Yifan Mai

Maintained the open source crfm-helm Python library for LLM and multimodal model evaluation, which has 2k stars on GitHub and has been used by Apple, Microsoft, Meta and IBM Built benchmarks and leaderboards for LLM and multimodal evaluation across aspects including safety, multi-linguality, and biomedical-domain tasks Co-authored publications in leading venues including NeurIPS, ICLR, ICML, JMLR and ACL Advised in the development of MLCommon's AILuminate benchmark

Maintained TensorFlow Extended (TFX), a platform for production machine learning pipelines, including data preprocessing, training, evaluation and batch inference Improved efficiency, performance and scalability of Cloud Dataflow and internal data processing systems — reduced the cost of running data processing pipelines with many parallel GroupByKeys by 50% Designed and implemented components and APIs for ingesting data from internal data stores into TensorFlow Extended (TFX) Promoted internal adoption of TensorFlow Extended (TFX) by running introductory courses and providing technical support to users Led migration of TensorFlow Extended (TFX) code base from Python 2 to Python 3

Reduced development environment setup time for new engineers from five days to half a day by migrating from VMs to cloud instances and automating image building using Packer. Designed and implemented a request-specific dynamic routing system for engineers to QA canary clusters of backend services in the production environment. Developed disaster recovery procedures to restore all online production systems to AWS cloud infrastructure from backup within 48 hours in the event of catastrophic system failure. Developed pipeline for integrating the frontend codebase with Smartling API platform for translation and localization of message strings using JavaScript parse tree traversal. Migrated the backend services codebase (>1 million lines of code) to Play 2.6 and Scala 2.12. Migrated service mesh to Envoy.

Designed and implemented a web service to deliver recurring email, SMS and robocall notifications to customers. Implemented Python APIs for automation of laboratory hardware.

Taught a section as part of Stanford's introductory CS course (CS 106A: Programming Methodology). Created, designed and taught a student-initiated JavaScript course (CS 98SI: Introduction to JavaScript).

Interned with the mobile advertising quality team. Designed and implemented distributed data pipelines to perform feature selection on training data.

One of three founding software engineers. Designed and implemented the first iteration of Coursera's learning platform on a one-quarter timeline in a team of four engineers, with individual contributions to video playback, course authoring tools, learner analytics dashboards, and web accessibility.

Conducted research in unsupervised feature learning, supervised by Andrew Ng. Co-authored the Stanford Deep Learning tutorial (deeplearning.stanford.edu) Investigated a novel variant of deep sparse filtering for image object recognition.