Geoffrey Angus
Resident
Computer Science
Google Research
I am a Resident in Google Research working on multimodal deep learning models for Image Search. I received a BS and MS in Computer Science from Stanford University, specializing in machine learning. I worked in both the Artificial Intelligence in Medical Imaging (AIMI) Center and HazyResearch lab, advised by Matthew Lungren and Chris Ré.
My most recent publication involves abnormality localization in full body PET-CT scans using weak supervision. We propose a method that allows for the automatic processing of thousands of natural language radiologist reports in the span of a few hours, for the purpose of training highly specialized abnormality classifiers. The full article can be found here.
I received my B.S. in Computer Science from Stanford University in 2018. I was class president, violinist, and teaching assistant in my spare time.
Computer Science
Google Research
I am a Resident in Google Research working on multimodal deep learning models for Image Search. I received a BS and MS in Computer Science from Stanford University, specializing in machine learning. I worked in both the Artificial Intelligence in Medical Imaging (AIMI) Center and HazyResearch lab, advised by Matthew Lungren and Chris Ré.
My most recent publication involves abnormality localization in full body PET-CT scans using weak supervision. We propose a method that allows for the automatic processing of thousands of natural language radiologist reports in the span of a few hours, for the purpose of training highly specialized abnormality classifiers. The full article can be found here.
I received my B.S. in Computer Science from Stanford University in 2018. I was class president, violinist, and teaching assistant in my spare time.
Projects & Papers
Cross-modal weak supervision enables abnormality localization in full-body PET-CT
Geoffrey Angus*, Sabri Eyuboglu*, Bhavik Patel, Anuj Pareek, Guido Davidzon, Jared Dunnmon, Matthew Lungren
We are among the first to build a high-performance machine learning model for abnormality localization in full body PET-CT scans. Our model detects lymph node, lung and liver abnormalities with median AUROCs of 87%, 85% and 92%, respectively. We propose a new method to generate labels from natural language radiologist reports to train weakly supervised image classifiers.Nature Communications, 2021
[Code] [Paper] [Slides]
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
Nimit S. Sohoni, Jared A. Dunnmon, Geoffrey Angus, Albert Gu, Christopher Ré
Developed a novel weak supervision approach for recent distributionally robust optimization algorithms, reducing worst-case generalization error by up to 74% compared to standard procedures, without fine-grained label annotations.NeurIPS, 2020
[Code] [Paper]
Via: Illuminating Undergraduate Academic Pathways at Scale
Geoffrey Angus*, Richard Diehl Martinez*, Mitchell Stevens, Andreas Paepcke
We synthesized 19 years of university enrollment data as a probabilistic graphical model to derive insights from the behavior of >52,000 students.Learning @ Scale ACM conference, 2019
[Code] [Paper]
Regulatory Activity Prediction with Attention-based Models
Geoffrey Angus*, Sabri Eyuboglu*
We propose a new neural network model that leverages attention mechanisms in order to predict transcriptional profiles from a given DNA sequence.CS273B: Deep Learning in Genomics and Bioinformatics.
[Code] [Paper] [Poster]
Abnormality Detection in Carotid Ultrasounds with Convolutional Networks
Geoffrey Angus*, Sabri Eyuboglu*, Pierce Freeman*, Bhavik Patel, Mu Zhou, Katie Shpanskaya, Kristen Yeom, Matthew Lungren
We attempt to use Computer Vision to find stenosis in the carotid artery. Spoiler: it's harder than it sounds.CS231N: Convolutional Neural Networks for Visual Recognition.
[Code] [Paper] [Poster]
Exercise Transcription using a Long-Term Recurrent Convolutional Network
Geoffrey Angus*, Sabri Eyuboglu*, Pierce Freeman*, Rooz Mahdavian*
We train a model on a dataset consisting of micro-electromagnetic sensor data streams recorded at the gym in order to build an exercise transcription application for wearables.CS230: Deep Learning.
[Code] [Paper] [Poster]
Wine Recommender System Using Multidimensional Clustering Methods
Geoffrey Angus*, Richard Diehl Martinez*, Rooz Mahdavian*
We train a recommender system on the sommelier reviews of over 150 thousand wines. Tell us a price point and we'll tell you what you want!CS229: Machine Learning.
[Code] [Paper] [Poster]
Teaching