Pioneering Research and Innovation in Blockchain

Blockchain technology is built around transparency and verifiable systems. However, current blockchain games rely on opaque centralized systems to facilitate action-oriented gameplay engines. This research explores extending blockchain technology's trust features further into games that bridge on- and off-chain systems.

This project aims to develop an asynchronous DAG-based protocol to enhance robustness against attacks and adapt to changing adversaries. Unlike current partially synchronous models vulnerable to denial-of-service attacks and static adversaries, the proposed protocol will offer improved security and adaptability while maintaining performance levels as close as possible to its partially synchronous counterparts.

The proposal will explore the use of Bullshark/Mysticeti as a shared sequencer algorithm. This will involve running multiple rollups that use Sui as the sequencing layer, allowing them to interpret transactions as per their execution layer.

The research aims to develop scalable zkSNARKs by addressing three main barriers: prover time complexity, prover space complexity, and prover SRS size. The goal is to construct zkSNARKs that overcome these barriers simultaneously, leading to deployment-ready, scalable cryptographic proofs for various applications in blockchain technology.

Smart contracts on Sui are written in the Move language, posing challenges for current large language models (LLMs) due to limited training data. This research aims to address this by fine-tuning LLMs with Move code and Sui-specific prompts. This research will gather a comprehensive dataset of Move language examples, enhance prompt engineering, and implement fine-tuning, comparing LLM effectiveness across these methods.

By surveying the current landscape of consensus, this project will offer novel insights into cryptographic consensus protocols. Results will lead to better understanding of existing algorithms and to new structures for designing distributed protocols.

By identifying bottlenecks stemming from smart contract design flaws, this project seeks to enhance the parallelization potential of blockchain applications. It will also explore how adjusting transaction fees can impact parallelization potential.

This research aims to formally verify Bullshark's properties using modern computer-aided verification tools, advancing the understanding of DAG-based consensus protocols. Additionally, the project will contribute to the advancement of distributed systems research by providing the first mechanically verified models of DAG-based consensus protocols.

This research investigates local fee markets to optimize congestion pricing, drawing parallels between congestion in transportation and transaction execution in blockchain networks. The goal is to establish efficient pricing mechanisms reflecting congestion states for optimal resource allocation. Initial analyses of user valuations and delay costs show promising directions for congestion pricing in blockchains.

This research will develop a hybrid deep learning model for optimal range predictions in Sui DeFi protocols. It combines enhanced recurrent neural networks and deep reinforcement learning, while integrating social media sentiment analysis to enhance predictive accuracy. This research seeks to improve DeFi protocols' responsiveness to market changes, positioning Sui as a leader in next-gen DeFi protocols.