About

Moshe Y. Vardi is a University Professor and the George Distinguished Service Professor in Computational Engineering at Rice University. He has received numerous awards including three IBM Outstanding Innovation Awards, the ACM SIGACT Goedel Prize, the ACM Kanellakis Award, the ACM SIGMOD Codd Award, the Blaise Pascal Medal, the IEEE Computer Society Goode Award, the EATCS Distinguished Achievements Award, the Southeastern Universities Research Association's Distinguished Scientist Award, the ACM SIGLOG Church Award, the Knuth Prize, and the ACM Allen Newell Award. Vardi is the author and co-author of over 600 papers and has written two books: 'Reasoning about Knowledge' and 'Finite Model Theory and Its Applications.' He is a Fellow of several prestigious organizations, including the American Association for the Advancement of Science, the American Mathematical Society, the Association for Computing Machinery, the American Association for Artificial Intelligence, the European Association for Theoretical Computer Science, the Institute for Electrical and Electronic Engineers, and the Society for Industrial and Applied Mathematics. He is a member of the US National Academy of Engineering and the National Academy of Science, as well as the American Academy of Arts and Science, the European Academy of Science, and Academia Europaea. Vardi holds seven honorary doctorates and is currently a Senior Editor of the Communications of the ACM, having previously served as Editor-in-Chief. His research areas include automated reasoning, databases, computational complexity theory, and design specification and verification.

Research topics

• Computer Science
• Artificial Intelligence
• Political Science
• Theoretical computer science
• Sociology
• Engineering ethics
• Mathematics
• Algorithm
• Programming language
• Pedagogy
• Knowledge management
• Engineering
• Law

Selected publications

• What Came First, Mathematics or Computing?

  Lecture notes in computer science · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• LTLf Synthesis Under Unreliable Input

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 1 citations

  articleOpen accessSenior author

  We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables. We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synthesis. Then we devise three different solution techniques: one based on direct automata manipulation, which is 2EXPTIME, one disregarding unreliable input variables by adopting a belief construction, which is 3EXPTIME, and one leveraging second-order quantified LTLf (QLTLf), which is 2EXPTIME and allows for a direct encoding into monadic second-order logic, which in turn is worst-case nonelementary. We prove their correctness and evaluate them against each other empirically. Interestingly, theoretical worst-case bounds do not translate into observed performance; the MSO technique performs best, followed by belief construction and direct automata manipulation. As a byproduct of our study, we provide a general synthesis procedure for arbitrary QLTLf specifications.

  PublisherDOI

• Keeping the Dream Alive: The Power and Promise of Federally Funded Research

  Communications of the ACM · 2025-09-11

  articleOpen access

  Technological innovation is not just a byproduct of American ingenuity-it has long been its driving force.Foundational studies have shown that most of the U.S.'s' 20 th -century productivity growth stemmed from domestic breakthroughs in science and technology.And as history has shown, this was no accident.After World War II, the U.S. government made a bold strategic choice: to link sustained

  PublisherOA PDFDOI

• Will AI Destroy the World Wide Web?

  Communications of the ACM · 2025-08-18 · 14 citations

  articleOpen access1st authorCorresponding

  Will AI Destroy the World Wide Web?The World Wide Web (Web) emerged as a new medium in the mid-1990s.It was invented by Tim Berners-Lee at the European Organization for Nuclear Research (CERN) in 1989, but its exploding popularity was also enabled by the release of the Mosaic Web browser in 1993 and the Internet becoming commercially available in 1995.A communication revolution was launched.Roughly 30 years later, the release of ChatGPT by OpenAI in Nov. 2022 launched another revolution.High-quality generation of natural-language text, defined as the hallmark of intelligence by Alan Turing in 1950, is suddenly widely available.I wonder, however, if the generative AI (GenAI) revolution will end up devouring the Web revolution.

  PublisherOA PDFDOI

• It's Not the AI - It's Each of Us! Ten Commandments for the Wise & Responsible Use of AI

  ArXiv.org · 2025-11-18

  preprintOpen access

  Artificial intelligence (AI) is no longer futuristic; it is a daily companion shaping our private and work lives. While AI simplifies our lives, its rise also invites us to rethink who we are - and who we wish to remain - as humans. Even if AI does not think, feel, or desire, it learns from our behavior, mirroring our collective values, biases, and aspirations. The question, then, is not what AI is, but what we are allowing it to become through data, computing power, and other parameters "teaching" it - and, even more importantly, who we are becoming through our relationship with AI. As the EU AI Act and the Vienna Manifesto on Digital Humanism emphasize, technology must serve human dignity,social well-being, and democratic accountability. In our opinion, responsible use of AI is not only a matter of code nor law, but also of conscientious practice: how each of us engages and teaches others to use AI at home and at work. We propose Ten Commandments for the Wise and Responsible Use of AI are meant as guideline for this very engagement. They closely align with Floridi and Cowls' five guiding principles for AI in society - beneficence, non-maleficence, autonomy, justice, and explicability.

