I am Black In Data because I am at heart a problem solver and I believe data science is the most powerful skill set to solve the problems I’m most invested in. I particularly want to use data science and machine learning to improve our understanding, prevention and treatment of neuropsychiatric disorders. I also want to use my knowledge of data science to advocate for ethical and equitable use of data in and outside my domain.
I’m a data scientist at the Laboratory of Organismal Biology at Stanford University, where I use machine learning to analyze neural and behavioral data of amphibians. In undergrad, I studied cognitive science and neuroscience and my main interest was getting to the bottom of what “neuropsychiatric disorder” actually is.
My senior comprehensive project examined executive function deficits in neuropsychiatric disorders and explored classifying psychopathology at the cognitive level to build a bridge between symptom-based diagnostic frameworks and the neurobiological research that attempts to explain those symptoms. While writing my senior thesis I realized two things: (1) lots of data and computation beyond my current ability is necessary to understand the complexity of cognition and the nuance of neuropsychiatric disorders, and (2) definitions of psychopathology are inextricable from social context.
From there I developed my broad career goals: (1) acquire the quantitative skills needed to make sense of this wildly complex organ called the brain and the minds they (allegedly) create, and (2) incorporate social considerations into science and use science to improve society.
Because of #BlackInData and other #BlackInX groups like #BlackInNeuro, I now have an incredible opportunity to learn data science and AI techniques applied to neuroscience research, and I have been able to connect with others who are similarly invested in making science equitable so that science can improve society equitably.