Richard E.L. Higgins

I am a Computer Science and Engineering PhD student at the University of Michigan, advised by David Fouhey. I was previously a Master's student here in CSE.

At Michigan, I'm currently working on forecasting and future prediction. I did my bachelors at the University of Maryland, where I studied Neuroscience and Computer Science.

Email  /  Resume  /  Google Scholar  /  Github  /  Devpost  /  Kaggle  /  LinkedIn

profile photo

I'm interested in computer vision, machine learning, neural networks, and weakly-supervised learning. I want to develop learning systems that scaffold or "couple" components such that they can cyclically improve from each other's improvements.

Fast and Accurate Emulation of the SDO/HMI Stokes Inversion with Uncertainty Quantification
Richard E.L. Higgins, David F. Fouhey, Dichang Zhang, Spiro K. Antiochos, Graham Barnes, Todd Hoeksema, KD Leka, Yang Liu, Peter W. Schuck, Tamas I. Gombosi
Astrophysical Journal, Accepted 2021
AGU, ML in Space Weather, Poster 2020
COSPAR2021, Workshop on ML for Space Sciences, Talk 2021

Graduate Research
Department of Electrical Engineering and Computer Science, University of Michigan
Ann Arbor, Summer 2019 - November 2020

‣ 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
Network Reconstruction Reveals that Valproic Acid Activates Neurogenic Transcriptional Programs in Adult Brain Following Traumatic Injury
Gerald A. Higgins, Patrick Georgoff, Vahagn Nikolian Ari Allyn-Feuer, Brian Pauls, Richard Higgins, Brian D. Athey, and Hasan E. Alam
Pharmaceutical Research, August 2017 bibtex

Postgraduate Research
Department of Computational Medicine and Bioinformatics, University of Michigan
Ann Arbor, August 2016 - October 2016

‣ I constructed topologically associated domains and analyzed RNA-seq data to identify differential gene expression using bioinformatics libraries in R and Python.

Matrix Metalloproteinase-9 Regulates Neuronal Circuit Development and Excitability
Sachiko Murase, Crystal Lantz, Eunyoung Kim, Nitin Gupta, Richard Higgins, Mark Stopfer, Dax A. Hoffman, and Elizabeth M. Quinlan
Journal of Molecular Neurobiology, July 2016 bibtex

Undergraduate Research
Neuroscience and Cognitive Science Program, University of Maryland
College Park, January 2014 - June 2014

‣ I wrote MATLAB functions that classified windows of mouse EEG recordings as seizure/not seizure with max-margin unsupervised learning.

Visualizing What Cichlids with Different Cone Opsins See

Undergraduate Research
Department of Biology, University of Maryland
College Park, September 2011 - April 2012

‣ 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.

Off-Policy RL that Prioritizes Value Function Improvement for Speedup
Ethan Brooks, Idris Hanafi, Jake DeGasperis, and Richard Higgins

Parallel Computer Architecture Project
Computer Science Department, University of Michigan
Ann Arbor, Winter 2019

‣ 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.

Augmenting Physical Safety by Modifying Vehicular Networking Communication Methods
Eric Newberry, Hsun-Wei Cho, and Richard Higgins

Advanced Networking Project
Computer Science Department, University of Michigan
Ann Arbor, Winter 2019

‣ 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.

Journal of Useless Information

Joke Project
Winter 2019

‣ I made a joke website for the statistically significant but insignificant results of the world.

site / github
GTA Perception

Self-Driving Cars Project
Computer Science Department, University of Michigan
Ann Arbor, Fall 2018

‣ We finetuned a ResNet to classify objects appearing in road-scene images. Finished in the top 10 for the class.

Applying CNN Architectures to Sentence Generation
Harmanpreet Kaur, Heeryung Choi, Aahlad Chandrabhatta, and Richard Higgins

Natural Language Processing Project
Computer Science Department, University of Michigan
Ann Arbor, Fall 2017

‣ We trained a DenseNet language model. We explored how residual connectivity can compare favorably to RNNs.

