Igor L. Markov

Last updated
Igor Leonidovich Markov
Ігор Леонідович Марков
Dr. Igor L Markov.webp
Born (1973-03-31) 31 March 1973 (age 51)
NationalityFlag of Ukraine.svg  Ukraine
Alma mater
Scientific career
Fields Computer Science, Electrical Engineering, Optimization, Electronic Design Automation, Artificial Intelligence, Quantum Computing
Institutions University of Michigan, Stanford University, Meta Platforms, Google
Thesis Top-Down Timing-Driven Placement with Direct Minimization of Maximal Signal Delay  (2001)
Doctoral advisor Andrew B. Kahng
Doctoral students Smita Krishnaswamy
Other notable students Vivek Shende

Igor Leonidovich Markov [a] (born in 1973) is an American professor, [1] computer scientist and engineer. Markov is known for results in quantum computation, work on limits of computation, research on algorithms for optimizing integrated circuits and on electronic design automation, as well as artificial intelligence. Additionally, Markov is an American non-profit executive [2] responsible for aid to Ukraine worth over a hundred million dollars. [3] [4] [5]

Contents

Igor L. Markov has no known relation to the mathematician Andrey Markov.

Career

Markov obtained an M.A. degree in mathematics and a Doctor of Philosophy degree in Computer Science from UCLA in 2001. [6] [7] From the early 2000s through 2018 he was a professor at University of Michigan, [1] where he supervised doctoral dissertations and degrees of 12 students in Electrical engineering and Computer science. [7] He worked as a principal engineer at Synopsys during a sabbatical leave. [8] [9] In 2013-2014 he was a visiting professor at Stanford University. [10] Markov worked at Google on Search and Information Retrieval, [11] and at Meta on Machine Learning platforms. [12] [13] [14] As of 2024, he works at Synopsys. [15]

Markov is a member of the Board of Directors of Nova Ukraine, a California 501(c)(3) charity organization that provides humanitarian aid in Ukraine. [16] At Nova Ukraine, Markov leads government and media relations, as well as advocacy efforts. Markov curated publicity efforts, established and curated large medical and evacuation projects, and contributed to fundraising.

Markov is 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. [17]

Awards and distinctions

ACM Special Interest Group on Design Automation honored Markov with an Outstanding New Faculty Award in 2004. [18]

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." [19] [20] Markov became ACM Distinguished Scientist in 2011. [21] [22] In 2013 he was named an IEEE fellow [23] "for contributions to optimization methods in electronic design automation". [24]

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:

Books and other publications

Markov co-authored over 200 peer-reviewed publications in journals and archival conference proceedings, and Google Scholar reported over 19,000 citations of his publications as of October 2023.

In a 2014 Nature article, [37] Markov surveyed known limits to computation, pointing out that many of them are fairly lose and do not restrict near-term technologies. When practical technologies encounter serious limits, understanding these limits can lead to workarounds. More often, what is practically achievable depends on technology-specific engineering limitations.

In 2024, Markov published a paper in Communications of the ACM critical of a prior Nature publication on chip design. [38] [39]

Markov co-edited the two-volume Electronic Design Automation handbook published in second edition by Taylor & Francis in 2016. [40] He also co-authored five scholarly books published by Springer, among them are two textbooks:

Markov's other books cover uncertainty in logic circuits, [44] dealing with functional design errors in digital circuits, [45] and physical synthesis of integrated circuits. [46]

Key technical contributions

Quantum computing

Markov’s contributions include results on quantum circuit synthesis (creating circuits from specifications) and simulation of quantum circuits on conventional computers (obtaining the output of a quantum computer without a quantum computer).

Physical design of integrated circuits

Markov's Capo placer [55] provided a baseline for comparisons used in the placement literature. The placer was commercialized and used to design industry chips. [56] Markov's contributions include algorithms, methodologies and software for

Machine learning

Markov led the development of an end-to-end AI platform called Looper, which supports the full machine learning lifecycle from model training, deployment, and inference all the way to evaluation and tuning of products. Looper provides easy-to-use APIs for optimization, personalization, and feedback collection. [12] [61] [62]

Activity on social media

Markov was awarded a Top Writer status on Quora in 2018, 2017, 2016, 2015 and 2014, he has over 80,000 followers. His contributions were republished by Huffington Post , Slate , and Forbes . [63]

Markov is a moderator for the cs.ET (Emerging Technologies in Computing and Communications) subject area on arXiv.

