Software Engineer on Censys’s Attack Surface Management platform specializing in building systems and algorithms to attribute internet assets to customers using public and proprietary data sources.

• Maintained + debugged legacy attribution systems (python, kubernetes, rabbit, celery)

• Maintained + extended lightweight rule-based-system inference engine and encapsulating microservice to enable self-service customer onboarding (golang, postgres, kubernetes, helm, gitlab CI/CD)

• Designed, built, documented, and optimized low-latency REST APIs to support new user flow around self-service customer onboarding (golang, postgres, kubernetes)

• Conducted research to evaluate efficacy of potential new datasets and evaluate improvement once they were incorporated (google colab, python)

• Contributed to design and building of data ingestion pipeline for new public or proprietary datasets to support both production usage and research access (airflow, bash, python, kubernetes, Bigquery, terraform)

• Conducted technical and behavioral interviews for software and data engineering candidates

Collected an open database of information about the 90,000 autonomous systems that make up the internet to support internet measurement research

Queried >8 APIs and scraped databases/webpages in Python to train classifiers and perform entity matching

Paper published in IMC

https://asdb.stanford.edu/

Designed curriculum for 200 students in Stanford's Trust and Safety Engineering class

Programmed fundamentals of automated moderation Discord bot in Python as starter code for the final project using Perspective and Discord's APIs

TAed for the HackLab class, leading hands-on labs to teach security concepts

Teaching sections, grading and preflighting assignments/tests, and holding office hours for the CS106 classes at Stanford. Helped design and implement an online assessment format.