Heyo, nice to see you here. I've worked on end-to-end driving from visual input, trajectory prediction, predicting maps from sensor data, 3D/4D neural scene representation, learned map representations. Most recently, im working on scene generation with Flow/Diffusion models. As of 2026, I'm finishing up my Ph.D. at The University of Texas at Austin, advised by Philipp Krähenbühl.
Education:
- PhD Computer Science at UT Austin, 2019 -
- BS+MS Computer Science at UT Austin, 2013 - 2018
- BS Mathematics at UT Austin, 2013 - 2016
Previous Collaborators:
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| AI Resident @ Intel 2018 - 2019 |
Research Intern @ Wayve 2021 |
Research Intern @ Motional 2022 |
Research Intern @ NVIDIA 2022 - 2023 |
Research
Compressed Map Priors for 3D Perception
Brady Zhou, Philipp Krähenbühl
arXiv 2025
A framework for learning global spatially anchored features that help downstream perception.
Cross-view Transformers for Real-Time Map-view Semantic Segmentation
Brady Zhou, Philipp Krähenbühl
CVPR 2022, Oral
Enhance image positional embeddings with camera extrinsics for multi-view models.
Domain Adaptation through Task Distillation
Brady Zhou, Nimit Kalra, Philipp Krähenbühl
ECCV 2020
Sim2Sim driving by distilling a teacher that uses depth/segmentation as input.
Learning by Cheating
Dian Chen, Brady Zhou, Vladlen Koltun, Philipp Krähenbühl
CoRL 2019, Spotlight
A model trained robustly with map input provides good supervision for an image-only model.
Does computer vision matter for action?
Brady Zhou, Philipp Krähenbühl, Vladlen Koltun
Science Robotics 2019
Going through intermediate representations (depth, segmentation) helps agents act.
Don't let your Discriminator be fooled
Brady Zhou, Philipp Krähenbühl
ICLR 2019
Training GANs with adversarial examples smooths the loss landscape and improves generation.
GPU accelerated k-nearest neighbor kernel for sparse feature datasets
Brady Zhou, George Biros
UT MS Research
Accelerated KNN for sparse features by writing some fancy cuda kernels (bitonic sort).
Teaching
- Deep Learning: Online AI MS 2024 (1000+ students), Course Staff; grading infra.
- Deep Learning: Online AI MS 2025 (1000+ students), Course Staff.
- Deep Learning: Online AI MS 2023 (700 students), Course Staff; modernize course + materials.
- Deep Learning: Online AI MS 2022 (400 students), Course Staff.
- Neural Networks, Fall 2020 (200 students), Instructor; lectures.
- Neural Networks, Fall 2019 (100 students), Teaching Assistant.
- Introduction to Programming, Fall 2015 (100 students), Teaching Assistant.
Fun
Outside of work you will find me
- Hanging out with my cats Pig and Teddy
- At the bouldering gym
- Tuning 3D printers
- Building and flying FPV drones
- Tweaking my tmux, VSCode, Vim configs + plugins
- Playing with / coding up tools for LLMs