Notes

  1. Ukrainian: Ігор Леонідович Марков, romanized: Ihor Leonidovych Markov

Related Research Articles

<span class="mw-page-title-main">Quantum computing</span> Computer hardware technology that uses quantum mechanics

A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. Theoretically a large-scale quantum computer could break some widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.

Shor's algorithm is a quantum algorithm for finding the prime factors of an integer. It was developed in 1994 by the American mathematician Peter Shor. It is one of the few known quantum algorithms with compelling potential applications and strong evidence of superpolynomial speedup compared to best known classical (non-quantum) algorithms. On the other hand, factoring numbers of practical significance requires far more qubits than available in the near future. Another concern is that noise in quantum circuits may undermine results, requiring additional qubits for quantum error correction.

In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. A classical algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. Although all classical algorithms can also be performed on a quantum computer, the term quantum algorithm is generally reserved for algorithms that seem inherently quantum, or use some essential feature of quantum computation such as quantum superposition or quantum entanglement.

Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment. Quantum circuit algorithms can be implemented on integrated circuits, conducted with instrumentation, or written in a programming language for use with a quantum computer or a quantum processor.

Placement is an essential step in electronic design automation — the portion of the physical design flow that assigns exact locations for various circuit components within the chip's core area. An inferior placement assignment will not only affect the chip's performance but might also make it non-manufacturable by producing excessive wire-length, which is beyond available routing resources. Consequently, a placer must perform the assignment while optimizing a number of objectives to ensure that a circuit meets its performance demands. Together, the placement and routing steps of IC design are known as place and route.

Jingsheng Jason Cong is a Chinese-born American computer scientist, educator, and serial entrepreneur. He received his B.S. degree in computer science from Peking University in 1985, his M.S. and Ph.D. degrees in computer science from the University of Illinois at Urbana-Champaign in 1987 and 1990, respectively. He has been on the faculty in the Computer Science Department at the University of California, Los Angeles (UCLA) since 1990. Currently, he is a Distinguished Chancellor's Professor and the director of Center for Domain-Specific Computing (CDSC).

Giovanni De Micheli is a research scientist in electronics and computer science. He is credited for the invention of the Network on a Chip design automation paradigm and for the creation of algorithms and design tools for Electronic Design Automation (EDA). He is Professor and Director of the Integrated Systems laboratory at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. Previously, he was Professor of Electrical Engineering at Stanford University. He was Director of the Electrical Engineering Institute at EPFL from 2008 to 2019 and program leader of the Swiss Federal Nano-Tera.ch program. He holds a Nuclear Engineer degree, a M.S. and a Ph.D. degree in Electrical Engineering and Computer Science under Alberto Sangiovanni-Vincentelli.

<span class="mw-page-title-main">Floorplan (microelectronics)</span> Layout of major electronic circuit blocks

In electronic design automation, a floorplan of an integrated circuit is a schematic representation of tentative placement of its major functional blocks.

In quantum computing, the threshold theorem states that a quantum computer with a physical error rate below a certain threshold can, through application of quantum error correction schemes, suppress the logical error rate to arbitrarily low levels. This shows that quantum computers can be made fault-tolerant, as an analogue to von Neumann's threshold theorem for classical computation. This result was proven by the groups of Dorit Aharanov and Michael Ben-Or; Emanuel Knill, Raymond Laflamme, and Wojciech Zurek; and Alexei Kitaev independently. These results built on a paper of Peter Shor, which proved a weaker version of the threshold theorem.

John Patrick Hayes is an Irish-American computer scientist and electrical engineer, the Claude E. Shannon Chair of Engineering Science at the University of Michigan. He supervised over 35 doctoral students, coauthored seven books and over 340 peer-reviewed publications. His Erdös number is 2.

