Chris Rockwell

I am a second year Master's student at the University of Michigan in Computer Science and Engineering. I am currently conducting research in computer vision and machine learning with Prof. David Fouhey and previously worked with Prof. Jia Deng .

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 also did my Bachelor's at the University of Michigan, where I researched High Frequency Trading and AI under Prof. Michael Wellman.

Github  /  CV  /  LinkedIn

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Within computer vision, I'm particularly interested in using semi-supervised methods and video, understanding humans and scenes, and creating generative models, particularly in 3D. I'm also curious to explore multi-modal tasks and supervision, and in using structured prediction, especially for few-shot settings.

net1 Full-Body Awareness from Partial Observations
Graduate Research, Fouhey AI Lab. Summer 2019 - Fall 2019
Advisor: Prof. David Fouhey

We adapt human pose estimation models towards partial observation common in consumer Internet video using semi-supervised and self-supervised techniques. Our method yields significant improvement on HMR in truncated settings, and results generalize to external datasets.

net1 Hourglass Networks with Top-Down Modulation for Human Pose Estimation
Graduate Research, Princeton Vision and Learning Lab. Summer 2018 - Winter 2019.
Advisor: Alejandro Newell and Prof. 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: Alejandro Newell and Prof. 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 Market Fragmentation and the Latency Arms Race
Undergraduate Research, Strategic Reasoning Group. Summer 2013.
Advisor: Erik Brinkman, Elaine Wah and Prof. 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.

clean-usnob Evaluating Scene Graph-Generated Images using Visual Question Answering
Chris Rockwell.
Course project, EECS 692 Advanced AI, Winter 2019.
Instructor: Prof. 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: Prof. 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: Prof. Matthew Johnson-Roberson and Prof. Ram Vasudevan

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: Prof. 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.

um2 EECS 498/598 Deep Learning, Winter 2019 - Grader
Instructor: Prof. Honglak Lee
Guest blog post on (Michigan Economics) Professor Miles Kimball's blog Confessions of a Supply Side Liberal