Relevant Coursework:
For an exhaustive list, please see Courseworks.
Developing cutting-edge solutions in neuromorphic computing and sensor-based systems in collaboration with academic and industrial partners. Focus on enhancing real-time classification models for medical and assistive technologies.
During my internship at the WSSL lab, I was deeply involved in a pioneering project that leveraged the capabilities of the SynSense Xylo system. Our goal was to develop a sophisticated localization algorithm that could determine if a person was balanced, utilizing cutting-edge Spiking Neural Networks (SNN) and neuromorphic computing techniques. This project required a deep understanding of both the theoretical and practical aspects of neural networks and neuromorphic hardware. My contributions included optimizing the algorithm for real-time performance and ensuring its accuracy in diverse scenarios. This work not only pushed the boundaries of what's possible with neuromorphic computing but also had potential applications in fields like rehabilitation and assistive technology, where balance detection is crucial.
At Markov AI, I work as a software developer, where I played a pivotal role in crafting both a web application and an iOS app for a new social media platform dedicated to local events. This platform was designed to combine the engaging video content features of TikTok with the community-focused event organization found on Facebook Events. My responsibilities included not only coding and deploying key features of the apps but also integrating user feedback mechanisms to refine the user experience continually. Through this project, I gained valuable experience in full-stack development, mobile app creation, and the unique challenges associated with developing social media technologies that cater to dynamic, event-based content.
As an undergraduate researcher at the Center for Automation and Learning for Medical Robotics (Cal-MR) at UC Berkeley, I had the opportunity to contribute significantly to cutting-edge projects that merged the fields of automation and machine learning with medical technology. My primary role involved developing and refining algorithms that enhance the precision and efficiency of robotic systems used in surgeries. Collaborating closely with a team of expert researchers and engineers, I assisted in the implementation of machine learning models that predict and adapt to various surgical scenarios in real-time, thereby increasing the safety and success rates of medical procedures. This role not only allowed me to apply my technical knowledge in a practical setting but also to contribute to impactful research that could revolutionize medical robotics.