My current research aims to use motion and human-object interaction to train systems that understand action in video. I've previously worked on predicting the solar magnetic field using spectra recorded by sun-observing satellites. I'm generally interested in network design and wiring, population-based training, learning from interactions, and leveraging video for offline reinforcement learning.
‣ We built a system that predicts segmentation masks for objects held by hands. The system trains from person, object, and background pseudolabels made by subtracting detected people from optical flow.paper
‣ Hinode's SOT-SP measures small areas of the sun in high spatial and spectral resolution to predict the magnetic field. SDO/HMI measures the full-disk in lower resolution, both spatial and spectral.
‣ By training a neural network to accurately predict Hinode's estimated field using only HMI's input, we created a virtual observatory that melds the best parts of both instruments.
‣ I trained a UNet to predict magnetic field parameters on the sun using polarized light (IQUV's) recorded from the Solar Dynamics Observatory's HMI sensor.paper / site / github / talk / poster
‣ I constructed topologically associated domains and analyzed RNA-seq data to identify differential gene expression using bioinformatics libraries in R and Python.
‣ I wrote MATLAB functions that classified windows of mouse EEG recordings as seizure/not seizure with max-margin unsupervised learning.
‣ I wrote a Java application that changed underwater images into false-color analogs for different cone opsins, to understand fish conspicuity.
‣ The idea was that these bright fish all have very different color cones and might actually be disguised in the eyes of predators.
‣ By allocating more of fixed resources to value function improvement, we were able to train a reinforcement learning model to converge more quickly than a baseline.
‣ The idea is that policy and value networks might need very different batch sizes/GPU usage for stability in different environments and that this can be learnt.
‣ We evaluated the frequency of vehicle safety messages to identify what adjustments could augment vehicle safety and reduce potential network congestion for self-driving cars.github
‣ We finetuned a Squeeze and Excitation ResNet to classify objects appearing in road-scene images. Finished in the top 10 for the class.github
‣ We trained a DenseNet language model. We explored how residual connectivity can compare favorably to RNNs.github
‣ We created a service for deep dreaming your Facebook profile picture with convolutional neural networks.github
‣ We created a mobile app for sharing photos and videos by location with a map interface. Like Snapmap.
‣ I created a virtual reality browser for exploring the internet as if it were a 3D city with websites as buildings.
‣ The idea here is that similar and inter-linked websites should be located nearby one another.
‣ I trained a convolutional neural network in Caffe to do plankton image classification.github
‣ I made a neural network in PyBrain and later PyTorch for stock forecasting using convolutional neural networks and policy gradients.github
‣ We made Android arcade games for Google Glass, controlled through head motions.
‣ We modeled a horse robot in CAD, tested gaits in a simulator, 3D printed it, and surpassed distance requirements in the course.video
‣ I led discussions, created assignments with Numpy and Pytorch in Python, graded projects, and hosted office hours for ~150 upper-level CS students.web.eecs.umich.edu/~fouhey/teaching/EECS442_W19/
‣ I incorporated and trained various object detection neural networks as part of a video analysis platform to identify objects in dashcam footage.voxel51.com
‣ I built a style-transfer service on AWS that used to process millions of images/day.
‣ I built a GAN that performs face attribute transformation for a social media company.
‣ I built a CNN backend to provide object recognition in a Fortune 500 company iOS app.
‣ I designed many CNN computer vision systems for Fortune 500 clients across industries.
‣ We built an automated document extraction service on AWS, with custom LSTMs for OCR.minimill.co/unscan
‣ I developed machine learning tools to automatically scale Kubernetes pods based on networks requests, CPU, and memory usage.ycombinator.com/companies/redspread
‣ I instructed multi-hour discussions on cardiac function, renal system, nervous system, pharmacology, digestion, and more.science.umd.edu/classroom/bsci440
‣ I was the primary contact with landlords, handled house finances, and organized housing for the next school year.chum.coop
‣ I taught children how to sail and not crash into expensive boats.woodsholeyachtclub.org