Advait Madhavan

1.4k total citations
37 papers, 483 citations indexed

About

Advait Madhavan is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Cellular and Molecular Neuroscience. According to data from OpenAlex, Advait Madhavan has authored 37 papers receiving a total of 483 indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Electrical and Electronic Engineering, 13 papers in Artificial Intelligence and 7 papers in Cellular and Molecular Neuroscience. Recurrent topics in Advait Madhavan's work include Advanced Memory and Neural Computing (27 papers), Ferroelectric and Negative Capacitance Devices (12 papers) and Neuroscience and Neural Engineering (7 papers). Advait Madhavan is often cited by papers focused on Advanced Memory and Neural Computing (27 papers), Ferroelectric and Negative Capacitance Devices (12 papers) and Neuroscience and Neural Engineering (7 papers). Advait Madhavan collaborates with scholars based in United States, France and Japan. Advait Madhavan's co-authors include Dmitri B. Strukov, Timothy Sherwood, M. D. Stiles, Matthew W. Daniels, Philippe Talatchian, Brian D. Hoskins, Luke Theogarajan, Miguel Ángel Lastras-Montaño, Kwang‐Ting Cheng and Melika Payvand and has published in prestigious journals such as Nature Communications, Applied Physics Letters and Scientific Reports.

In The Last Decade

Advait Madhavan

34 papers receiving 471 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Advait Madhavan United States 12 383 144 85 73 50 37 483
Xiaoyong Xue China 15 587 1.5× 80 0.6× 51 0.6× 100 1.4× 35 0.7× 86 716
Wooseok Yi South Korea 8 429 1.1× 141 1.0× 96 1.1× 47 0.6× 29 0.6× 16 542
Soonwan Kwon South Korea 7 495 1.3× 114 0.8× 97 1.1× 57 0.8× 29 0.6× 12 570
Sungmeen Myung South Korea 6 463 1.2× 106 0.7× 94 1.1× 49 0.7× 28 0.6× 9 526
Qilin Zheng China 9 316 0.8× 96 0.7× 49 0.6× 39 0.5× 31 0.6× 41 437
Amogh Agrawal United States 15 673 1.8× 136 0.9× 54 0.6× 56 0.8× 60 1.2× 32 774
Mrigank Sharad United States 13 641 1.7× 186 1.3× 249 2.9× 58 0.8× 91 1.8× 57 731
Yoon-Jong Song South Korea 9 499 1.3× 93 0.6× 113 1.3× 45 0.6× 42 0.8× 13 588
Boyoung Seo South Korea 3 362 0.9× 90 0.6× 97 1.1× 44 0.6× 20 0.4× 4 418
Sourav Dutta United States 19 1.2k 3.2× 264 1.8× 166 2.0× 87 1.2× 31 0.6× 53 1.4k

Countries citing papers authored by Advait Madhavan

Since Specialization
Citations

This map shows the geographic impact of Advait Madhavan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Advait Madhavan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Advait Madhavan more than expected).

Fields of papers citing papers by Advait Madhavan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Advait Madhavan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Advait Madhavan. The network helps show where Advait Madhavan may publish in the future.

Co-authorship network of co-authors of Advait Madhavan

This figure shows the co-authorship network connecting the top 25 collaborators of Advait Madhavan. A scholar is included among the top collaborators of Advait Madhavan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Advait Madhavan. Advait Madhavan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hoskins, Brian D., William A. Borders, Advait Madhavan, et al.. (2025). Layer ensemble averaging for fault tolerance in memristive neural networks. Nature Communications. 16(1). 1250–1250. 4 indexed citations
2.
Borders, William A., et al.. (2025). Investigation of key performance metrics in TiOX/TiN based resistive random-access memory cells. Scientific Reports. 15(1). 23720–23720.
3.
Talatchian, Philippe, U. Ebels, Daniel P. Lathrop, et al.. (2024). Measurement-driven Langevin modeling of superparamagnetic tunnel junctions. Physical Review Applied. 22(1). 1 indexed citations
4.
Madhavan, Advait, et al.. (2024). Energy Efficient Convolutions with Temporal Arithmetic. 354–368.
5.
Prasad, Nitin, Lorena Anghel, Advait Madhavan, et al.. (2024). Unbiased random bitstream generation using injection-locked spin-torque nano-oscillators. Physical Review Applied. 21(3). 10 indexed citations
6.
Prasad, Nitin, Brian D. Hoskins, Matthew W. Daniels, et al.. (2021). Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions. arXiv (Cornell University). 9 indexed citations
7.
Tzimpragos, Georgios, Dilip Vasudevan, Nestan Tsiskaridze, et al.. (2021). Temporal Computing With Superconductors. IEEE Micro. 41(3). 71–79. 15 indexed citations
8.
Talatchian, Philippe, Matthew W. Daniels, Advait Madhavan, et al.. (2021). Mutual control of stochastic switching for two electrically coupled superparamagnetic tunnel junctions. Physical review. B.. 104(5). 15 indexed citations
9.
Zaslavsky, A., Curt A. Richter, Pragya R. Shrestha, et al.. (2021). Impact ionization-induced bistability in CMOS transistors at cryogenic temperatures for capacitorless memory applications. Applied Physics Letters. 119(4). 8 indexed citations
10.
Madhavan, Advait, Matthew W. Daniels, & M. D. Stiles. (2021). Temporal State Machines: Using Temporal Memory to Stitch Time-based Graph Computations. ACM Journal on Emerging Technologies in Computing Systems. 17(3). 1–27. 6 indexed citations
11.
Palmer, J., et al.. (2020). Intrinsic Reliability Study in Low-k Dielectrics with Co Metallurgy in 10nm Process. 124–126. 2 indexed citations
12.
Daniels, Matthew W., et al.. (2020). Energy-Efficient Stochastic Computing with Superparamagnetic Tunnel Junctions. Physical Review Applied. 13(3). 52 indexed citations
13.
Madhavan, Advait, Georgios Tzimpragos, M. D. Stiles, & Timothy Sherwood. (2019). A Truth-Matrix View into Unary Computing. 1 indexed citations
14.
Hoskins, Brian D., Matthew W. Daniels, Advait Madhavan, et al.. (2019). Streaming Batch Eigenupdates for Hardware Neural Networks. Frontiers in Neuroscience. 13. 793–793. 9 indexed citations
15.
Madhavan, Advait, Timothy Sherwood, & Dmitri B. Strukov. (2018). High-Throughput Pattern Matching With CMOL FPGA Circuits: Case for Logic-in-Memory Computing. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 26(12). 2759–2772. 16 indexed citations
16.
Griggio, Flavio, J. Palmer, Nikholas G. Toledo, et al.. (2018). Reliability of dual-damascene local interconnects featuring cobalt on 10 nm logic technology. 6E.3–1. 44 indexed citations
17.
Payvand, Melika, Miguel Ángel Lastras-Montaño, Amirali Ghofrani, et al.. (2015). Vertical integration of memristors onto foundry CMOS dies using wafer-scale integration. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 957–962. 11 indexed citations
18.
Madhavan, Advait, Timothy Sherwood, & Dmitri B. Strukov. (2014). Race logic. ACM SIGARCH Computer Architecture News. 42(3). 517–528. 42 indexed citations
19.
Madhavan, Advait & Dmitri B. Strukov. (2012). Mapping of image and network processing tasks on high-throughput CMOL FPGA circuits. 2. 82–87. 5 indexed citations
20.
Madhavan, Advait & Dmitri B. Strukov. (2012). Mapping of image and network processing tasks on high-throughput CMOL FPGA circuits. 2. 82–87. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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