About

Maurice Herlihy is the An Wang Professor of Computer Science at Brown University. He holds an A.B. in Mathematics from Harvard University and a Ph.D. in Computer Science from MIT. He has served on the faculty of Carnegie Mellon University and the staff of DEC Cambridge Research Lab. Herlihy is a distinguished researcher recognized with numerous prestigious awards, including the 2003 Dijkstra Prize in Distributed Computing, the 2004 Gödel Prize in theoretical computer science, the 2008 ISCA influential paper award, the 2012 Edsger W. Dijkstra Prize, and the 2013 Wallace McDowell award. He received a 2012 Fulbright Distinguished Chair in the Natural Sciences and Engineering Lecturing Fellowship. He is a fellow of the ACM, the National Academy of Inventors, the National Academy of Engineering, and the National Academy of Arts and Sciences. In 2022, he won his third Dijkstra Prize. Between 2022 and 2025, he was appointed Fellow-Ambassadeur of the Centre national de recherche scientifique (CNRS) in France. In 2025, he received honorary doctorates from the Università della Svizzera italiana and from Technion (Israel Institute of Technology).

Research topics

• Computer Science
• Computer Security
• Theoretical computer science
• Database
• Programming language
• Business
• Parallel computing

Selected publications

• Defensive Rebalancing for Automated Market Makers

  Open MIND · 2026-01-26

  preprintSenior author

  This paper introduces and analyzes \emph{defensive rebalancing}, a novel mechanism for protecting constant-function market makers (CFMMs) from value leakage due to arbitrage. A \emph{rebalancing} transfers assets directly from one CFMM's pool to another's, bypassing the CFMMs' standard trading protocols. In any \emph{arbitrage-prone} configuration, we prove there exists a rebalancing to an \textit{arbitrage-free} configuration that strictly increases some CFMMs' liquidities without reducing the liquidities of the others. Moreover, we prove that a configuration is arbitrage-free if and only if it is \emph{Pareto efficient} under rebalancing, meaning that any further direct asset transfers must decrease some CFMM's liquidity. We prove that for any log-concave trading function, including the ubiquitous constant product market maker, the search for an optimal, arbitrage-free rebalancing that maximizes global liquidity while ensuring no participant is worse off can be cast as a convex optimization problem with a unique, computationally tractable solution. We extend this framework to \emph{mixed rebalancing}, where a subset of participating CFMMs use a combination of direct transfers and standard trades to transition to an arbitrage-free configuration while harvesting arbitrage profits from non-participating CFMMs, and from price oracle market makers such as centralized exchanges. Our results provide a rigorous foundation for future AMM protocols that proactively defend liquidity providers against arbitrage.

  DOI

• Defensive Rebalancing for Automated Market Makers

  ArXiv.org · 2026-01-26

  articleOpen accessSenior author

  This paper introduces and analyzes \emph{defensive rebalancing}, a novel mechanism for protecting constant-function market makers (CFMMs) from value leakage due to arbitrage. A \emph{rebalancing} transfers assets directly from one CFMM's pool to another's, bypassing the CFMMs' standard trading protocols. In any \emph{arbitrage-prone} configuration, we prove there exists a rebalancing to an \textit{arbitrage-free} configuration that strictly increases some CFMMs' liquidities without reducing the liquidities of the others. Moreover, we prove that a configuration is arbitrage-free if and only if it is \emph{Pareto efficient} under rebalancing, meaning that any further direct asset transfers must decrease some CFMM's liquidity. We prove that for any log-concave trading function, including the ubiquitous constant product market maker, the search for an optimal, arbitrage-free rebalancing that maximizes global liquidity while ensuring no participant is worse off can be cast as a convex optimization problem with a unique, computationally tractable solution. We extend this framework to \emph{mixed rebalancing}, where a subset of participating CFMMs use a combination of direct transfers and standard trades to transition to an arbitrage-free configuration while harvesting arbitrage profits from non-participating CFMMs, and from price oracle market makers such as centralized exchanges. Our results provide a rigorous foundation for future AMM protocols that proactively defend liquidity providers against arbitrage.

  PublisherOA PDF

• Asynchronous Byzantine Consensus with Trusted Monotonic Counters

  Lecture notes in computer science · 2025-01-01

  book-chapter
  PublisherDOI

• The Smart Contract Model

  ArXiv.org · 2025-02-07 · 1 citations

  preprintOpen access

  Many of the problems that arise in the context of blockchains and decentralized finance can be seen as variations on classical problems of distributed computing. The smart contract model proposed here is intended to capture both the similarities and the differences between classical and blockchain-based models of distributed computing. The focus is on cross-chain protocols in which a collection of parties, some honest and some perhaps not, interact through trusted smart contracts residing on multiple, independent ledgers. While cross-chain protocols are capable of general computations, they are primarily used to track ownership of assets such as cryptocurrencies or other valuable data. For this reason, the smart contract model differs in some essential ways from familiar models of distributed and concurrent computing. Because parties are potentially Byzantine, tasks to be solved are formulated using elementary game-theoretic notions, taking into account the utility to each party of each possible outcome. As in the classical model, the parties provide task inputs and agree on a desired sequence of proposed asset transfers. Unlike the classical model, the contracts, not the parties, determine task outputs in the form of executed asset transfers, since they alone have the power to control ownership.

