Kurtis Evan David

I am a recent MS graduate in Computer Science at UT Austin, where I worked on interpretability and bias of deep learning. I had the pleasure of being advised by Professor Qiang Liu and Dr. Ruth Fong. I also completed my undergraduate studies at UT, majoring in Computer Science and Mathematics, and acted as a competition organizer for UT Machine Learning and Data Science.

I completed 5 great internships at HRL Laboratories, Instagram, Facebook, ExxonMobil, and Cancer Prevention & Research Institute of Texas. I am excited to return to Facebook as a Software Engineer!

Email  /  CV  /  GitHub  /  LinkedIn  /  Google Scholar

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Broadly, I'm interested in deep learning, computer vision, and NLP. In school I worked on methods for interpretability and understanding bias of convolutional neural networks. I am also interested in adversarial robustness and model pruning.

Debiasing Convolutional Neural Networks via Meta Orthogonalization
Kurtis Evan David, Qiang Liu, Ruth Fong
NeuRIPS, 2020 Workshop on Algorithmic Fairness through the Lens of Causality and Interpretability (AFCI)
arXiv / poster / code

We show that we can recreate early work in debiasing word embeddings within convolutional neural networks using an extra meta step. We evaluate on a multitude of conditions and show it to be competitive to adversarial debiasing methods.

GANchors: Realistic Image Perturbations for Anchors Using Generative Models
Kurtis Evan David, Harrison Keane, Jun Min Noh
arXiv, 2019
arXiv / code / slides

To increase trust from model agnostic anchors, we instead conditionally sample backgrounds generated from a generative adversarial network, rather than random images from the dataset. We address additional complexity of our method through a diverse encoder, and show that our explanations can lead to smaller and higher precision anchors.

Work Experience
HRL Laboratories — Research Intern
Summer 2020
Testing AI Team — Advised by Dr. Michael Warren

Primarily worked on adversarial robustness of neural networks and understanding Adversarial Examples Are Not Bugs, They Are Features. Investigated promising defenses using neural network pruning and Fourier space analysis.

Instagram — Software Engineering Intern
Summer 2019
Instagram Sharing ML Team — Advised by Christina Wadsworth

Developed new Instagram Stories ranking models, balancing Direct and Stories metrics. Deployed IG share sheet rankings with significant gains, and tested Direct caching. Lastly, implemented Lottery Ticket Hypothesis on production ranking models.

Facebook — Software Engineering Intern
Summer 2018
Monetization Ranking Team — Advised by Dr. Qinqin Zhu

Incorporated new user side NLP features into the feed ads ranking model. Explored possible connections between user and ads side features to increase metrics. Implemented a new pooling layer into their models, pushed to open source.

ExxonMobil — Data Science Intern
Summer 2017
Internal Audit Data Science Team — Advised by Scott Nelson

Built an anomaly detection model for internal audits using PCA and Isolation Forest. Running an automated proof-of-concept supervised learning model for future continuous audits that achieved an F1-score of .93 with .08% anomalies (100 of 120K). Additionally supported a document analysis tool that found similarities between two specification documents using a bag-of-words implementation.

Cancer Prevention & Research Institute of Texas — Summer Research Fellow
Summer 2016
UT School of Biomedical Informatics — Advised by Dr. Trevor Cohen

Analyzed the relationship between cancer drug sensitivity data and their reported side effects, scraped from the FDA’s Adverse Event Reporting System database. Developed an organ level side effect classifier for novel cancer drugs.

Spring 2020

Fall 2019

Spring 2019

Spring 2018
EE460J Data Science Laboratory

EE461P Data Science Principles

EE461P Data Science Principles

CS429H Computer Organization and Architecture
Teaching Assistant

Teaching Assistant

Undergraduate Teaching Assistant

Undergraduate Teaching Assistant

Legendary website template from Jon Barron.