Mayya Sharipova is a Principal Software Engineer with 12 years of experience focused on search relevance, vector search, and lexical search systems. Currently leading work on Elastic’s Search Relevance team, she has advanced vector functionality and scoring in the high-profile open-source Elasticsearch project, including vector norms and script_score fixes. Her background spans deep search and distributed database work at IBM Cloudant, where she contributed to CouchDB, Lucene-based full-text search, and a Spark connector. She holds a PhD in Artificial Intelligence in Education and an MSc in Information Retrieval, combining strong research credentials with production-grade engineering. Known for bridging academic rigor and pragmatic system design, she also brings hands-on ops and mentoring experience from academic and industry roles. Based in Toronto, she blends contributions to one of the most widely used search engines with a long-standing interest in intelligent systems and developer tooling.
12 years of coding experience
3 years of employment as a software developer
Master's degree, Computer Science, Information Retrieval, Master's degree, Computer Science, Information Retrieval at 立命館大学 / Ritsumeikan University
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Tashkent State Technical University
Doctor of Philosophy (Ph.D.), Artificial Intelligence in Education, Intelligent Tutoring Systems, Doctor of Philosophy (Ph.D.), Artificial Intelligence in Education, Intelligent Tutoring Systems at University of Saskatchewan
Free and Open Source, Distributed, RESTful Search Engine
Role in this project:
Back-end Developer
Contributions:904 reviews, 370 commits, 631 PRs in 4 years 10 months
Contributions summary:Mayya's contributions primarily involve improving documentation and implementing new features within the Elasticsearch codebase. Their work includes improvements to the smart_cn analyzer, moving dense_vector and sparse_vector implementations to a module, adding the dims parameter to the dense_vector mapping, and fixing bugs in the script_score query. They also worked on adding L1 and L2 norms and associated functions for vector calculations within the scoring scripts.
Contributions:99 pushes, 52 branches in 3 years 10 months
solrapachelucenejavaapache-lucene
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