  PublisherOA PDFDOI

• SoK: Concurrency in Blockchain -- A Systematic Literature Review and the Unveiling of a Misconception

  ArXiv.org · 2025-06-02

  preprintOpen access

  Smart contracts, the cornerstone of blockchain technology, enable secure, automated distributed execution. Given their role in handling large transaction volumes across clients, miners, and validators, exploring concurrency is critical. This includes concurrent transaction execution or validation within blocks, block processing across shards, and miner competition to select and persist transactions. Concurrency and parallelism are a double-edged sword: while they improve throughput, they also introduce risks like race conditions, non-determinism, and vulnerabilities such as deadlock and livelock. This paper presents the first survey of concurrency in smart contracts, offering a systematic literature review organized into key dimensions. First, it establishes a taxonomy of concurrency levels in blockchain systems and discusses proposed solutions for future adoption. Second, it examines vulnerabilities, attacks, and countermeasures in concurrent operations, emphasizing the need for correctness and security. Crucially, we reveal a flawed concurrency assumption in a major research category, which has led to widespread misinterpretation. This work aims to correct that and guide future research toward more accurate models. Finally, we identify gaps in each category to outline future research directions and support blockchain's advancement.

  PublisherOA PDFDOI

• Brief Announcement: Cross-Chain Consensus

  Lecture notes in computer science · 2025-11-17

  book-chapterSenior author
  PublisherDOI

• Conthereum: Concurrent Ethereum Optimized Transaction Scheduling for Multi-Core Execution

  ArXiv.org · 2025-04-09

  preprintOpen access

  Conthereum is a concurrent Ethereum solution for intra-block parallel transaction execution, enabling validators to utilize multi-core infrastructure and transform the sequential execution model of Ethereum into a parallel one. This shift significantly increases throughput and transactions per second (TPS), while ensuring conflict-free execution in both proposer and attestor modes and preserving execution order consistency in the attestor. At the heart of Conthereum is a novel, lightweight, high-performance scheduler inspired by the Flexible Job Shop Scheduling Problem (FJSS). We propose a custom greedy heuristic algorithm, along with its efficient implementation, that solves this formulation effectively and decisively outperforms existing scheduling methods in finding suboptimal solutions that satisfy the constraints, achieve minimal makespan, and maximize speedup in parallel execution. Additionally, Conthereum includes an offline phase that equips its real-time scheduler with a conflict analysis repository obtained through static analysis of smart contracts, identifying potentially conflicting functions using a pessimistic approach. Building on this novel scheduler and extensive conflict data, Conthereum outperforms existing concurrent intra-block solutions. Empirical evaluations show near-linear throughput gains with increasing computational power on standard 8-core machines. Although scalability deviates from linear with higher core counts and increased transaction conflicts, Conthereum still significantly improves upon the current sequential execution model and outperforms existing concurrent solutions under a wide range of conditions.

  PublisherOA PDFDOI

• Byzantine Reliable Broadcast with One Trusted Monotonic Counter

  Lecture notes in computer science · 2024-10-19 · 1 citations

  book-chapter
  PublisherDOI

• Invited Paper: The Smart Contract Model

  Lecture notes in computer science · 2024-10-19 · 1 citations

  book-chapter
  PublisherDOI

• Invited Paper: Further Decentralizing Decentralized Finance: Loss vs Rebalancing Without a Shared Reference Market

  2024-10-09

  article1st authorCorresponding

  Automated market makers (AMMs) typically rely on arbitrage agents to keep prices in line with a shared reference market such as a large centralized exchange. This paper considers an alternative, even-more-decentralized model where prices must stabilize without a shared reference market.We first consider a model where there is one population of AMMs, and another of arbitrage agents who seek to profit from pairwise price differences between randomly-chosen AMMs. For constant-product AMMs, repeated random pair-wise arbitrage causes the AMMs’ expected prices to converge within any precision ϵ > 0 in $\Theta \left( {\max \left( {{n^2}\log n,\log \frac{1}{\varepsilon }} \right)} \right.$ interactions, and arbitrage agents’ profits are proportional to the original price imbalances. Within certain limits, the arbitrage agents can collude to set the final stable price.If, instead, randomly-chosen pairs of AMMs could rebalance their asset pools directly, capturing profits that would have gone to arbitrage agents, then expected AMM prices converge within ϵ with respective upper and lower bounds of $\Omega \left( {\max \left( {{n^2}\log n,\log \frac{1}{\varepsilon }} \right)} \right.$ and $O\left( {\max \left( {{n^2}\log n,\log \frac{1}{\varepsilon }} \right)} \right.$ interactions. Within certain limits, the AMMs can collude to set the final stable price.

  PublisherDOI

Recent grants

• SHF: Medium: Collaborative Research: Run-Time Support for Scalable Concurrent Programming

  NSF · $540k · 2016–2019

• FMitF:Collaborative Research:Track I:Formal Techniques for Monitoring Low-level Cross-chain Functions

  NSF · $375k · 2019–2023

• Combinatorial Topology and Concurrent Computation

  NSF · $300k · 2009–2014

• A Unified Open-Source Transactional-Memory Infrastructure

  NSF · $250k · 2008–2012

• SHF: Medium: Collaborative Research: Transactional Software Infrastructures: Making the Most of Hardware Transactions

  NSF · $600k · 2013–2017

Frequent coauthors

• R. Iris Bahar

  Brown University

  65 shared

• Sergio Rajsbaum

  59 shared

• Nir Shavit

  56 shared

• Costas Busch

  48 shared

• Victor Luchangco

  43 shared

• Tali Moreshet

  41 shared

• Eric Koskinen

  37 shared

• Jack Dongarra

  35 shared

Labs

• Maurice Herlihy's Personal PagePI

Education

• B.A., Math

  Harvard

• Ph.D., CS

  MIT