Chris Rockwell

I am a Ph.D. candidate in Computer Science and Engineering at the University of Michigan, advised by David Fouhey and Justin Johnson. I also obtained my Master's at the University of Michigan, where I was advised by David Fouhey and Jia Deng. I'm broadly interested in computer vision and machine learning.

Before grad school, I worked as a trader at Citadel LLC, where I applied statistical arbitrage in fixed income. Earlier, I structured exotic options and created systematic hedging strategies at BNP Paribas. I received my Bachelor's at the University of Michigan, where I researched High Frequency Trading and AI under Michael Wellman.

Feel free to reach out if you are interested in doing research together or for any other reason! My email is cnris at umich dot edu

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Publications
net1 PixelSynth: Generating a 3D-Consistent Experience from a Single Image
Chris Rockwell, David F. Fouhey and Justin Johnson
ICCV 2021
project page / PDF / code / bibtex / press

PixelSynth fuses the complementary strengths of 3D reasoning and autoregressive modeling to create an immersive experience from a single image.

net1 Full-Body Awareness from Partial Observations
Chris Rockwell and David F. Fouhey
ECCV 2020
project page / PDF / code / bibtex / press

Our simple self-training framework adapts human mesh recovery systems to highly-truncated consumer videos.

Projects
net1 Hourglass Networks with Top-Down Modulation for Human Pose Estimation
Graduate Research, Princeton Vision and Learning Lab. Summer 2018 - Winter 2019.
Advisor: Jia Deng
code: attention / pretrained model, 2HG attention
code: regularization / pretrained model, 8HG regularization

We increase performance of Newell et al.'s Stacked Hourglass Networks on MPII using a decoder network as attention, along with cutout and vertical flipping. In addition, we improved precision of network confidence and explored utilizing confidence for curriculum sampling of tail cases.

meta A Simple Baseline on Meta-Dataset
Graduate Research, Princeton Vision and Learning Lab. Spring 2019.
Advisor: Jia Deng

We improve a simple fine-tuning baseline on Meta-Dataset to within 0.1 average rank (minimum reportable difference) of the authors' best meta-learning based method using higher regularization on fine-tuning layer compared to the backbone.

clean-usnob Evaluating Scene Graph-Generated Images using Visual Question Answering
Chris Rockwell.
Course project, EECS 692 Advanced AI, Winter 2019.
Instructor: John Laird.
presentation / report

I replicate and summarize Image Generation from Scene Graphs , and evaluate replicated generated images, original generated images, and ground truths using VQA.

clean-usnob Replicating and Improving Stacked Hourglass Networks for Human Pose Estimation
Chris Rockwell, Uzziel Cortez, Eric Huang.
Course project, EECS 545 Machine Learning, Fall 2018.
Instructor: Clayton Scott.
report / code / pretrained model, 8HG

We replicate and improve upon validation accuracy from Stacked Hourglass Networks for Human Pose Estimation using Adam and larger batch size. I led implementation of the project and it was jointly useful for research. My Pytorch implementation is published in the princeton-vl Github.

clean-usnob GTA Perception
Richard Higgins, Parth Chopra, Chris Rockwell, Sahib Dhanjal, Ung Hee Lee.
Course project, EECS 598/ROB 535 Self-Driving Cars, Fall 2018.
Instructors: Matthew Johnson-Roberson and Ram Vasudevan
code

We finetune a Squeeze and Excitation ResNet classify objects appearing in road-scene images in the Driving in the Matrix dataset. With improved sampling and data augmentation, we finished top 10 in the class. I helped with implementation and improved data augmentation.

clean-usnob Link Prediction on the Patent Citation Network
Samuel Chen, David Dang, Robert Macy, Chris Rockwell.
Course project, EECS 598 Advanced Data Mining, Winter 2019.
Instructor: Danai Koutra.
poster / report / code

We compare several methods for link prediction on partitions of the Patent Citation Network dataset for the first time. The very sparse nature of this graph yielded low performance compared to a more typical dataset, but methods performed well in a temporal setting. I led temporal experiments and adapted representation learning method SDNE for our task.

clean-usnob Market Fragmentation and the Latency Arms Race
Undergraduate Research, Strategic Reasoning Group. Summer 2013.
Advisor: Michael Wellman
poster / presentation
I was featured in a UMSI Youtube video.

I assisted Erik Brinkman in expanding the agent-based latency arbitrage simulation of Wah and Wellman to model a prisoner's dilemma in the high-frequency trading space.

Teaching & Activities
um2 AI4ALL, Summer 2020: Project Instructor
Summer 2021: Application Reviewer, Curriculum Advisory Board
Michigan AI4ALL Lead: David Fouhey
um2 African Undergraduate Research Adventure (AURA), Summer 2020: Research Mentor
Research Advisor: David Fouhey
um2 EECS 498/598 Deep Learning, Winter 2019: Grader
Instructor: Honglak Lee
Guest blog post on Miles Kimball's Economics blog Confessions of a Supply Side Liberal
post

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