Laura E. Brandt, PhD

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About me

I am a systems engineer working at Blue Origin on their MK-2 crewed Lunar lander. In May 2024 I graduated with a PhD from the Robust Robotics Group at MIT, advised by Nick Roy and supported by a NSF Graduate Fellowship. I hold a doctoral minor in Space Systems Engineering, and a master's degree from Bill Freeman's computer vision group.

I am generally interested in human and robot vision research, and developing the autonomous navigation,robotic, and electrical systems that will help humans explore space. I like problem-oriented engineering design, and like to play with both hard- and soft- ware, with more recent emphasis being on software and AI/ML-based computer vision models. I enjoy teaching mathematics, physics, and electrical engineering in classrooms and 1-on-1, and nurturing the next generation of engineering researchers through internship.

In the past I made electronics for Venus probes, built telescopes, defined concepts and requirements for Lunar payloads, and designed sensors that helped keep rocket engines from exploding. I hold a PhD/SM EECS and doctoral minor (space sys-eng) from MIT, and a BS EECS/BA Physics from UC Berkeley.

Teaching

Electromagnetics (6.630), MIT
Grader. Professors: Qing Hu, Jelena Notaros
Fa 2020, Fa 2022, Fa 2023

Electromagnetic Waves and Applications (6.013), MIT
Teaching Assistant. Professors: Luca Daniel, Kevin O'Brien
Sp 2022

Quantum Measurement and Control (6.644), MIT
Grader. Professor: Kevin O'Brien
Fa 2021

Signals, Systems, and Inference (6.011), MIT
Grader. Professors: George Verghese, Peter Hagelstein
Sp 2021

L.R. Ingersoll Physics Museum, UW Madison
Museum Docent. Supervisor: Steve Narf
Su 2014 - Sp 2015

Intro to Electrical Engineering (ECE 210), UW Madison
Lab Assistant. Professor: Giri Venkataramanan
Fa 2013 - Sp 2015

Interns


Xinyun (Sunshine) Jiang (Fa 2023-present) with Sid Ancha -- Uncertainty estimation for diffusion models.
Louisa Wood (Su 2022-Sp 2023) with Sandra Liu — Better GelSight sensors using artificial neural networks.
Stephanie Fu (Fa 2021-Sp 2023) with Mark Hamilton — Feature upsampling. Work published at ICLR 2024.
Howard Beck (Su 2022) — Priming neural depth estimators with rough disparity predictions.


Project Highlights

Lunar Ascent Camera
Mission Systems and Integration Intern, Lunar Transportation (Blue Origin, Su 2023)
Mentors: Dr. Alex Miller and Tim Lloyd

Rocket Engine Health Monitoring System
DreamChaser Propulsion Intern (Sierra Space, Su 2017)
Mentors: Scott Munson and J. Arthur 'Chip' Sauer

Clock Timing Interface
South Pole Telescope collaboration (UC Berkeley, Su 2016)
Mentors: Nicholas Huang and Nathan Whitehorn

CubeSat Deployment System
InterEgr 160: Engineering Design (UW Madison, Sp 2013)
Mentors: Scott Munson and J. Arthur 'Chip' Sauer of Orbital Technologies (now part of Sierra Space)

Datasets

SurfaceGrid: (Almost) a million images for learning shape from surface contours: [LINK]
from Toward Automatic Interpretation of 3D Plots (2021)


Publications

2024
PhD Thesis: Human-Inspired Methods for Extending Advances in Computer Vision to Data- and Compute-Constrained Environments
Massachusetts Institute of Technology
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
with Stephanie Fu and Mark Hamilton (primaries), Axel Feldmann, Zhoutong Zhang, and Bill Freeman.
International Conference on Learning Representations (ICLR), 2024 [Poster]
Website. Preprint. Code. Demo. Colab. News feature
2023
Fusion for Reducing Domain Specificity in Computer Vision Models
with Nick Roy.
March Meeting of the American Physical Society, 2023 [Oral]
Abstract
2022
One Visualization is Worth 1000 Words: Toward Automated Data Recovery and Interpretation from Past 3D Visualizations
with Bill Freeman.
March Meeting of the American Physical Society, 2022 [Oral]
Abstract. Dataset
The Importance of Experimental Controls: Evidence that Task-Dependent Invariances Define Functional Specialization in Cortical Hierarchy
March Meeting of the American Physical Society, 2022 [Poster]
Abstract
2021
SM Thesis: Shape from Surface Contours via Artificial Neural Nets
Massachusetts Institute of Technology
Thesis. Dataset
Toward Automatic Interpretation of 3D Plots
with Bill Freeman.
International Conference on Document Analysis and Recognition (ICDAR), 2021 [Poster]
Preprint. Paper. Dataset
2019
Graphene as a Diffusion Barrier in High-Temperature Electronics
with Ananth Yalamarthy, Peter Satterthwaite, Sam Vaziri, Savannah Benbrook, Eric Pop, and Debbie Senesky.
March Meeting of the American Physical Society, 2019 [Oral]
Abstract
2018
Variability Study for Low-Voltage Micro-Electro-Mechanical Relay Operation
with Benjamin Osoba (primary), Bivas Saha, Sergio Almeida, Jatin Patil, Maurice Roots, Edgar Acosta, Junqiao Wu, and Tsu-Jae King Liu.
IEEE Transactions on Electron Devices
Paper

Copyright © 2024, Laura E. Brandt - lebrandt@mit.edu