Keras for X-Ray Radiology
Alexander Zaitzeff and Richard Higgins

Advanced Computer Vision Project
Computer Science Department, University of Michigan
Ann Arbor, Fall 2017

‣ We built a (briefly) state-of-the-art multilabel x-ray disease classifer.

presentation / github
Nerve Sense

Summer Project
San Leandro, June 2016 - August 2016

‣ I built a stacked hourglass model with residual connections for nerve segmentation.
‣ I released a keras residual unit for use as a configurable building block on github.

project github / residual unit github
Shariq Hashme, Matt Favero, and Richard Higgins

Summer Project
July 2015 - August 2015

‣ We created a service for deep dreaming your Facebook profile picture with convolutional neural networks.

God Game
Shariq Hashme, Matt Favero, and Richard Higgins

Hackathon Project
Bitcamp, April 2015

‣ We created a Unity and Microsoft Kinect game in virtual reality using superpowers triggered by gesture and gaze.

devpost / github
Fuad Balashov and Richard Higgins

Android Project
March 2015

‣ We created a mobile app for sharing photos and videos by location with a map interface. Like Snapmap.


Winter Project
January 2015

‣ 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.


Hackathon Project
MHacks, Ann Arbor, January 2015

‣ I made an Android app that matchmakes people and lets two players race from one wikipedia page to another by only clicking links.

devpost / video
National Data Science Bowl

Winter Project
College Park, January 2015

‣ I trained a convolutional neural network in Caffe to do plankton image classification.


Summer Project
Fremont, May 2014

‣ I made a neural network in PyBrain and later PyTorch for stock forecasting using convolutional neural networks and policy gradients.

Air Quality / UV / Pollen App
Ted Smith, David Ho, and Richard Higgins

Human Computer Interaction
Computer Science Department, University of Maryland
College Park, April 2013

‣ We made an Android app for Air Quality, UV, and Pollen Count tracking.

video / video demo / github
Dan Gillespie, Jeff Hilnbrand, Brent Bovenzi, and Richard Higgins

Hackathon Project
HackMIT, 2013

‣ We made a robot which was steered by an Android phone and sent video to a Google Glass.

devpost / video / github
Alejandro Newell, Jonathan Lockwood, Wajahat Siddiqui, and Richard Higgins

Hackathon Project
MHacks, Ann Arbor, 2013

‣ We made an Android app for sending payment reminders to "friends".

devpost / video / github
Dan Gillespie, Jeff Hilnbrand, Evan, and Richard Higgins

Hackathon Project
PennApps, 2013

‣ We made an Android location-based messaging app.

devpost / video
Arcade Glass
Dan Gillespie and Richard Higgins

Summer Project
Startup Shell
College Park, June 2013 - August 2013

‣ We made Android arcade games for Google Glass, controlled through head motions.

Bio-Inspired Robotics
Alejandro Newell, Matthew Smith, and Richard Higgins

Mechanical Engineering Course
University of Maryland
College Park, January 2013

‣ We modeled a horse robot in CAD, tested gaits in a simulator, 3D printed it, and surpassed distance requirements in the course.

University of Michigan

Graduate Student Instructor
Computer Science Department, University of Michigan
Ann Arbor, December 2018 - May 2019

‣ I led discussions, created assignments with Numpy and Pytorch in Python, graded projects, and hosted office hours for ~150 upper-level CS students.
Voxel 51

Computer Vision Engineering Intern
Ann Arbor, November 2018 - March 2019

‣ I incorporated and trained various object detection neural networks as part of a video analysis platform to identify objects in dashcam footage.

Software Engineering Consultant
San Francisco, August 2016 - July 2018

‣ 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.

New York, August 2015 - May 2016

‣ We built an automated document extraction service on AWS, with custom LSTMs for OCR.

First Engineer
San Francisco, March 2015 - August 2015

‣ I developed machine learning tools to automatically scale Kubernetes pods based on networks requests, CPU, and memory usage.
Mammalian Physiology

Teaching Assistant
Biology Department, University of Maryland
College Park, January 2014 - June 2014

‣ I instructed multi-hour discussions on cardiac function, renal system, nervous system, pharmacology, digestion, and more.
Cooperative Housing University of Maryland

Housing Chair, Finance Manager
College Park, August 2011 - June 2013

‣ I was the primary contact with landlords, handled house finances, and organized housing for the next school year.
Woods Hole Yacht Club

Sailing Instructor
Woods Hole, June - August, 2007 - 2009

‣ I taught children how to sail and not crash into expensive boats.

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