Welcome! I am a computational neuroscientist/ machine learning engineer who aims to develop a technology to facilitate people’s learning.
I am currently a PhD student in the Neural Computation/ Machine Learning Joint Program at Carnegie Mellon University, working with Prof. Byron Yu and Prof. Matthew Smith.
My life goal is to bridge the gap between language-based learning and experience-based learning. While language allows us to share explicit knowledge effectively, there is a significant challenge in transmitting implicit knowledge gained through experiences. For instance, while math can be easily taught using numbers, symbols, and formulas, the knowledge acquired from experiences remains unarticulated and challenging to convey.
Having worked with data for over a decade, I find myself contemplating the potential of leveraging data to address this challenge. Specifically, I would like to pioneer a path toward expressing experiences in a structured, data-driven manner. Furthermore, I would like to develop an educational framework that facilitates the acquisition of implicit knowledge through data.
I believe that just as language serves as a conduit for sharing explicit knowledge, a framework can be developed to represent experiential knowledge in a data-centric form. This framework would enable us to decode the intricacies of learning from experiences and unearth the implicit knowledge residing within our minds.