James Verbus
I build AI systems for uncertain and adversarial environments: systems where measurement is hard, feedback is noisy, and getting the right answer requires more than an evaluation score.
After nearly a decade at LinkedIn, most recently as a Senior Staff Machine Learning Engineer focused on anti-abuse, trust, and platform integrity, I chose to leave to focus full-time on frontier AI systems: agents, simulation, evaluation, measurement, and research workflows.
Before LinkedIn, I earned my Ph.D. in physics at Brown working on LUX, one of the world's most sensitive dark-matter detectors. Across physics, platform integrity, and AI, the through-line has been the same: extract weak signals, measure what matters, understand uncertainty, and build systems that remain reliable when the ground truth is difficult to see.
Featured Writing
Open Source
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.
Ph.D. Thesis
Calibrating the LUX Dark Matter Experiment
An absolute calibration of sub-keV nuclear recoils in the LUX detector using neutron scattering kinematics, improving low-mass WIMP sensitivity sevenfold.
Projects
Open Source
Isolation Forest
A distributed Spark/Scala isolation forest library for unsupervised anomaly detection, built at LinkedIn and open sourced. Recent major updates added Extended Isolation Forest and ONNX support.
Research / Publications
2024
Finding AI-Generated Faces in the Wild
G.J. Aniano Porcile, J. Gindi, S. Mundra, J.R. Verbus, H. Farid
CVPR Workshop on Media Forensics
Paper ·
arXiv:2311.08577
2023
Exposing GAN-Generated Profile Photos from Compact Embeddings
S. Mundra, G.J. Aniano Porcile, S. Marvaniya, J.R. Verbus, H. Farid
CVPR Workshop on Media Forensics
Paper
2024
Deep Learning to Detect Abusive Sequences of User Activity in Online Network
J.R. Verbus, B. Wang
US Patent 11,936,682
Patent
Talks / Videos
2025
Brown AI Winter School 2025: Exploring LLMs and RAG
A 2.5 hour interactive workshop at the 2025 AI Winter School, hosted by the Center for the Fundamental Physics of the Universe at Brown University.
Venue/Host: AI Winter School 2025, Center for the Fundamental Physics of the Universe at Brown University.
2024
SXSW 2024 Panel with DARPA: Real or Not - Defending Authenticity in a Digital World
A panel discussion on authenticity, trust, and detecting synthetic media in an AI-native information ecosystem.
Venue/Host: SXSW and DARPA.
DARPA recap
2019
Fighting Abuse @Scale: Preventing Abuse Using Unsupervised Learning
Detection of abusive activity on a large social network is an adversarial challenge with quickly evolving behavior patterns and imperfect ground truth labels. These characteristics limit the use of supervised learning techniques, but they can be overcome using unsupervised methods. To address these challenges, we created a Scala/Spark implementation of the isolation forest unsupervised outlier detection algorithm.
Venue/Host: @Scale Conference.
github.com/linkedin/isolation-forest ·
Original @Scale page
Open to Conversations
I enjoy comparing notes with people building or studying AI systems. I'm especially interested in the intersection of physics and AI, from world models to reinforcement learning and simulation, and in measurement, adversarial problems, and security. If that overlaps with what you're working on, I'd be glad to hear from you.