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• Techno-Optimism, Techno-Pessimism, and Techno-Realism

  Communications of the ACM · 2025-12-18

  article1st authorCorresponding
  PublisherDOI

• Automata Linear Dynamic Logic on Finite Traces

  Logical Methods in Computer Science · 2025-07-09

  articleOpen accessSenior author

  Temporal logics are widely used by the Formal Methods and AI communities. Linear Temporal Logic is a popular temporal logic and is valued for its ease of use as well as its balance between expressiveness and complexity. LTL is equivalent in expressiveness to Monadic First-Order Logic and satisfiability for LTL is PSPACE-complete. Linear Dynamic Logic (LDL), another temporal logic, is equivalent to Monadic Second-Order Logic, but its method of satisfiability checking cannot be applied to a nontrivial subset of LDL formulas. Here we introduce Automata Linear Dynamic Logic on Finite Traces (ALDL_f) and show that satisfiability for ALDL_f formulas is in PSPACE. A variant of Linear Dynamic Logic on Finite Traces (LDL_f), ALDL_f combines propositional logic with nondeterministic finite automata (NFA) to express temporal constraints. ALDL$_f$ is equivalent in expressiveness to Monadic Second-Order Logic. This is a gain in expressiveness over LTL at no cost.

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• LTLf+ and PPLTL+: Extending LTLf and PPLTL to Infinite Traces

  2025-09-01 · 1 citations

  articleSenior author

  We study two logics, LTLf+ and PPLTL+, to express properties of infinite traces, that are based on the linear-time temporal logics LTLf and PPLTL on finite traces. LTLf+/PPLTL+ use levels of Manna and Pnueli’s LTL safety-progress hierarchy, and thus have the same expressive power as LTL. However, they also retain a crucial characteristic of reactive synthesis for the base logics: the game arena for strategy extraction can be derived from deterministic finite automata (DFA). Consequently, these logics circumvent the notorious difficulties associated with determinizing infinite trace automata, typical of LTL synthesis. We present optimal DFA-based technique for solving reactive synthesis for LTLf+ and PPLTL+. Additionally, we adapt these algorithms to optimally solve satisfiability and model-checking for these two logics.

  PublisherDOI

• Big Tech, You Need Academia. Speak Up!

  Communications of the ACM · 2025-04-28 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• I Teach Computer Science, and That Is Not All

  Communications of the ACM · 2025-06-13

  articleOpen access1st authorCorresponding

  I teach computer science, and that is all," wrote Boaz Barak, of Harvard University, in a recent oped in The New York Times.a The main point of the op-ed was to protest the growing politicization of U.S. higher education, especially at elite universities, where we have seen many faculty members proceed from scholarship to advocacy.But in spite of the provocative title, the content of Barak's op-ed is quite more nuanced."We should not normalize bringing one's ideology to the classroom," wrote Barak, and I could not agree more.But he also wrote that "The interaction of computer science and policy sometimes arises in my classes, and I make sure to present multiple perspectives."Here, Barak is advocating fairness and balance, rather than neutrality and avoidance of non-technical topics.And, again, I could not agree more.Following the Cambridge Analytica Scandal in 2016, we decided in 2018 to introduce at Rice University a computer science course on computing, ethics, and society.The course started as an elective, but eventually, with the strong encouragement of our students, became a required course.The course focuses on the societal, economical, and political impacts of computing.We cannot avoid controversial topics, but we present the issues from multiple perspectives, focusing on the cultivation of societal responsibility.Thus, we teach computer science, but that is definitely not all.This is critical, I believe, in view of Barak's acknowledgement of the declining trust by the public in higher education, and his exhortation "for us academics to step up the long overdue work of restoring trust in universities."The loss of popular trust in higher education is a complicated phenomenon, but I believe academia bears some responsibility for it.What is the fundamental purpose of higher education?This is a topic of much debate these days, sometimes framed as a choice between the purpose of truth and the purpose of social justice.Others have argued that the purpose of universities is not truth, rather it is inquiry.I believe those who advocate truth, inquiry, or social justice as the purpose of higher education are getting it wrong.I believe the purpose of universities was best expressed in the American Association of University Professors' influential 1940 statement on academic freedom: "Institutions of higher education are conducted for the common good."b This is not to say that truth, inquiry, and social justice are not goals of higher education; rather, they are means toward an end, which is the public good.From where I sit, however, it seems that rather than pursue the public good, elite academic institutions are often pursuing money and prestige.I am not arguing that money and prestige are unimportant, but they should always be considered means and not ends.The end should be to promote the public good.I believe higher education does make a significant contribution to the public good, but this raison d'tre has disappeared from our academic discourse.No wonder the public does not see higher education in these terms.Only following the unwarranted attacks by

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Recent grants

• Eager: Automated Synthesis for System Design

  NSF · $250k · 2010–2013

• Automata-Theoretic Approach to Design Verification

  NSF · $200k · 2003–2007

• SHF: Medium: Collaborative Research: Formal Analysis and Synthesis of Multiagent Systems with Incentives

  NSF · $800k · 2017–2022

• SOD:HCER: A Theory of Automated Design

  NSF · $200k · 2006–2009

• An Automata-Theoretic Approach to Design Synthesis

  NSF · $300k · 2007–2010

Frequent coauthors

• Juris Hartmanis

  Lancaster University

  312 shared

• Friedemann Mattern

  308 shared

• Oscar Nierstrasz

  307 shared

• Bernhard Steffen

  TU Dortmund University

  307 shared

• David Hutchison

  Lancaster University

  307 shared

• Madhu Sudan

  Harvard University Press

  307 shared

• Moni Naor

  307 shared

• Doug Tygar

  University of California, Berkeley

  307 shared

Awards & honors

• IBM Outstanding Innovation Awards
• ACM SIGACT Goedel Prize
• ACM Kanellakis Award
• ACM SIGMOD Codd Award
• Blaise Pascal Medal