Two SEAS faculty members - Charalampos Papamanthou and Leandros Tassiulas – are the recipients of the JP Morgan Chase Faculty Research Award for their work on artificial intelligence. The awards aim to “empower the best research thinkers across AI today” in order to “advance cutting-edge AI research to solve real-world problems.”
Following are summaries of the winning projects:
Category: AI to Liberate Data Safely
“New Zero-Knowledge Arguments for Cryptocurrencies”
Zero-knowledge proofs are cryptographic tools that enable a party to convince the world about the validity of a mathematical statement without revealing why the statement is true. They have recently found a lot of applications in privacy-preserving blockchains and cryptocurrencies: Using zero-knowledge proofs, a payer can transfer money to a payee without revealing (not even to the “bank” responsible for maintaining balances and checking transaction validity) any information about the identities/amount involved in the transaction. The proposed research will construct new zero-knowledge proofs with improved scalability and will explore various applications of zero-knowledge proofs, such as building efficient and more secure systems for privacy-preserving smart contracts.
Papamanthou serves as the co-director of the Yale Applied Cryptography Laboratory and his research focuses on computer security and applied cryptography. His current efforts include verifiable and privacy-preserving computations, with applications to cloud computing security, leakage-abuse attacks on searchable encryption systems, private and scalable blockchains and cryptocurrencies, as well as building real-world privacy-preserving systems.
Category: AI to Eradicate Financial Crime
“Combining DeFi and AI for Intelligent DeFi Applications”
Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is the exchange of financial assets without trusted intermediaries. Ethereum smart contracts provide an ecosystem of decentralized finance (DeFi), where users can interact with lending pools, Automated Market Makers (AMMs) exchanges, stablecoins, derivatives, etc. with a cumulative locked value that has exceeded the amount of 100B USD. While DeFi comes with high rewards, it also carries plenty of risks. We propose a system that will leverage AI methods to reduce DeFi investment risk, improve protocol security and enable intelligent DeFi lending, while analyzing users’ activity and recognizing patterns. The system will utilize and get trained on publicly available transaction data, archived on our in-house Ethereum node infrastructure that is set up for this purpose.
Leandros Tassiulas is the John C. Malone Professor of Electrical Engineering and Computer Science. His research interests are in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, architectures and protocols of wireless systems, sensor networks, novel internet architectures and experimental platforms for network research. His most notable contributions include the max-weight scheduling algorithm and the back-pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems.