I am currently transitioning from academia to the neurotechnology industry, motivated by a passion for understanding how physical processes in the brain give rise to mental phenomena and using that knowledge to improve human health. My journey began with a Psychobiology B.S. from UCLA and has since included diverse roles that have strengthened my competencies in neuroscience, engineering, and data science.

I have grown as an independent researcher, starting as a research technician at UCSF and WUSTL and progressing to research data analyst positions at UCLA and Cedars-Sinai. Most recently, as a neuroscience Ph.D. trainee at Mount Sinai, I undertook a proof-of-concept experiment from the ground up. This included designing and implementing the experiment, developing hardware for data collection, applying machine learning techniques for analysis, and using chemogenetics to explore hippocampal circuit involvement.

Currently, I am deepening my skills through a part-time Master’s program in Data Science at UT Austin, with a focus on decoding algorithms for neural signal processing using machine learning techniques. My long-term goal is to translate experimental neuroscience into the engineering of neural interfaces that can effectively address complex neurological and psychiatric disorders.

I am eager to gain hands-on experience in real-world clinical settings, where I can gain valuable patient-provider insights to complement my technical skills. I hope to bring this experience back to industry R&D, contributing to the development of neuroscience-informed, patient-centered, and clinically validated neurotechnologies.

I am deeply thankful for the educational and research opportunities that have guided me thus far. I hope to honor these experiences by giving back to humanity through meaningful innovation in neurotechnology.