Featured
Classifying Musical Genre from BOLD fMRI
Neuroscience Honors Thesis Poster
Comparing Centrality and Behavior in Online vs. In-Person Social Networks
Social Psychology Data Blitz
Data Preparation and Analysis of 3D-Reconstructed-Neuronal Data
Data Wrangling and Analysis
Masked Multivariate Pattern Analysis (MVPA) Code
Data Analysis
A Data-Driven Map of New York City's Demographic Composition
NYC Department of Health & Mental Hygiene Internship Project
matthewfam.com
Personal Website/Portfolio
A poster, accompanied by voice-over audio description, presenting a portion of my neuroscience honors thesis. This presentation focuses on my attempts at predicting the genre of music a subject is listening to from the corresponding brain images. It includes linear modelling as well as multivariate prediction via machine learning. The poster reveals the presence of higher-order mental representations of music, separate from general sonic stimuli, as well as the brain regions responsible for that representation.
A research project exploring how people become popular online, and whether those behaviors predict popularity in person. This work spanned over 3 years with Dartmouth College's Social Systems Lab, working as Presidential Scholar alongside Christopher Welker under the supervision of Dr. Thalia Wheatley. This work was presented as a data blitz keynote at the Society for Personality and Social Psychology's (SPSP) Annual Convention in 2022, as part of the Bringing Intragroup Processes Back to Social Psychology pre-conference. Code for data preparation can be found here.
My main contribution to my first publication, "Altered synaptic ultrastructure in the prefrontal cortex of Shank3‐deficient rats," was quickly turning around aggregated averages of 3D reconstructions from images of dye-filled neurons in treated brain tissue. Though the statistics wasn't too difficult to handle, sorting through the messy data was the main hurdle—automating an algorithm to access data stored in over 200 separate excel files, not organized into clean data columns, nested in two layers of folders, with identifying information at each level of the filepath. If you'd rather not look at code, here's a written outline of how I went about navigating the mess.
A sample of code from my undergraduate Honors Neuroscience thesis at Dartmouth College, advised by Professor Michael Casey and Professor Richard Granger: "Comparing Decoding Approaches for Classifying Musical Genre from Blood-Oxygen-Level Dependent (BOLD) Functional Magnetic Resonance Imaging (fMRI)". It includes Python code written in Jupyter and run on Dartmouth's Discovery cluster environment, masking a set of brain data and running multivariate pattern analysis (MVPA) on those masked brain images.
Working with the Office of Informatics and Research at New York City's Department of Health & Mental Hygiene, I utilized census data to put together an interactive, data-driven, demogrpahic map of the city for use throughout the department. Without any experience using Geographic Information Systems (GIS), I was tasked with establishing the foundation of a project to be used for epidemiological studies targetting the city's population. My supervisor handed me a GIS textbook as soon as I walked out of my interview for the position on a Friday afternoon. I spent my bus-ride home and the rest of the weekend with my head in that book. By the time I came in on Monday . . . I had progressed from cover to cover. The rest is history.
You guessed it! This site you're currently viewing is an example of my own work; everything from the design and animations to the code, search engine optimization (SEO), and hosting setup was done by yours truly (with the help of the interwebs, of course). It started as an attempt at making a website with an online site-building service but quickly descended into a spiral of teaching myself HTML, CSS, and JavaScript to build the site from the ground up. It's a work in progress . . . but isn't everything? Enjoy!