<span class="mw-page-title-main">Massoud Pedram</span> Iranian American computer engineer

Massoud Pedram is an Iranian American computer engineer noted for his research in green computing, energy storage systems, low-power electronics and design, electronic design automation and quantum computing. In the early 1990s, Pedram pioneered an approach to designing VLSI circuits that considered physical effects during logic synthesis. He named this approach layout-driven logic synthesis, which was subsequently called physical synthesis and incorporated into the standard EDA design flows. Pedram's early work on this subject became a significant prior art reference in a litigation between Synopsys Inc. and Magma Design Automation.

<span class="mw-page-title-main">Quantum machine learning</span> Interdisciplinary research area at the intersection of quantum physics and machine learning

Quantum machine learning is the integration of quantum algorithms within machine learning programs.

Andrew MacGregor Childs is an American computer scientist and physicist known for his work on quantum computing. He is currently a professor in the department of computer science and Institute for Advanced Computer Studies at the University of Maryland. He also co-directs the Joint Center for Quantum Information and Computer Science, a partnership between the University of Maryland and the National Institute of Standards and Technology.

Approximate computing is an emerging paradigm for energy-efficient and/or high-performance design. It includes a plethora of computation techniques that return a possibly inaccurate result rather than a guaranteed accurate result, and that can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provide disproportionate gains in performance and energy, while still achieving acceptable result accuracy. For example, in k-means clustering algorithm, allowing only 5% loss in classification accuracy can provide 50 times energy saving compared to the fully accurate classification.

<span class="mw-page-title-main">David Atienza</span> Spanish physicist and materials scientist

David Atienza Alonso is a Spanish/Swiss scientist in the disciplines of computer and electrical engineering. His research focuses on hardware‐software co‐design and management for energy‐efficient and thermal-aware computing systems, always starting from a system‐level perspective to the actual electronic design. He is a full professor of electrical and computer engineering at the Swiss Federal Institute of Technology in Lausanne (EPFL) and the head of the Embedded Systems Laboratory (ESL). He is an IEEE Fellow (2016), and an ACM Fellow (2022).

In quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. The term was coined by John Preskill in 2012, but the concept dates to Yuri Manin's 1980 and Richard Feynman's 1981 proposals of quantum computing.

Lawrence Pileggi is the Coraluppi Head and Tanoto Professor of Electrical and Computer Engineering at Carnegie Mellon University. He is a specialist in the automation of integrated circuits, and developing software tools for the optimization of power grids. Pileggi's research has been cited thousands of times in engineering papers.

This glossary of quantum computing is a list of definitions of terms and concepts used in quantum computing, its sub-disciplines, and related fields.

<span class="mw-page-title-main">Dmitri Maslov</span> Computer scientist

Dmitri Maslov is a Canadian-American computer scientist known for his work on quantum circuit synthesis and optimization, quantum advantage, and benchmarking quantum computers. Currently, he is the Chief Software Architect at IBM Quantum. Maslov was formerly a program director for Quantum Information Science at the National Science Foundation. He was named a Fellow of the Institute of Electrical and Electronics Engineers in 2021 "for contributions to quantum circuit synthesis and optimization, and compiling for quantum computers."

Vivek Vijay Shende is an American mathematician known for his work on algebraic geometry, symplectic geometry and quantum computing. He is a professor of Quantum Mathematics at Syddansk Universitet while on leave from University of California Berkeley.

