Arya Irani is a Cofounder and AI scientist with a PhD from Georgia Tech and 14 years of experience building production-grade software and research systems. Based in Somerville, MA, he blends deep functional-programming expertise in Scala with practical systems design—contributing to prominent open-source projects like doobie, shapeless, scalaz, and http4s to improve query analysis, type-level abstractions, and HTTP handling. At Georgia Tech Research Institute he led teams applying type systems and functional design to government and health projects, co-developing a Scala-based Family System of Records and a vaccination scheduler used by over 200,000 users. His work spans from low-level algorithmic research in robotics and ML to shipping user-facing web tools, showing a rare ability to move from proofs-of-concept to deployed services. Colleagues know him for quietly restoring backward compatibility and reducing type loss in complex libraries—an attention to detail that keeps critical codebases stable as they evolve.
14 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Georgia Institute of Technology
BS, Computer Science, BS, Computer Science at University of Hawai‘i System
Contributions:20 releases, 1521 reviews, 2343 commits in 6 years 9 months
Contributions summary:Arya implemented new functionalities for listing dependents and dependencies in the Unison programming language. They added new commands to the parser and integrated them into the codebase, including the implementation of `dependents`, `dependencies`, and the `debug.file` command. These changes involved modifications to the codebase editor and output messages, focusing on displaying definitions and their relationships. The commits also included various bug fixes and refactoring efforts.
Contributions:7 commits, 6 PRs, 22 comments in 3 years 10 months
Contributions summary:Arya primarily focused on fixing bugs and improving the `shapeless` library, specifically related to HList operations. Their contributions included correcting issues in the `Union` and `Intersection` implementations, fixing type inference problems, and restoring original methods for backward compatibility. Furthermore, the user improved the code by reducing type loss using `.Aux` versions of inductive inputs. Their work mainly involved modifying core functionalities of the library.
functional-programminggenericscalalensestypelevel
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