Hagar Meir is a Research Staff Member at IBM with nine years of engineering experience focused on blockchain and scalable distributed systems. She contributes to prominent open-source projects like Hyperledger Fabric, where her backend work improved performance and observability by optimizing private data caching and adding detailed gossip/consensus metrics. A Technion-trained computer engineer with both B.Sc. and M.Sc. degrees, she combines academic rigor from teaching computer architecture and software systems with hands-on industry research. Her background includes research internships at Yahoo and IBM and early experience in power and performance validation at Intel, giving her breadth across systems, performance, and distributed protocols. Hagar’s work emphasizes measurable improvements—reducing expensive configuration reads and instrumenting leader-election and messaging behavior—to make complex blockchain systems more reliable and transparent. Based in Haifa, she blends deep implementation skills with research-driven curiosity to tackle production-scale consensus challenges.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Science (B.Sc.), Computer Engineering, Bachelor of Science (B.Sc.), Computer Engineering at Technion - Israel Institute of Technology
Hyperledger Fabric is an enterprise-grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies a broad range of industry use cases. It offers a unique approach to consensus that enables performance at scale while preserving privacy.
Role in this project:
Back-end Developer
Contributions:7 reviews, 3 commits, 7 PRs in 1 day
Contributions summary:Hagar primarily contributed to the Hyperledger Fabric repository by caching the `pushAckTimeout` in pvt data, which optimized the performance by reducing the number of viper.GetDuration calls. They also added metrics for the gossip state, introducing reporting for events like height increment, block commits, and payload buffer size changes. In addition, they implemented metrics for gossip leader election and communication, adding counters for message overflows, sent messages, and received messages.
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