New Results of the Sui Academic Research Award Announced: Global Renowned Universities Participate, 17 Projects Funded with 420,000 USD

Recently, the Sui Foundation announced the winners of the new round of Sui Academic Research Awards. This program aims to fund research that promotes the development of Web3, with a particular focus on breakthroughs in blockchain networks, smart contract programming, and technologies related to products built on Sui.

In the past two phases, a total of 17 proposals from internationally renowned universities have been approved, with a total funding amount of $425,000. 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 Voting Diversity in Decentralized Autonomous Organizations ( DAOs )

A team led by Professor Ari Juels from Cornell University will explore the nature of decentralized organizations, establish metrics to measure the degree of decentralization of DAOs, and study practical methods to enhance decentralization within organizations.

Adaptive Secure Asynchronous DAG Consensus Protocol

Dr. Philipp Jovanovic from University College London proposed the development of an asynchronous directed acyclic graph ( DAG ) protocol to enhance resilience against attacks and adapt to the ever-changing adversarial environment. The protocol aims to provide better security and adaptability while maintaining a performance level close to that of partially synchronous models.

Sui Smart Contract Audit Based on Large Language Models

Professor Arthur Gervais's team at University College London plans to use large language models such as GPT-4-32k and Claude-v2-100k to improve the auditing process of Move smart contracts. They will expand their research scope to Sui smart contracts based on preliminary analysis experience of Solidity DeFi contracts, emphasizing the importance of timely and robust security assessments.

Systematic research in the field of password consensus protocols

Professor Christopher Cachin from the University of Bern will conduct a comprehensive investigation into the current field of consensus protocols, providing new insights into cryptographic consensus protocols that will help better understand existing algorithms and offer new ideas for designing distributed protocols.

High-Trust Verification Framework for Decentralized Oracle Protocols

Dr. Giselle Reis from Carnegie Mellon University and Bruno Woltzenlogel Paleo from Djed Alliance will create a framework to rigorously analyze and verify blockchain oracles through formal methods. The research will utilize the Coq proof management system to develop a comprehensive library of definitions and proof strategies.

Identify blockchain scalability bottlenecks

The team led by Professor Roger Wattenhofer at ETH Zurich will focus on identifying scalability bottlenecks arising from design flaws in smart contracts and exploring how to influence parallelization potential by adjusting transaction fees.

Mechanical Verification of the Bullshark Consensus Protocol

Professor Ilya Sergey from the National University of Singapore will use modern computer-aided verification tools to formally verify the properties of the Bullshark protocol, advancing the understanding of DAG-based consensus protocols and providing the first mechanically verified model for distributed systems research.

Blockchain Benchmarking Standard Framework ( BBSF )

Professor Henry F. Korth from Lehigh University proposed the creation of a standardized benchmark format for blockchain, aimed at fairly comparing various L1 blockchains and L2 scaling solutions, providing users and developers with transparent insights into chain performance.

Build a scalable and decentralized shared sorting layer

Professor Min Suk Kang from the Korea Advanced Institute of Science and Technology will explore the possibility of using Bullshark/Mysticeti as a shared sequencer algorithm, studying how to enable multiple Rollups to use Sui as a sequencing layer and interpret transactions based on their respective execution layers.

Local fee market for optimal congestion pricing

Professor Abdoulaye Ndiaye from New York University will study the local pricing market to optimize congestion pricing, exploring analogies between trading congestion and executing transactions on blockchain networks, with the goal of establishing an effective pricing mechanism that reflects the state of network congestion.

Sharded Automated Market Maker ( SAMM )

Professor Ittay Eyal's team at the Technion - Israel Institute of Technology is developing the concept of "sharded contracts" to increase concurrency through multiple contracts. The research focuses on adjusting the incentive mechanisms for liquidity providers and traders to maintain multiple AMM shards, achieving fully parallelized sharded AMMs.

Private Information Disclosure in Competitive Mechanisms

Professor Andrea Attar from Tor Vergata University in Rome will explore new approaches to market mechanism design, studying the impact of designers privately disclosing information to agents on market outcomes and strategic interactions, aiming to provide deep insights into modern market dynamics and competition.

Generate Sui smart contracts using large language models

Ken Koedinger and Eason Chen from Carnegie Mellon University will study how to fine-tune large language models by using Move code and Sui-specific prompts to address the current challenges that LLMs face in generating Move language smart contracts.

COMET: Move language transition comparison metrics and framework

Professor George Giaglis's team at the University of Nicosia will conduct a comprehensive comparative analysis between Solidity and Move, aiming to facilitate a deep understanding of Move's features and capabilities, and to create a framework that helps developers transition smoothly to Move development.

Innovative DeFi: Deep Learning Methods Optimize Liquidity and Dynamic Fees on Sui

Rachid Guerraoui and Walid Sofiane from the École Polytechnique Fédérale de 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 to improve the DeFi protocol's responsiveness to market changes.

Assessing the volatility prediction capability of SUI

Professor Stavros Degiannakis from the Open University of Cyprus will investigate the effectiveness of the SPEC algorithm in predicting the volatility of Sui assets, focusing primarily on SUI using high-frequency price data and validating it in other blockchain assets.

low-memory post-quantum transparent zkSNARKs

Brett Falk and Pratyush Mishra from the University of Pennsylvania will focus on developing scalable zkSNARKs, while addressing the three main obstacles of prover time complexity, space complexity, and SRS size, to provide deployable scalable cryptographic proof schemes for various applications in blockchain technology.

These research projects cover multiple cutting-edge areas of blockchain technology, from consensus mechanism optimization to smart contract security, from DeFi innovation to cryptographic proof systems. Their results are expected to bring significant breakthroughs to the Sui ecosystem and the entire blockchain industry, promoting the further development of Web3 technology.