computational social & cognitive neuroscientist in training.
currently: phd student in luke j. chang’s cosan lab @ dartmouth
outside of research, i enjoy scouring spotify for new releases, ruminating while showering, and playing league of legends (if you play, please let me know!). lately, i’ve been loving yoga classes and learning how to snowboard. in the pre-covid times, i also enjoyed spending my time indoor rock climbing and training in lyra/aerial hoop.
i often think about how we cannot access or perceive another's reality given our inability to share brains, rendering our experienced realities subjective and "incomplete." for this reason, i am primarily interested in the neurocomputational mechanisms underlying our brain's ability to extract relevant features from noisy environments (especially the social domain, where another person's mind is arguably the ultimate form of uncertainty), allowing for adaptive learning and decision-making.
i am also interested in individual differences in belief updating (i.e., the degree to which we integrate new information and uncertainty in updating our internal models of how the world works) and how we remember and communicate information to other people, and how this all potentially connects to affective disorders. to explore these interests, i aim to use a combination of behavioral experiments, computational modeling, and neuroimaging.
some of my pre-grad school projects, which have heavily informed my interests and training, investigate 1) active information-seeking behavior and uncertainty computations in obsessive-compulsive disorder (OCD) and 2) the role of perceptual uncertainty in reward learning and episodic memory.
in our current era of information overload, it can be overwhelming to learn a new skill, method, or concept when we aren’t sure which resource(s) to draw from. (at least, this is oftentimes the case for me!) to potentially help, below is the start of my curated list of resources which i am currently using, will use, have used, or find myself referencing regularly. eventually, i may categorize them or maybe write about example use cases in the form of blog posts, but for now this is what we’re working with:
helpful for learning more about the in-terminal text editor vim, terminal commands in general, git version control, and other misc. topics computer science courses don’t explicitly cover.
simplify & standardize your git commit messages and/or make them fun with some emojis!
Provenza, N.R., Gelin, L., Mahaphanit, W., McGrath, M., Dastin-van Rijin, E., Fan, Y., Dhar, R., Frank, M.J., Restrepo, M.I., Goodman, W.K., and Borton, D. Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use. Brazillian Journal of Psychiatry.
python-specific tutorials on development (testing, packaging, black) and data pipelines. also includes some meta resources to improve productivity and streamline workflows.