Hey. Welcome to the website.
In the past I've worked on end-to-end driving from visual input, trajectory prediction, predicting maps from sensor data, 3D/4D neural scene representation, learning priors for autonomous vehicles, and RL-ing diffusion models for scene generation.
I completed my Ph.D. in 2026 at The University of Texas at Austin, advised by Philipp Krähenbühl.
Location: bouncing between Austin and New York City.
Research
Compressed Map Priors for 3D Perception
Brady Zhou, Philipp Krähenbühl
arXiv 2025
Learnable priors from past traversals.
Cross-view Transformers for Real-Time Map-view Semantic Segmentation
Brady Zhou, Philipp Krähenbühl
CVPR 2022, Oral
Learn map-view embeddings via cross-attention over image features, camera pose, and intrinsics.
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
End-to-end driving by distilling a privileged map-based policy into a vision-only one.
Does computer vision matter for action?
Brady Zhou, Philipp Krähenbühl, Vladlen Koltun
Science Robotics 2019
Predicting depth and segmentation as intermediate steps improves end-to-end agent performance.
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 2026 (1000+ students), Course Staff.
- Deep Learning: Online AI MS 2025 (1000+ students), Course Staff.
- Deep Learning: Online AI MS 2024 (1000+ students), Course Staff; grading infra.
- 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
I also spend time by..
- Hanging out with my cats Pig and Teddy
- Climbing (bouldering)
- Tuning 3D printers
- Building FPV quads
- Tweaking tmux, VSCode, Vim, and Claude Code configs
