Steven Basart

Computer Science PhD student


University of Chicago PhD in Computer Science (CS) Dec 2020

University of Miami B.S. in Biochemistry and CS May 2014


Intro. to Computer Science

Computational Biology

Machine Learning

Machine Learning and Large-Scale Data Analysis

Technical Skills

Languages: (Order of Proficiency) Python, C, C++, Java

Machine Learningi: Pytorch, Tensorflow, sklearn

Web Tools: HTML/CSS, NodeJS, Selenium

Other Tools: Git, SVN

Professional History

Autobon AI Head of AI Auguest 2019 to September 2020
Developed the AI/ML infrastructure at Autobon, which involves designing data ingestion into Amazon AWS, constructing labeling tasks, and quality assurance over the labeled data. aws

Google Brain Research Intern May 2018 to September 2018
Researched the area of Fact Checking related to this paper and developed solutions to deal with the problem of content abuse and contributed with the Google News team. python, pytorch, tensorflow, apache-beam, flume

Here Maps Research Intern May 2017 to September 2017
Engineered models to better estimate time of arrival (ETA), and also improved lane level navigation prediction. python, pytorch

Here Maps Research Intern May 2016 to September 2016
Developed a model that creates road probability maps that can be used to detect differences between artificial maps and the real roads. python, tensorflow

University of Miami Undergraduate Project May 2012 to September 2014
Created a genetic therapy via transducible gene editing proteins with Dr. Richard Myers. Involves running western blots, gel electrophoresis, transductions, PCR, and electroporation.


Aligning AI With Shared Human Values In Submission
Created a new benchmark to evaluate AI Safety by measuring how well models agree with human values.

Many Faces of Robustness: An Analysis of Out-of-Distribution Generalization In Submission
Collected a new dataset and introduced a new technique which achieves state of the art on out of distribution detection.

A Benchmark for Anomaly Segmentation In Submission
Constructed a synthetic dataset and utilize pre-existing datasets to evaluate different techniques for Anomaly Segmentation. We also showed how some classic approaches can improve performance in this task.

Natural Adversarial Examples ICML 2019 Workshop
Constructed a dataset which captures long tail distributions to highlight where current models fail in terms of generalization.

DIODE: A Dense Indoor and Outdoor DEpth Dataset 2019
Utilized a single depth sensor to capture both indoor and outdoor scenes to create the most accurate depth dataset to date.

Analysis of Generative Adversarial Models 2017
Introduce a novel measure for quantitatively assessing the quality of generative models and presented a method for utilizing GANs to interpret classifiers.