Focus: Firmware, Embedded Systems, Control Systems, AI/ML for Predictive Control

Firmware & Embedded Systems:

Designed and implemented control systems for exoskeleton devices.
Developed sensor input processing and actuator output handling.
Collaborated with electrical engineers to define communication protocols and select appropriate sensors.
Control Systems & Predictive AI:

Processed comprehensive time-series sensor data with noise filtering for real-time decision-making.
Implemented data storage strategies for iterative improvement and deterministic model training.
Developed and trained machine learning models to predict user gait in real-time, enhancing system responsiveness.

•Designed and implemented a compact Gyro/Accelerometer sensor system to ensure precise tracking and stability of the Chem-E car on the X-Y plane, which improved the car’s performance and handling in dynamic environments.
•Integrated and optimized sensor placement with space constraints in mind, utilizing jumper wires and custom shields for efficient connectivity, which maximized the use of available space and minimized signal interference.
•Developed and programmed Arduino code to interface with the Gyro/Accelerometer sensor, ensuring accurate angle detection and car stability, which contributed to reliable and consistent car operation during experiments.

•Leveraged a comprehensive stack encompassing HTML5, CSS3, and JavaScript to architect and realize a dynamic, feature-rich website for the non-profit entity, which enhanced the organization's online presence and engagement.
•Orchestrated seamless collaboration with a multifaceted team of developers to orchestrate the flawless creation of the web platform, resulting in a cohesive and high-quality web solution.
•Devised interfaces adhering to user-centered design principles to accentuate usability and foster a gratifying experience for all users, which improved user satisfaction and interaction with the website.

• Developed a fitness application leveraging OpenCV and MediaPipe in Python to track body keypoints using a camera, ensuring accurate exercise form and tracking repetitions for bicep curls, lateral raises, and shoulder presses.
• Built/trained form-validation logic using real time body tracking data to compare user performance against reference positions for each exercise.
• Designed and implemented an intuitive user interface using React, enabling seamless interaction and usability for users across devices.
• Automated data preprocessing and analysis pipelines, leveraging advanced libraries to optimize performance and ensure real-time feedback.