References

  1. 1 2 "Prof. Igor Markov". University of Michigan, Computer Science and Engineering. Retrieved August 6, 2023.
  2. "Nova Ukraine: Supporting Ukraine in Crisis and Beyond". National Philanthropic Trust. March 30, 2022.
  3. "Civilians Evacuated from Mariupol". CNN Newsroom Transcripts. May 2, 2022.
  4. "Nova Ukraine has raised $30M to help with relief in #Ukraine since #Russia's invasion (video)". Twitter. First Move CNN. May 11, 2022.
  5. "Nova Ukraine Delivers More Than $50 Million of Aid to Ukraine in 2022". PR Newswire. 2022. Retrieved December 19, 2022.
  6. "Igor Leonidovich Markov". Mathematics Genealogy Project. Retrieved August 11, 2023.
  7. 1 2 "Igor Markov: IEEE Xplore author profile". IEEE Xplore. Retrieved October 8, 2023.
  8. US8141024B2,Markov, Igor L.&McElvain, Kenneth S.,"Temporally-assisted resource sharing in electronic systems",issued 2012-03-20
  9. US9285796B2,Markov, Igor L.&McElvain, Kenneth S.,"Approximate functional matching in electronic systems",issued 2016-03-15
  10. "Visiting Professor: Igor Markov". Stanford Electrical Engineering. Retrieved August 11, 2023.
  11. "Patent US 10,235,432 "Document retrieval using multiple sort orders"". Google Patents. Retrieved August 11, 2023.
  12. 1 2 "Inside Meta's AI optimization platform for engineers across the company". Facebook. Retrieved August 11, 2023.
  13. VanBilliard, Jefferson (2023-07-26). "Igor Markov". The AI Conference. Retrieved 2023-10-05.
  14. Kasturi, Nitya; Markov, Igor L. (2022-02-11). "Text Ranking and Classification using Data Compression". I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021. PMLR: 48–53. arXiv: 2109.11577 .
  15. "Mona Knutsen on LinkedIn: #genairevolution #welcometothefuture #innovationleader #legend". www.linkedin.com. Retrieved 2024-03-27.
  16. "Nova Ukraine Board of Directors". Nova Ukraine. 18 April 2022. Retrieved August 12, 2023.
  17. "Board of Directors – American Coalition for Ukraine" . Retrieved 2024-06-23.
  18. "Outstanding New Faculty Award". ACM SIGDA. 18 June 2019. Retrieved October 8, 2023.
  19. "IEEE CEDA Ernest S. Kuh Early Career Award". IEEE Council on Electronic Design Automation. Retrieved August 7, 2023.
  20. "IEEE Council on EDA Honors Igor Markov with Early Career Award" (PDF). Business Wire . Retrieved October 3, 2023.
  21. "ACM Names 54 Distinguished Members for Contributions to Computing". ACM. December 15, 2011.
  22. "Igor Markov Named ACM Distinguished Scientist". University of Michigan, Computer Science and Engineering. December 15, 2011.
  23. "Igor Markov | IEEE CASS". ieee-cas.org. Retrieved 2023-10-05.
  24. "Fellows directory". IEEE. Retrieved August 6, 2023.
  25. Vivek V. Shende; Aditya K. Prasad; Igor L. Markov; John P. Hayes (2003). "Synthesis of reversible logic circuits". IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 22 (6): 710–722. doi:10.1109/TCAD.2003.811448.
  26. "IEEE Transactions on Computer-Aided Design Donald O. Pederson Best Paper Award | IEEE Council on Electronic Design Automation". ieee-ceda.org. Retrieved 2023-08-12.
  27. Smita Krishnaswamy; George F. Viamontes; Igor L. Markov; John P. Hayes (2005). "Accurate Reliability Evaluation and Enhancement via Probabilistic Transfer Matrices". Proceedings of Design Automation and Test in Europe (DATE). 2005: 282–287.
  28. "Best Paper Awards DATE 2006" (PDF). Retrieved August 12, 2023.
  29. Smita Krishnaswamy; George F. Viamontes; Igor L. Markov; John P. Hayes (2008). "Probabilistic transfer matrices in symbolic reliability analysis of logic circuits". ACM Transations on Design Automation of Electronic Systems. 13 (1): 8:1–8:35.
