Erik Brinkman is a Responsible AI Researcher in New York with 11 years of experience bridging research and production at major tech labs, including a long research scientist tenure at Facebook and a current role at LinkedIn. He holds a Ph.D. in Computer Science from the University of Michigan and has a track record of tackling hard systems and algorithmic problems from target tracking and nonlinear simulation to responsible ML. Erik contributes to open source—adding DAG layout and quality improvements to d3-dag and strengthening type safety in the widely used ajv JSON-schema validator—showing fluency across full-stack, back-end, and type-system work. Colleagues rely on him for rigorous experimentation and practical systems thinking that translate research into robust, auditable tooling.
11 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at University of Michigan
BS, Systems Science, BS, Systems Science at Washington University in St. Louis
Layout algorithms for visualizing directed acyclic graphs
Role in this project:
Full-stack Developer
Contributions:3 reviews, 330 commits, 27 PRs in 4 years 5 months
Contributions summary:Erik contributed to implementing layout algorithms for directed acyclic graphs. They added the dagre layout feature and made test changes related to dag hierarchy and topological sorting. Furthermore, the user changed the rollup to extend d3, added size and curve capabilities to the layout implementation, and implemented distance-minimization and cycle removal to improve layout quality, indicating work across the project's core functionality.
The fastest JSON schema Validator. Supports JSON Schema draft-04/06/07/2019-09/2020-12 and JSON Type Definition (RFC8927)
Role in this project:
Back-end Developer
Contributions:21 reviews, 34 commits, 26 PRs in 1 year 10 months
Contributions summary:Erik primarily contributed to the development of JTD Schema type definitions and associated type checking tests. They introduced and refined the `JTDSchemaType` and `JTDDataType` types, adding tests to verify correct type inference for various schema components, including numbers, strings, enums, and records. Their work also involved fixing bugs related to nullable types and ensuring proper handling of records, demonstrating a strong understanding of the schema validation logic and type system. These contributions improved the type safety and overall robustness of the library.
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Erik Brinkman - Responsible AI Researcher at LinkedIn