research

I’m interested in self-supervised learning, representation learning, curiosity-based exploration, and leveraging internet-scale models and data. I am keen to draw inspiration from intelligence in humans and nature—especially as a goal-post rather than a blueprint. My long-term goal is to develop intelligent agents that can generalize and continually adapt as robustly and efficiently as humans do, allowing them to be safely deployed in the real world.

Publications

2023

  1. ⏳ CVPR-23 ⌛
    Internet Explorer: Representation Learning on the Open Web
    Alexander Li*Ellis Brown*Alexei A. Efros, and Deepak Pathak
    In Conference on Computer Vision and Pattern Recognition, 2023

2022

  1. Internet Curiosity: Directed Unsupervised Learning on Uncurated Internet Data
    Alexander Li*Ellis Brown*Alexei A. Efros, and Deepak Pathak
    In European Conference on Computer Vision Workshop on “Self Supervised Learning: What is Next?”, 2022

2018

  1. An Architecture for Spatiotemporal Template-Based Search
    Ellis Brown, Soobeen Park, Noel Wardord, Adriane Seiffert, and 3 more authors
    Advances in Cognitive Systems, 2018
  2. ACS-18
    SpatioTemporal Template-based Search: An Architecture for Spatiotemporal Template-Based Search
    Ellis Brown, Soobeen Park, Noel Warford, Adriane Seiffert, and 3 more authors
    In Proceedings of the 6th Conference on Advances in Cognitive Systems, Aug 2018



Talks

2021

  1. Linearly Constrained Separable Optimization
    Ellis Brown, Nicholas Moehle, and Mykel J. Kochenderfer
    In JuliaCon 2021 JuMP Track, Jul 2021

2019

  1. AISES-19
    Modeling Uncertainty in Bayesian Neural Networks with Dropout: The effect of weight prior and network architecture selection
    Ellis Brown*, Melanie Manko*, and Ethan Matlin*
    In American Indian Science and Engineering Society National Conference, Oct 2019
    🎖️ Third Place, Graduate Student Research Competition

2017

  1. AISES-17
    Computational Cognitive Systems to Model Information Salience
    Ellis Brown, Adriane Seiffert, Noel Warford, Soobeen Park, and 1 more author
    In American Indian Science and Engineering Society National Conference, Sep 2017



Reports

2022

  1. CMU 16-824
    Self-Supervised Representation Learning via Curiosity-Driven Exploration
    Alvin Shek, Ellis Brown, Nilay Pande, and David Noursi
    May 2022

2021

  1. CMU 16-811
    Scaling Interpretable Reinforcement Learning via Decision Trees to Minecraft
    Ellis Brown, and Aaron M. Roth
    Dec 2021

2020

  1. Stanford CS 361
    Securities Lending Policy Optimization
    Ellis Brown
    Jun 2020

2019

  1. Columbia CS E6699
    Modeling Uncertainty in Bayesian Neural Networks with Dropout
    Ellis Brown*, Melanie Manko*, and Ethan Matlin*
    May 2019