Throughout Fall 2024 and Spring 2025, I have had the oppotrunity to work with DayDream, a Midwest technology startup with projects relating to the developemnt of XR hardware devices. I worked as part of a mixed hardware/software team, focusing on embedded systems development. As such core duties included:

• Implementing firmware with C++ to interface the ESP32-S2 SoC with rotary encoders through GPIOs and with the BMI270 IMU through I2C
• Leveraging FreeRTOS to manage 6 concurrent tasks across 2 cores, optimizing resource utilization and ensuring reliable system performance
• Designing schematics and PCB layout for a smart AR device via Altium Designer, focusing on power and communication circuits

Over the course of the summer of 2024, I had the opportunity to work at KPIT, an automotive software development company, as part of their middleware team. This team focuses on developing services and tools that allow applications running on electronic control units (ECUs) to access and interact with low-level operating system services. Key responsibilities included:

• Building a formatting tool with Python to help with implementation of MISRA C guidelines for generated C scripts, identifying over 90% of compliance violations and reducing manual review time
• Automating generation of Lauterbach PRACTICE test scripts through Excel and Python, lowering prerequisite technical barrier and accelerating script development
• Preparing project documentation and weekly presentations to keep key stakeholders apprised of progress and obtain feedback

Worked as an URA within the Physical Ultrasonics, Microscopy and Acoustics Lab (PUMA), focusing on material characterization projects which aimed to measure physical properties of materials and identify defects. As such, key responsibilities included:

• Employing data augmentation and preprocessing techniques to enhance diversity of time-series datasets with MATLAB
• Utilizing Python to implement algorithms to compute and visualize material properties from signals collected via ultrasonic testing, obtaining results within 5% of accepted values
• Building preprocessing pipeline to clean and format raw data from ultrasonic testing and prepare it for machine learning applications.