Hi, I’m Nathan Samson

I find it really cool to build, but I also really care about privacy and safety.

This is the earliest project I can find of me coding when I was 8.

Click this if you want to learn more.

Here’s some of my experience.

Experience

Grassroots Analytics

Aug 2026 – present

Junior Artificial Intelligence Engineer

AI Engineer Intern · Jan – Aug 2026

  • Built a news pipeline that ranks relevant coverage and an article library for staff research and AI-drafted content.
  • Built chat for news search and message drafting. Now building tools to group donors and predict who is likely to give.

Capital One

Jun – Aug 2025

Software Engineer Intern

  • Diagnosed a timeout that silently broke production API monitoring and helped migrate the service from AWS Lambda to Fargate.
  • Added logs, alerts, incident reports, and a results dashboard so engineers could catch failures and investigate them.

OmniSyncAI

May – Jul 2024

Software Engineer Intern

  • Built company onboarding from scratch in React, Node.js, and PostgreSQL, including account setup, authentication, and team invitations.
  • Built an AI recommendation engine that suggests teammates and configurations based on each company’s signup needs.

DAMS Lab, UMBC

Sep 2023 – present

Undergraduate Researcher

  • Built a sensor pipeline and React dashboard for a campus system that estimates and forecasts room occupancy from laser scanner readings.
  • Developing SPACES course labs that teach students to connect sensors, exchange messages, and build data pipelines.

Research experience

Durable Guarantees

First author

  • An audit of whether sensitive attributes remain recoverable after a model claims to remove them.
  • Tests representations and model outputs with stronger attackers, then measures the accuracy cost of preventing leakage.

Submitted to IEEE S&P 2027

PCRL

First author

  • A representation learning method that produces a different view of the same data for each intended use.
  • Trains a shared encoder with privacy constraints for each purpose, with audits for leakage of sensitive attributes.

MQTT-DAP

Co-author

  • A framework for enforcing consent and permitted data uses within the MQTT protocol.
  • Supports access, correction, and deletion requests, with a benchmark for privacy correctness and performance.

Submitted to ACM SenSys 2027

PSMark

Co-author

  • A distributed benchmark for comparing publish/subscribe systems under realistic IoT workloads.
  • Models varied devices and behaviors from real datasets, enabling consistent comparisons across MQTT and DDS.

Published at IEEE PerCom 2026 · Artifact Certified