The latest round of academic research funding from Sui is announced: top universities around the world participate, with 17 projects receiving over $420,000 in support.

Recently, the Sui Foundation announced the winners of a new round of academic research funding. This program aims to support research that promotes the development of Web3 technologies, particularly in the areas of blockchain networks, smart contract programming, and products built on Sui.

In the past two phases, a total of 17 research proposals from internationally renowned universities have been approved, with a total funding amount of $425,000. The participating universities include the Korea Advanced Institute of Science and Technology, University College London, École Polytechnique Fédérale de Lausanne, and the National University of Singapore, among others.

Overview of Award-winning Proposals

Research on the Diversity of DAO Voting Groups

The research team at Cornell University will explore the nature of decentralized organizations, establish metrics to measure the degree of decentralization of DAOs, and propose practical methods to enhance internal decentralization within organizations.

Adaptive Security of Asynchronous DAG Protocol Consensus

Researchers at University College London have proposed the development of an asynchronous DAG protocol to enhance attack resistance and adapt to changing adversarial environments. The protocol aims to provide better security and adaptability than current partially synchronous models.

Sui Smart Contract Audit Based on Large Language Models

Another team from University College London plans to improve the auditing process of Move smart contracts using large language models such as GPT-4-32k and Claude-v2-100k. They will expand their research scope to Sui smart contracts based on previous analysis experience with Solidity contracts.

Research in Consensus Protocols

The project at the University of Bern will investigate the current field of consensus technologies, providing new insights into cryptographic consensus protocols that will help better understand existing algorithms and offer new ideas for designing distributed protocols.

Decentralized Oracle Protocol Verification Framework

Carnegie Mellon University and Djed Alliance have collaborated to develop a framework for rigorously analyzing and verifying blockchain oracles through formal methods. The project will utilize the Coq proof management system to develop a comprehensive library of definitions and proof strategies.

Identifying the scalability bottlenecks of blockchain

The research at ETH Zurich aims to identify performance bottlenecks arising from design flaws in smart contracts and to explore how adjusting transaction fees can enhance parallelization potential.

Bullshark Protocol Mechanized Verification

The project at the National University of Singapore will use modern computer-aided verification tools to formally verify the properties of Bullshark, advancing the research on DAG-based consensus protocols.

Blockchain Standardization Framework

Lehi University proposed the creation of a blockchain benchmarking standard format to fairly compare L1 blockchains and L2 scaling solutions, providing users and developers with transparent insights into chain performance.

Build a scalable decentralized shared sequence layer

Korea Advanced Institute of Science and Technology will explore using Bullshark/Mysticeti as a shared sorting algorithm, studying how to run multiple Rollups that use Sui as the sorting layer.

Local Fee Market Optimization Congestion Pricing

A research study by New York University investigates the local fee market to optimize congestion pricing, aiming to establish an effective pricing mechanism that reflects the state of network congestion and achieves optimal resource allocation.

Automated Market Maker for Sharding

The Israel Institute of Technology is developing the concept of sharded contracts to increase concurrency using multiple contracts. This project will explore how to adjust incentive mechanisms to maintain multiple AMM shards, achieving fully parallelizable sharded AMM.

Disclosure of Private Information in Competitive Mechanisms

The University of Tor Vergata in Rome explores new methods for designing market mechanisms, investigating the impact of designers privately disclosing information to agents on market outcomes, aiming to provide insights into modern market dynamics and competition.

Sui smart contracts generated based on large language models

The team at Carnegie Mellon University will study how to leverage Move code and Sui-specific hints to fine-tune large language models for better generation of Sui smart contracts.

Move language conversion comparison metrics and framework

The University of Nicosia will conduct a comprehensive comparative analysis between Solidity and Move, aimed at promoting a deeper understanding of the functionalities and capabilities of Move, and assisting developers in transitioning more easily to using Move for development.

Optimizing Sui DeFi's liquidity and dynamic fees based on deep learning

The Swiss Federal Institute of Technology Lausanne will develop a hybrid deep learning model for optimal range prediction in the Sui DeFi protocol, combining enhanced recurrent neural networks, deep reinforcement learning, and social media sentiment analysis.

SUI Volatility Prediction Capability Assessment

The Open University of Cyprus will investigate the effectiveness of the SPEC algorithm in predicting the volatility of Sui assets, with a primary focus on SUI, and will validate it across various blockchain assets.

low-memory post-quantum transparent zkSNARKs

The research at the University of Pennsylvania aims to develop scalable zkSNARKs by simultaneously addressing the three major obstacles of prover time complexity, space complexity, and SRS size, providing deployable scalable cryptographic proofs for various applications in blockchain technology.

These research projects cover multiple key areas of blockchain technology, from consensus mechanisms to smart contract security, from DeFi optimization to privacy protection. Their results are expected to bring significant breakthroughs and innovations to the Sui ecosystem and the entire blockchain industry.