  30. 1 2 Stephen Plaza; Igor L. Markov; Valeria Bertacco (2008). "Optimizing non-monotonic interconnect using functional simulation and logic restructuring". Proceedings of International Symposium on Physical Design (ISPD). 2008: 95–102.
  31. "Best Paper Awards International Symposium on Physical Design (ISPD) 2008" . Retrieved October 26, 2023.
  32. Myung-Chul Kim; Dongjin Lee; Igor L. Markov (2010). "SimPL: An effective placement algorithm". Proceedings of International Conference on Computer-Aided Design (ICCAD). 2010: 649–656.
  33. "Best Paper Awards IEEE/ACM International Conference on Computer-Aided Design (ICCAD) 2010" . Retrieved October 26, 2023.
  34. 1 2 Myung-Chul Kim; Dongjin Lee; Igor L. Markov (2012). "SimPL: An Effective Placement Algorithm". IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems . 31 (1): 50–60. doi:10.1109/TCAD.2011.2170567. S2CID   47293399.
  35. Hadi Katebi; Karem A. Sakallah; Igor L. Markov (2012). "Graph Symmetry Detection and Canonical Labeling: Differences and Synergies". Turing-100. Easy Chair. ISBN   9781782310006.
  36. "Computer Scientists Win Best Paper Award at Turing Centenary Conference". Computer Science and Engineering. Retrieved 2023-08-13.
  37. Markov, Igor (2014). "Limits on Fundamental Limits to Computation". Nature . 512 (7513): 147–154. arXiv: 1408.3821 . Bibcode:2014Natur.512..147M. doi:10.1038/nature13570. PMID   25119233. S2CID   4458968.
  38. Markov, Igor L. (23 October 2024). "Reevaluating Google's Reinforcement Learning for IC Macro Placement". Communications of the ACM. 67 (11): 60–71. doi:10.1145/3676845 . Retrieved 2024-11-10.
  39. "November 2024 CACM: Reevaluating Google's Reinforcement Learning for IC Macro Placement". www.youtube.com. 28 October 2024. Retrieved 2024-11-05.
  40. Luciano Lavagno; Igor L. Markov; Grant Martin; Louis K. Scheffer, eds. (2016). Electronic Design Automation for IC System Design, Verification, and Testing; 2nd ed. Taylor & Francis. p. 664. ISBN   9781138586000.
  41. George F. Viamontes; Igor L. Markov; John P. Hayes (2009). Quantum Circuit Simulation. Springer. p. 200. ISBN   978-90-481-3064-1.
  42. Andrew B. Kahng; Jens Lienig; Igor L. Markov; Jin Hu (2011). VLSI Physical Design - From Graph Partitioning to Timing Closure. Springer. pp. 1–310. ISBN   978-90-481-9590-9.
  43. Andrew B. Kahng; Jens Lienig; Igor L. Markov; Jin Hu (2022). VLSI Physical Design - From Graph Partitioning to Timing Closure, 2nd ed. Springer. pp. 1–317. ISBN   978-3-030-96415-3.
  44. Smita Krishnaswamy; Igor L. Markov; John P. Hayes (21 September 2012). Design, Analysis and Test of Logic Circuits Under Uncertainty. Springer. ISBN   978-90-481-9643-2.
  45. Kai-hui Chang; Valeria Bertacco; Igor L. Markov (2009). Functional Design Errors in Digital Circuits - Diagnosis, Correction and Repair. Lecture Notes in Electrical Engineering. Vol. 32. Springer. p. 185. ISBN   978-1-4020-9364-7.
  46. David A. Papa; Igor L. Markov (2013). Multi-Objective Optimization in Physical Synthesis of Integrated Circuits. Lecture Notes in Electrical Engineering. Vol. 166. Springer. p. 155. ISBN   978-1-4614-1355-4.
  47. K. N. Patel; I. L. Markov; J. P. Hayes (2008). "Efficient Synthesis of Linear Reversible Circuits". Quantum Information and Computation. 8 (3–4): 282–294. arXiv: quant-ph/0302002 . doi:10.26421/QIC8.3-4-4.
  48. Aaronson, Scott; Gottesman, Daniel (2004). "Improved Simulation of Stabilizer Circuits". Phys. Rev. A. 70 (5): 052328. arXiv: quant-ph/0406196 . Bibcode:2004PhRvA..70e2328A. doi:10.1103/PhysRevA.70.052328. S2CID   5289248.
