AI systems under uncertainty

James Verbus

I build AI systems for uncertain or adversarial environments: abuse detection, behavior modeling, agents, anomaly detection, AI-generated media, and production-scale ML.

Previously, I spent nearly a decade at LinkedIn, most recently as a Senior Staff Machine Learning Engineer, building large-scale systems for anti-abuse, trust, and platform integrity.

My background began in rare-event physics: I earned my Ph.D. at Brown on LUX, a dark matter experiment searching for faint signals deep underground. That path still shapes my work on noisy evidence, uncertainty, robustness, and real-world constraints.

James Verbus working on the LUX dark matter detector — Working on detector hardware for the LUX, one of the world's most sensitive dark matter experiments.

Featured Work

Projects

Extended Isolation Forest for Distributed Spark/Scala Anomaly Detection

Extended Isolation Forest support for linkedin/isolation-forest, with random hyperplane splits, validation plots, benchmarks, reference parity checks, and edge-case tests.

Research

Finding AI-Generated Faces in the Wild

Research and engineering work on detecting AI-generated profile images in real-world settings, including CVPR workshop publication and LinkedIn Engineering write-up.

Workshop

Reinforcement Learning for Orbital Transfers

A hands-on Brown AI Winter School workshop connecting orbital mechanics, reinforcement learning, PPO agents, and practical model diagnostics.

Featured Writing

Mar 18, 2026

Extended Isolation Forest for Distributed Spark/Scala Anomaly Detection

Extended Isolation Forest support for LinkedIn's open-source Spark/Scala isolation-forest library, including random hyperplane splits, benchmarks, synthetic plots, and validation evidence. The work also became a useful case study in how to validate AI-assisted production code with evidence rather than trust.

Jan 9, 2026

Reinforcement Learning for Orbital Transfers at the 2026 AI Winter School (Brown University)

Hands-on workshop using reinforcement learning (PPO) to solve orbital transfer problems. Environment design, reward shaping, and training RL agents for space mechanics.

Feb 10, 2025

Exploring LLMs and RAG at the 2025 AI Winter School (Brown University)

Hands-on workshop on Large Language Models and Retrieval-Augmented Generation (RAG) for physics researchers. Using OpenAI API and LLaMA for domain-specific applications.

View all writing

Projects

Open-source and software work centered on `linkedin/isolation-forest`, a distributed Spark/Scala implementation for large-scale unsupervised anomaly detection.

Mar 18, 2026