ellis.brown at nyu dot edu
I am an incoming CS PhD Student at NYU Courant advised by Saining Xie and Rob Fergus. I recently graduated from a Master’s at CMU where I was advised by Deepak Pathak and Alyosha Efros. Before that, I was a founding research engineer at BlackRock AI Labs, where I was fortunate to work with Mykel Kochenderfer, Stephen Boyd, and Trevor Hastie on applied research & finance. I earned Bachelors degrees in CS & Math from Vanderbilt University, where I worked on CoCoSci & Vision with Maithilee Kunda. I’m originally from St. Louis, MO.
If you haven’t made time for a regular checkin with a doctor recently, please do! Even if you feel perfectly healthy.
|May., 2023||Defended my Master’s thesis and graduated from CMU! Excited to start a CS PhD at NYU advised by Saining Xie and Rob Fergus this fall 🎉|
|Apr., 2023||Our paper Internet Explorer was accepted to ICML 2023!! See you in Hawaii 🌺🏝😎|
|Jan., 2022||Joined Deepak Pathak’s group in CMU’s Robotics Institute, and will focus on self-supervised learning and curiosity-driven exploration in computer vision.|
|Aug., 2021||Awarded the Intel Growing The Legacy Scholarship and accepted into the Lighting the Pathway to Faculty Careers program through AISES. Accepted into Google Research’s Computer Science Mentorship Program.|
|Jul., 2021||Left BlackRock AI Labs to pursue a Master’s degree in Computer Science at CMU. Bittersweet!|
selected research (more)
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.
- Your Diffusion Model is Secretly a Zero-Shot ClassifierarXiv:cs.LG, 2023
- Internet Explorer: Targeted Representation Learning on the Open WebIn International Conference on Machine Learning, 2023
- An Architecture for Spatiotemporal Template-Based SearchAdvances in Cognitive Systems, 2018
- Linearly Constrained Separable OptimizationIn JuliaCon 2021 JuMP Track, Jul 2021
- AISES-19Modeling Uncertainty in Bayesian Neural Networks with Dropout: The effect of weight prior and network architecture selectionIn American Indian Science and Engineering Society National Conference, Oct 2019🎖️ Third Place, Graduate Student Research Competition
- AISES-17Computational Cognitive Systems to Model Information SalienceIn American Indian Science and Engineering Society National Conference, Sep 2017