Steven Basart

Computer Science PhD


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

Professional History

The Center for AI Safety Research Engineer/Reliability Engineer May 2022 to present
Research Engineer responsibilities: Data collection and using huggingface for model fine-tuning and evaluation. Managed small teams on a adversarial examples project and other safety projects.
Reliability Engineer responsibilities: Set up technical procedures and infrastructure for the company such as github actions and formalizing code review processes. Contract negotiations with several cloud providers, and architecting the design for the cloud infrastructure.
Github, Kubernetes (K8s), python

SpaceX Software Engineer II Februrary 2021 to May 2022
Updated the kubernetes environment to support new satellites. Worked on StarLink mobility effort to allow for mobile User Terminals (UTs). Worked on low level RAM interface that was used to store critical information about satellite ephemeris.
Kubernetes (K8s), flatbuffers, bazel, C++

Autobon AI Head of AI August 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.

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 collaborated 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.


Scaling Out-of-Distribution Detection for Real-World Settings ICML 2022
Constructed a synthetic dataset and utilized pre-existing datasets to evaluate different techniques for Anomaly Segmentation. We also showed how some classic approaches can improve performance in this task.

Measuring Coding Challenge Competence With APPS NeurIPS 2021
Collected and created an evaluation benchmark for converting word problems and tasks into python code. The evaluation framework consisted of taking abitrary python code and running it to compare against ground truth solutions.

Measuring Mathematical Problem Solving With the MATH Dataset NeurIPS 2021
Creating functions that can generate arbitrary math problems to evaluate models for different grade levels of math competency.

Aligning AI With Shared Human Values ICLR 2021
Created a new benchmark to evaluate AI Safety by measuring how well models agree with human values.

Towards Robustness of Neural Networks Thesis 2021
Thesis consisting of the previous work in robustness, and trying to make few-shot models robust.

Many Faces of Robustness: An Analysis of Out-of-Distribution Generalization ICCV 2021
Collected a new dataset and introduced a new technique which achieves state of the art on out of distribution detection.

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.