at Carnegie Mellon focused on Hardware Systems. I bridge the gap between high-level AI and the silicon it runs on.
Whether it's building transimpedance amplifiers for biometric sensing or optimizing PID control loops on embedded microcontrollers, I build the "brains" that make autonomous machines intelligent.
Some of my Favorite Projects
Engineered a magnet-on-Hall-sensor system to translate physical pressure into digital haptic feedback loops. Optimized sensing sensitivity through custom flexure structures to enable vision-free object recognition.
Designed a high-precision biometric sensor utilizing transimpedance amplifiers and RC filters. Extracted low-amplitude biometric signals from high DC offsets and implemented hardware-level signal conditioning for Arduino ADC integration.
Built a multi-stage envelope detector to decode 125kHz carriers. Focused on hardware-software co-design to facilitate real-time data decoding on resource-constrained microcontrollers.
Programmed a Raspberry Pi Pico-based robot utilizing Java to coordinate motor functions and sensor inputs. Developed a custom PID-based control system for autonomous scoring, ranking 2nd out of 32 teams with a score 69% above the field average.
Designed a 6-foot custom two-stage robotic elevator using CAD and bike-chain drive systems. Developed custom Python scripts for AprilTag detection and autonomous navigation, resulting in a 94.3% autonomous scoring success rate.
Technical Stack
Experience
Engineering bio-inspired robotic hands for high-fidelity tactile sensing. Developing a magnet-on-Hall-sensor system to translate physical pressure into digital signals for precise haptic feedback loops. Utilizing Fusion 360 to iterate through 17+ soft-robotic mold designs and 3D printing flexure-based finger structures.
Architected the full software stack for real-time communication between motor controllers and sensors. Designed a custom 2-stage robotic elevator in CAD and developed Python scripts for AprilTag detection and autonomous navigation, achieving a 94.3% scoring success rate.
Authored technical curricula for Python, Java, and Machine Learning delivered to 310+ students. Managed bi-weekly cohorts through project-based learning on Google Colab, achieving a 98.1% satisfaction rate.
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