Research Assistant (Pre-Doctoral)

University of Pennsylvania

Biography

I am a Research Assistant at the Computational Social Science Lab at University of Pennsylvania. I previously worked at Microsoft Research, NYC in the Computational Social Science group. I have a Masters of Computational Social Science from the University of Chicago with my thesis research on using deep learning to estimate the memorability of images. As an undergraduate at Washington University in St. Louis, I majored in Physics and Economics, and minored in the Philosophy of Science. My work focuses on developing novel methods for social scientific concepts. My hobbies include analyzing video games, cooking, and playing classical guitar.

This site is intended to be a place for me to post my projects, some personal, some academic. They are mostly computational in nature, but some are philosophical.

Interests
  • Artificial Intelligence
  • Computational Game Theory
  • Information Science
  • Cultural Patterns
  • Information, Its Spread, and Individual Epistemology
Education

  • MA in Computational Social Science, 2021

    University of Chicago

  • BA in Economics and Physics, 2019

    Washington University in St. Louis

Skills

R
Data Science
Physics
Artifical Intelligence
Economics
Python
Game Design
Philosophy of Science
Writing
Linux
Rust
Data Engineering

Experience

 
 
 
 
 
Predoctoral Researcher
University of Pennsylvania, Computational Social Science Lab
Aug 2022 – Present Philadelphia, PA

Research and Development on multiple projects across the behavioral sciences. Projects include:

  • Examining past behavior of News Publishers to better understand the speed of the news cycle.
  • Constructing a “News Observatory” to monitor news publishers’ behavior online.
  • Building a “Living Journal” to aid in the publication of social scientific research, and improving the rigor of non-scientific writing.
    • Includes building a new statistical framework for use on the web, authorship management systems, and site management tools, all with a KISS-focused structure.
 
 
 
 
 
Predoctoral Researcher
Microsoft Research
Jun 2021 – Jul 2022 New York, NY

Performed research on multiple projects across the behavioral sciences. Projects include:

  • Examining past behavior of News Publishers to better understand the speed of the news cycle.
  • Analysing text data using deep learning to better understand market penetration of durable goods.
  • Project Ratio
 
 
 
 
 
Research Assistant
University of Chicago, Biological Sciences Division, Department of Medicine
Jun 2020 – Jun 2021 Chicago, IL
Develops an on-line system for evaluating cognitive function.
 
 
 
 
 
Research Assistant
University of Chicago, Brain Bridge Lab
Mar 2020 – Jun 2021 Chicago, IL
Develops Computer Vision models for understanding memory and memorability. Currently focused on developing novel techniques for predicting the memorability of an image.
 
 
 
 
 
Freelance Data Scientist
Upwork
May 2019 – May 2020 St. Louis, MO / Chicago, IL

Provided Data Science Services to Various Clients. Responsibilities include:

  • Interviewing Potential Full-Time Data Scientists
  • Building Statistical Learning Tools
  • Natural Language Processing Analysis
  • Machine Learning Development and Deployment
 
 
 
 
 
Programmer/Data Scientist (Internship)
Washington University in St. Louis: Alumni and Development
May 2018 – Aug 2018 St. Louis, MO
Continued development of previous non-scientific automation. Created new data models for donor identification. Other data analysis and visualization projects.
 
 
 
 
 
Real Estate Analyst (Internship)
Kairos Investment Management
May 2017 – Aug 2017 Rancho Santa Margarita, CA
Wrote automation programs for data processing, and constructed a model for optimal rent estimation. Built data mining programs for continued use by analysts.
 
 
 
 
 
Economics Simulation Programmer
Washington University in St. Louis: Department of Economics
May 2016 – Aug 2017 St. Louis, MO
Built macroeconomic simulations for teaching of Economics 4121. Wrote simulations in Mathematica for the ISLMFE model, the Solow-Swan model, and permutations thereof.

Recent Posts

Comparing ResMem and MemNet
In my previous posts about Memorability (see the project link above), I’ve been talking about the performance of my models fairly matter-of-factly. I’ve been comparing their scores on things, reporting them in abstracts, and talking about how one model performs better than another, and why I think that is happening.
Last updated on Nov 2, 2021 19 min read
Project
Collaborative Filtering and Innovation: A Case Study from Steam Recommendations
Introduction The modern world is flooded with options; which TV show to watch, which video games to play, which espresso machine to buy. E-commerce is characterized by its quantity. This
Last updated on Nov 2, 2021 11 min read
ResMem and M3M
In my last post on computer vision and memorability, I looked at an already existing model and started experimenting with variations on that architecture. The most successful attempts were those that use Residual Neural Networks.
Last updated on Nov 2, 2021 5 min read
ResMem Release
A user-ready version of ResMem is now available on PyPI! The model included in the package is designed to estimate the memorability of an input image but is not intended for feature space analysis.
Last updated on Nov 2, 2021 3 min read
MemNet: Models for Predicting Image Memorability
Memnet1 was an attempt to build a neural network-based model to predict the memorability of an image. This attempt was carried out by Khosla et al. at the Computer Science and Artificial Intelligence Labs at MIT to moderate success.
Last updated on Nov 2, 2021 7 min read
See all posts

Projects

Last updated on Nov 2, 2021
Computer Vision for Memorability

Info Reimplemementing and testing Khosla et al.’s MemNet[1], and beyond. Goals The original MemNet has proved difficult to use and access in recent years. We would like to implement it so that we can use their work for our own research.

Last updated on Nov 2, 2021
Accelerated Gammatones

Last winter, I worked on a personal project I call ongaku (from the Japanese for ‘music’). This was an attempt to use manifold learning to create a metric space for music.

Last updated on Nov 2, 2021
Computational Rupahistory

This is an ongoing project to create 4X-style simulations of civilizations in a simulated world. This is intended to be a long-term project, that I will add to over the course of a long period, when I have some free time.

Last updated on Nov 2, 2021
Bandcamp Album Cover Analysis

This is a project to look into the question: How do indie musicians use visual signs to indicate their subgenre? The main functions are in bandcamp_webtools.py, and do basic web scraping.

Last updated on Nov 2, 2021
Ongaku

Overview Ongaku is a method for creating playlists programmatically, using only the content of the song alone. It uses gammatone cepstral analysis to create unique matrices to represent each song. A gammatone cepstrum is similar to the more common spectra used for audio analysis.

Last updated on Nov 2, 2021
Fluxx for Robots

Introduction: Fluxx is a mildly popular card game. All things considered, it’s actually fairly contentious. It has a score of 5.7 on Board Game Geek, but it has a serious cult following.

Recent Publications

Coen D. Needell, Wilma A. Bainbridge (2021). Embracing New Techniques in Deep Learning for Predicting Image Memorability. Computational Brain and Behavior..

arXiv URL

Popular Topics

Color Analysis Computational Game Theory Computer Vision Cultural Patterns Deep Learning Digital Archaeology GPU Computing Internet Culture Machine Learning Music Natural Language Recommendation Algorithms Scientific Tools SIBR Splorts Statistical Learning Urban Legends Video Games Visual Memory