  49. 1 2 Shende, Vivek V.; Bullock, Stephen S.; Markov, Igor L. (2006). "Synthesis of quantum logic circuits". IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems . 25 (6): 1000–1010. arXiv: quant-ph/0406176 . doi:10.1109/TCAD.2005.855930. S2CID   265038781.
  50. Shende, Vivek V.; Markov, Igor L.; Bullock, Stephen S. (2004-06-30). "Minimal universal two-qubit controlled-NOT-based circuits". Physical Review A. 69 (6): 062321. arXiv: quant-ph/0308033 . Bibcode:2004PhRvA..69f2321S. doi:10.1103/PhysRevA.69.062321. S2CID   119489186.
  51. Araujo, Israel F.; Park, Daniel K.; Petruccione, Francesco; da Silva, Adenilton J. (2021-03-18). "A divide-and-conquer algorithm for quantum state preparation". Scientific Reports. 11 (1): 6329. doi: 10.1038/s41598-021-85474-1 . ISSN   2045-2322. PMC   7973527 . PMID   33737544.
  52. Markov, Igor L.; Shi, Yaoyun (January 2008). "Simulating Quantum Computation by Contracting Tensor Networks". SIAM Journal on Computing. 38 (3): 963–981. arXiv: quant-ph/0511069 . doi:10.1137/050644756. ISSN   0097-5397. S2CID   3187832.
  53. 1 2 Aharonov, Dorit; Landau, Zeph; Makowsky, Johann (2006). "The quantum FFT can be classically simulated". arXiv: quant-ph/0611156 .
  54. Yoran, Nadav; Short, Anthony J. (2007-10-16). "Efficient classical simulation of the approximate quantum Fourier transform". Physical Review A. 76 (4): 042321. arXiv: quant-ph/0611241 . Bibcode:2007PhRvA..76d2321Y. doi:10.1103/PhysRevA.76.042321. S2CID   119444986.
  55. 1 2 Andrew E. Caldwell; Andrew B. Kahng; Igor L. Markov (2000). "Can recursive bisection alone produce routable placements?". Proceedings of the 37th conference on Design automation - DAC '00. Vol. 2000. pp. 477–482. doi:10.1145/337292.337549. ISBN   1581131879. S2CID   4926321.
  56. "IEEE Council on EDA Honors Igor Markov with Early Career Award". www.chipestimate.com. Retrieved 2023-10-03.
  57. Andrew E. Caldwell; Andrew B. Kahng; Igor L. Markov (2000). "Optimal partitioners and end-case placers for standard-cell layout". IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 19 (11): 1304–1313. doi:10.1109/43.892854.
  58. Caldwell, Andrew E.; Kahng, Andrew B.; Markov, Igor L. (2001-12-31). "Design and implementation of move-based heuristics for VLSI hypergraph partitioning". ACM Journal of Experimental Algorithmics. 5: 5–es. doi:10.1145/351827.384247. ISSN   1084-6654. S2CID   2074760.
  59. Saurabh N. Adya; Igor L. Markov (2003). "Fixed-outline floorplanning: enabling hierarchical design". IEEE Trans. Very Large Scale Integr. Syst. 11 (6): 1120–1135. doi:10.1109/TVLSI.2003.817546.
  60. Jarrod A. Roy; Igor L. Markov (2008). "High-performance routing at the nanometer scale". IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 27 (6): 1066–1077. doi:10.1109/ICCAD.2007.4397313. S2CID   61607526.
  61. Markov, Igor L.; Wang, Hanson; Kasturi, Nitya S.; Singh, Shaun; Garrard, Mia R.; Huang, Yin; Yuen, Sze Wai Celeste; Tran, Sarah; Wang, Zehui; Glotov, Igor; Gupta, Tanvi; Chen, Peng; Huang, Boshuang; Xie, Xiaowen; Belkin, Michael (2022-08-14). "Looper: An End-to-End ML Platform for Product Decisions". Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD '22. New York, NY, USA: Association for Computing Machinery. pp. 3513–3523. arXiv: 2110.07554 . doi:10.1145/3534678.3539059. ISBN   978-1-4503-9385-0.
  62. Looper: An End-to-End ML Platform for Product Decisions - Igor Markov | Stanford MLSys #60.
  63. "Igor Markov's profile". Quora. Retrieved October 8, 2023.