- Designed a high-accuracy low-cost photosynthetically active radiation (PAR) sensor for use in large-scale IoT data collection.- Collaborated with an interdisciplinary team of biologists, chemists, and environmental scientists to generate renewable energy from ficus-indica's crassulacean acid metabolism mechanism.

- Co-Led data collection for both subvocal phoneme recognition research, utilizing facial electromyography (fEMG), and brain-wave to motor control classification via electroencephalogram (EEG).- Designed and implemented a custom circuit for automatically separating phoneme patterns.- Presented both research findings with team at an annual California neurtechnology conference, showcasing ~96% accuracy for all 44 standard English phonemes and ~70% accuracy for brain-wave motor control.- Received first place in the largest undergraduate neurotechnology competition, hosted by NeuroTechX.

- Developed an automated, software-controllable nanosecond-accurate Time Stamp Counter (TSC) distribution plug-in using Perl and proprietary HDL.- Created a customizable netlist generation system for TSC distribution, optimizing inputs, outputs, and behavior.- Integrated the module as a plug-in across GPU, mobile, and other NVIDIA platforms.- Automated workflows to enhance scalability and cross-platform efficiency.

- Assisted in middle school STEM outreach at an annual Science Alive event hosted at Gavilan College.- Coordinated an industry panel with leading physics industry professionals in materials science.- Presented on acquiring research experience to fellow undergraduates.- Supported general meeting coordination and special event logistics.

- Developed a high-accuracy synchrophasor algorithm for real-time amplitude, phase, and frequency calculation for a distributed network of smart-grid sensors, enabling automated detection and localization of power outages or fluctuations.- Simulated and characterized transformer properties including real-world losses and distortions, conducting detailed circuit analysis and high-frequency noise distortion studies to validate sensor robustness in real-world power grids.

- Trained the CEO of eRoots Analytics in C++ and OOP for a successful technical interview with RTE (France's transmission system operator). - Designed and developed a modular cloud-based platform to execute advanced grid analysis studies, significantly improving the processing speed compared to industry standards.- Integrated custom-built front-end interface using PHP and JavaScript for an enhanced user experience.- Utilized Amazon's Web Services (AWS) to ensure scalable, secure, and efficient computational infrastructure.