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computational social & cognitive neuroscientist in training.

currently: phd student in luke j. chang’s cosan lab @ dartmouth

formerly: lab manager of michael j. frank’s lab of neural computation + cognition &
undergrad research assistant in amitai shenhav’s lab @ brown

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.

research interests

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.

resources

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:

- useful web skills

- the missing semester of your CS education

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.

- commitizen & commitizen-emoji

simplify & standardize your git commit messages and/or make them fun with some emojis!

- honeycomb (jsPsych + react + electron task creation boilerplate)

have you ever programmed a task in say, psychtoolbox or psychopy for in-lab use, and then gone through the trouble to program the same task in javascript for online crowdsourcing? ever wanted a task creation pipeline that would permit flexible online crowdsourcing and in-lab (with triggers) deployment all-in-one? do you want to know how to turn your computerized task into an executable app? or need to send triggers in javascript? look no further!

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 programming for psychologists

- python tools and good practices

python-specific tutorials on development (testing, packaging, black) and data pipelines. also includes some meta resources to improve productivity and streamline workflows.

- sutton & barto intro RL exercises in python

- 10 simple rules for behavioral modeling (wilson & collins, 2019)

- naturalistic neuroimaging

- analyzing fMRI data (dartbrains)

- mathematics for machine learning

- mathematical tools for neuroscience

- statistics and data science

- data science and statistics (ritwikmath)

- the essence of linear algebra (3blue1brown)