Igor L. Markov


Igor Leonidovich Markov is a Ukrainian-American computer scientist and engineer. A former professor of electrical engineering and computer science at the University of Michigan, Markov is known for contributions in quantum computation, algorithms for integrated circuit optimization, electronic design automation, artificial intelligence platforms, and AI for chip design. He authored a widely cited 2014 review in Nature on limits to computation. Markov is a Fellow of IEEE, a Distinguished Scientist with the Association for Computing Machinery, and a Distinguished Architect at Synopsys. Markov currently serves on the board of Nova Ukraine, a U.S.-based nonprofit that has delivered large-scale humanitarian aid to Ukraine, where he has participated in oversight and fundraising activities.

Early life and education

Igor Markov was born and raised in Kyiv, Ukraine. He graduated from Kyiv Natural Science Lyceum No. 145 and completed his undergraduate studies in mathematics at Taras Shevchenko National University of Kyiv. Markov obtained an M.A. degree in mathematics and a Doctor of Philosophy degree in computer science from UCLA in 2001.

Academic career

From the early 2000s through 2018, Markov was a professor at the University of Michigan. In 2007, he was a visiting associate professor at the National Taiwan University. At the University of Michigan, Markov served as chair of the undergraduate Computer Engineering and Computer Science programs from 2009 to 2012 and in 2015, overseeing curriculum development and accreditation efforts. He was promoted to full professor in 2012. In 2013-2014, Markov was a visiting professor at Stanford University.
Markov served as associate editor of ACM Transactions on Design Automation of Electronic Systems, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, and Communications of the ACM. He served at the ACM Special Interest Group on Design Automation as a member of the Advisory Board and the Executive Committee. Additionally, he contributed to the revision of the 2011 ACM Computing Classification System, where he led the Hardware category.

Industry career

In the 1990s, Markov worked as a software engineer at the Parametric Technology Corporation. In 2008, he worked as a principal engineer at Synopsys during a sabbatical leave from University of Michigan.
In 2014, Markov joined Google’s Search team, where he completely rewrote the algorithm responsible for trivial‐query detection, improving its worst‐case time complexity from exponential to linear and reducing query‐processing latency in production workloads. Working at Google through 2017, he also developed and implemented a new algorithm for information retrieval that finds top-N relevant records for user-configurable priorities without identifying all relevant records.
From 2018 to 2023, he worked at Meta on machine learning platforms and news feed integrity. In the early 2020s, he consulted for IonQ on quantum computer design and optimization. Markov returned to Synopsys in 2024 to work on computing hardware as a Distinguished Architect.
In October 2025, Markov was elected vice chair of the Si2 Large Language Model Benchmarking Coalition, a collaborative industry initiative focused on advancing AI applications for silicon design and verification.

Nonprofit leadership

Since 2017, Markov has been a member of the board of directors of Nova Ukraine, a California 501 charity organization that provides aid and services to people in Ukraine. According to annual IRS Form 990 filings, the organization has raised over $100 million in aid and distributed a large part of it in Ukraine;
news media reporting in the US and Ukraine corroborates large-scale shipments of medical supplies to Ukraine and numerous aid projects during 2022–2024.
Markov is also the vice president and a member of the board of directors of the American Coalition for Ukraine, an umbrella organization that coordinates one hundred US-based nonprofits concerned about events in Ukraine.

Accolades

The ACM Special Interest Group on Design Automation honored Markov with an Outstanding New Faculty Award in 2004. Markov received the NSF CAREER award in 2005. Markov won 2007-08 EECS Outstanding Achievement Awards at the University of Michigan in recognition of excellence in research, teaching, and service. Markov was the 2009 recipient of IEEE CEDA Ernest S. Kuh Early Career Award "for outstanding contributions to algorithms, methodologies and software for the physical design of integrated circuits." Along with Andrew Kahng, in 2011 Igor Markov won the A. Richard Newton GSRC Industrial Impact Award for research on circuit placement and the Capo software package, used by researchers and companies.
Markov became an ACM Distinguished Member in 2011. In 2013, he was named an IEEE fellow "for contributions to optimization methods in electronic design automation".

