|Birth of interests|
|Pursuing theoretical neuroscience through physics|
|Pursuing cognitive science through machine learning|
|Using my research for outreach|
My interest in the mind was somewhat inherited from my parents. Both well-read, socially aware, and quite vocal, throughout my childhood, they constantly emphasized how people’s actions were manifestations of their cumulative histories. My mother, a cultural anthropologist, made it a theme in my childhood to deconstructively analyze people as products of their environments and experiences.
At the same time, I experienced the life of a foreign child in a homogenous, upper-middle class, conservative white community. Because of the physical and cultural differences between my peers and me, I constantly grappled with social exclusion. The social and psychological analysis I learned from my parents became the tools I used to cope with this.
Eventually, I realized that we, as people, are largely unaware of the behavioral patterns we exhibit. Pursuit of the origins of these patterns led me to the brain, which constantly receives and parses massive amounts of information to create a representation of the world we live in and to provide the basis for our behavior in it.
While in NYC, I was fortunate to attend Brooklyn Technical High School, where, through the applied physics major, I discovered my disposition towards analytic and axiomatic reasoning. Later, in college, I decided to pursue physics in order to understand the brain; Its reductionist analytical approach fit my analytical preferences as it seemed like the natural science’s conjugate of the deconstructive analytical approach I learned from my mother. After coming across theoretical neuroscientist and physicist Christof Koch’s research attempting to reduce behavior and cognition into biophysical mechanisms, I decided my long-term pursuit would be theoretical neuroscience.
In my senior year, I realized that I didn’t believe in much of the theoretical neuroscience research I saw which attempted to correlate statistical properties of the brain’s physical dynamics to the behavior and cognition it produced. At the same time, through discussions with a professor I worked with, I learned about machine learning, a field in which models are built that utilize learning algorithms in order to identify the structure of data and learn from it. Learning algorithms seemed like the missing piece of the puzzle necessary to understand behavior and cognition.
When my focus shifted away from biophysical mechanisms towards learning algorithms, I realized that I was more interested in cognitive science than neuroscience. My current goal is to add machine learning to my analytical repertoire. With it, I hope to study other fields like psychology, linguistics, and economics to uncover the learning algorithms employed by the brain and to be able to emulate them. Emulation would be my way of validating my speculation of what algorithms are employed by the brain (with a proxy measurement for success being the performance of the algorithm).
I also believe that understanding what they brain’s learning algorithms are and how they function will help us to combat some of the social issues our society faces. For example, the same class of algorithms may be responsible for both the generalizations humans do when creating ensembles of objects and the generalization humans do when they extrapolate small amounts of data about members of social groups to create social stereotypes. Understanding these algorithms may help us combat issues like social stereotyping, which harm many communities to whom I feel membership: Latino, black, and indigenous. The effects range from those that are overt such as the disproportionate killing of my black community by police officers because of stereotypes surrounding black people, to those that are covert such as the inferior treatment of blacks and Hispanics because of their assumed lack of intelligence or capability.