Matthew Bejtlich © 2020
Matthew (b. 1991) grew up in the quiet corn fields and pine trees of rural Massachusetts. He is a photographer, musician, composer, and data scientist with an interest in sustainability and storytelling for impact. He especially enjoys working across the domains of music, fashion, food, and the natural environment. His research exists at the interface of data science, experience design, human-computer interaction, participatory design, data ethics and data provenance, and creativity support tools.
For the past ten years, Matthew has been deeply invested in the global electronic music scene, producing electronic music as part of the audio-visual duo Dayspired. He is a current partner at Rhythm Section International (London, UK), where he is being mentored by record label founder Bradley Zero (DJ at BBC Radio 1, NTS Radio, Boiler Room). Soon he will also be exploring more minimal and atmospheric directions in solo work released under his own name.
Matthew recently graduated with his MFA from Rhode Island School of Design (RISD) after receiving his MS in Data Science from Brown University. He was a graduate researcher at Brown’s Human-Computer Interaction Lab and the head teaching assistant for the class Machine Learning and Design. He is currently open to full time work and freelancing opportunities.
Connect
Email, Resume, Music, Instagram, LinkedIn, GitHub
Client list
He has worked or collaborated with Google, Holition, Warren Alpert Medical School of Brown University, British Fashion Council, Brown University HCI Lab, and the Naval Undersea Warfare Center.
Education
Rhode Island School of Design
MFA Graphic Design, 2021
Brown University
MS Data Science, 2018
University of Massachusetts Dartmouth
BS Electrical Engineering, 2015
PREDICTING HOUSEHOLD INCOME IN NYC
The goal of this collaborative project was to use census data to create two sensible models for predicting median household income and income per capita in New York City and the surrounding area.
Two analytical approaches, Stepwise Selection and Shrinkage Methods, were used to select a subset of predictors. Using the variables selected, we performed multiple linear regression to model median household income and income per capita as a linear combination of the potential predictors. Starting from that baseline model, we added interaction terms and performed transformations on variables based on exploratory analysis and model diagnosis. Finally, an interpretation for each model was provided.
Client: Self
Date: 2018
Class: Statistical Learning, Brown University
Collaborators: Yiwen Shen and Zhiwei Zhang
Instructor: Alice Paul, PhD
The goal of this collaborative project was to use census data to create two sensible models for predicting median household income and income per capita in New York City and the surrounding area.
Two analytical approaches, Stepwise Selection and Shrinkage Methods, were used to select a subset of predictors. Using the variables selected, we performed multiple linear regression to model median household income and income per capita as a linear combination of the potential predictors. Starting from that baseline model, we added interaction terms and performed transformations on variables based on exploratory analysis and model diagnosis. Finally, an interpretation for each model was provided.
Client: Self
Date: 2018
Class: Statistical Learning, Brown University
Collaborators: Yiwen Shen and Zhiwei Zhang
Instructor: Alice Paul, PhD