Award-winning publications

Markov's peer-reviewed scholarly work was recognized with five best-paper awards, including four at major conferences and a journal in the field of electronic design automation, and one in theoretical computer science:

Quantum computing

Markov's contributions include results on quantum circuit synthesis and simulation of quantum circuits on conventional computers.
  • An algorithm for the synthesis of linear reversible circuits with at most CNOT gates that was extended by Scott Aaronson and Daniel Gottesman to perform optimal synthesis of Clifford circuits, with applications to quantum error correction.
  • Optimal synthesis of a two-qubit unitary that uses the minimal number of CNOT gates.
  • Asymptotically optimal synthesis of an -qubit quantum circuit that implements a given unitary matrix using no more than CNOT gates and induces an initial quantum state using no more than CNOT gates. In independent evaluations, researchers observed strong performance of the circuit synthesis algorithm published by Markov as implemented in IBM Qiskit software.
  • Efficient simulation of quantum circuits with low tree-width using tensor-network contraction. Follow-up works extended this technique with approximations, which allowed them to simulate quantum Fourier transform in polynomial time. Markov's work was used in an essential way in the first proof that quantum Fourier transform can be classically simulated.
  • Markov contributed to advancements in ion trap quantum computing at IonQ as a consultant and research collaborator. He led research on an error mitigation technique called "debiasing via frugal symmetrization" that addresses computational inaccuracies in quantum systems by using computational symmetries to reduce errors across multiple algorithm implementations. The method improved the accuracy of quantum computations without additional execution overhead.
Markov has been leading early quantum computing efforts at Synopsys with emphasis on leveraging the existing design and manufacturing ecosystem for silicon chips. At a 2025 quantum computing hardware review, Igor Markov highlighted how established semiconductor fabrication techniques are instrumental in scaling up quantum computing systems and discussed innovations in compiler technology and atomic-scale modeling needed for efficient qubit production.
Markov has been leading Synopsys collaborations with the Quantum Scaling Alliance to advance the simulation and design of large-scale programmable superconducting qubit arrays. These efforts focus on advancing software tools and methodologies for modeling, verification, and optimization of quantum hardware.

Physical design of integrated circuits

Markov's Capo placer provided a baseline for comparisons used in the placement literature. The placer was open-sourced, commercialized and used to design industry chips. Markov's contributions include algorithms, methodologies and software for
  • Circuit partitioning: high-performance heuristic optimizations for hypergraph partitioning
  • Placement: algorithms for finding locations of circuit components that optimize interconnects between those components
  • Floorplanning: algorithms and methodologies for chip planning in terms of locations of large components
  • Routing: algorithms based on Lagrangian relaxation to construct global wire routs on a multilayer grid structure
  • Physical synthesis: algorithms and methodologies for altering logic circuits to admit layouts with shorter interconnects or lower latency

    Artificial intelligence

At Meta, Markov led the development of an end-to-end AI platform called Looper, an internal machine learning platform that supports end-to-end product decision-making and optimization. It provides easy-to-use APIs for data ingestion, feature extraction, model training, and real-time inference, along with built-in support for multi-objective optimization and feedback collection. Looper's modular architecture enables rapid experimentation and deployment, and by 2022 it was adopted by over 90 Meta product teams for tasks ranging from content ranking to user engagement analysis. In 2023, Markov gave a presentation at the AI conference on the current limitations of generative AI, including output errors and hallucinations, limited reasoning capabilities, and problematic numerical skills.
At Synopsys, Markov leads the AI Disruption Task Force that tracks the impact of AI on chip design and evaluates possible business disruptions. In June 2025, he delivered a one-hour tutorial titled "AI for EDA: Challenges and Opportunities" as part of Short Course 2 at the Symposium on VLSI Technology and Circuits in Kyoto.