Xinwei Fang

Papers

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About

Xinwei Fang is a leading researcher in the intersection of formal verification, evolutionary computation, and software engineering. Their most cited work, "Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies" (2021, 7 citations), introduces a groundbreaking approach to synthesizing optimal Markov decision process (MDP) policies that satisfy complex quality-of-service (QoS) requirements. This work uniquely combines evolutionary algorithms with formal verification to automatically generate verified, Pareto-optimal software configurations—a significant advance for designing reliable, high-performance systems. Fang’s contributions are pivotal in enabling engineers to navigate trade-offs between multiple, often conflicting, QoS objectives, such as performance, cost, and reliability. By translating MDP models into practical policies, their research bridges the gap between theoretical verification and real-world software design. With a growing citation footprint, Fang’s work is increasingly recognized for its potential to automate and guarantee the correctness of complex, adaptive systems. Their achievements highlight a rare ability to integrate rigorous formal methods with scalable search techniques, making them a key figure in the future of trustworthy, self-optimizing software.