I am a PhD candidate in Computer Engineering (Software), conducting research at the intersection of distributed systems, blockchain consensus, and applied machine learning.
My doctoral thesis, “Design of an Adaptive, Distributed Consensus Protocol with Intelligent Byzantine Behavior Detection Capabilities for Blockchain Networks,” focuses on tackling one of the core challenges in decentralized systems: achieving scalable, secure, and efficient consensus in adversarial environments. I am designing and prototyping ABRC (Adaptive Byzantine-Resistance Consensus), a novel consensus protocol that integrates Graph Neural Networks (GNNs) for intelligent, fine-grained detection of Byzantine behaviors at both the node and block levels, combined with Reinforcement Learning (RL) for dynamic, self-adapting protocol parameter optimization.

This research directly engages with the fundamental trade-offs in blockchain design—security, decentralization, scalability, and energy efficiency—and proposes a next-generation, learning-augmented consensus architecture. My work involves extensive simulation (using environments like OMNeT++/INET), formal protocol analysis, and performance benchmarking against established protocols like PBFT, PoW, and PoS.
I am deeply interested in Ethereum’s ecosystem, its ongoing evolution regarding consensus mechanisms, scalability solutions (layer-2, sharding), and the practical application of cryptographic and AI techniques to enhance protocol robustness and efficiency. I believe my expertise in Byzantine Fault Tolerance (BFT), adaptive algorithms, and distributed machine learning can contribute to Ethereum’s research community, particularly in exploring future-proof, resilient, and intelligent consensus models.
I am eager to collaborate, share insights, and learn from the pioneering work within the Ethereum research team to help advance the state of decentralized consensus.

Keywords: Distributed Consensus, Byzantine Fault Tolerance (BFT), Blockchain Protocols, Graph Neural Networks (GNN), Reinforcement Learning (RL), Protocol Design, Scalability, Security, Ethereum Research.