Samuel Liu

1.8k total citations
65 papers, 1.3k citations indexed

About

Samuel Liu is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Samuel Liu has authored 65 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Electrical and Electronic Engineering, 15 papers in Atomic and Molecular Physics, and Optics and 15 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Samuel Liu's work include Advanced Memory and Neural Computing (18 papers), Magnetic properties of thin films (12 papers) and Magnetic Properties of Alloys (12 papers). Samuel Liu is often cited by papers focused on Advanced Memory and Neural Computing (18 papers), Magnetic properties of thin films (12 papers) and Magnetic Properties of Alloys (12 papers). Samuel Liu collaborates with scholars based in United States, Australia and China. Samuel Liu's co-authors include Jean Anne C. Incorvia, Perry B. Cregan, R. C. Frohberg, B. Moreno‐Sevilla, Asbjørn Fjeld, R. W. Stack, J. Mitchell Fetch, Qijian Song, Clarice J. Coyne and James A. Anderson and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nano Letters and ACS Nano.

In The Last Decade

Samuel Liu

60 papers receiving 1.3k citations

Peers

Samuel Liu

EEE

EOMM

AMPO

Rie Matsumoto Japan

Youngeun Kim South Korea

S. Suzuki Japan

Yasuyuki Watanabe Japan

Koichi Oka Japan

Karen Alim Germany

Naveen Shamsudhin Switzerland

Peter M. Johnson United States

Ken-ichi Matsuda Japan

Ali Hashmi United States

Rie Matsumoto Japan

Samuel Liu

37 ×0.1

441 ×1.3

EEE

27 ×0.1

295 ×1.1

EOMM

652 ×3.0

AMPO

Citations per year, relative to Samuel Liu Samuel Liu (= 1×) peers Rie Matsumoto

Countries citing papers authored by Samuel Liu

Since Specialization

Citations

This map shows the geographic impact of Samuel Liu'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 Samuel Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel Liu more than expected).

Fields of papers citing papers by Samuel Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel Liu. 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 Samuel Liu. The network helps show where Samuel Liu may publish in the future.

Co-authorship network of co-authors of Samuel Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Liu. A scholar is included among the top collaborators of Samuel Liu 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 Samuel Liu. Samuel Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Cardwell, Suma, J. Darby Smith, Samuel Liu, et al.. (2025). AI-Guided Codesign for Novel Computing Paradigms. 849–856.

Wang, Stephanie, et al.. (2025). ASO Author Reflections: Breast Cancer Among Asian American, Native Hawaiian, and Pacific Islander Patients. Annals of Surgical Oncology. 32(9). 6682–6684. 1 indexed citations

Wang, Stephanie, et al.. (2025). Disparities in Stage at Presentation for Disaggregated Asian American, Native Hawaiian, and Pacific Islander Patients with Breast Cancer. Annals of Surgical Oncology. 32(5). 3317–3330. 2 indexed citations

Liu, Samuel, J. Darby Smith, James B. Aimone, et al.. (2024). Magnetic tunnel junction random number generators applied to dynamically tuned probability trees driven by spin orbit torque. Nanotechnology. 35(27). 275204–275204. 5 indexed citations

Tsang, Art, et al.. (2024). Within- and out-of-school FL exposure and learning: An expectancy-value theory perspective on FL listening motivation. System. 122. 103264–103264. 8 indexed citations

Liu, Samuel, et al.. (2024). A cross-sectional exploration of cognitive ability across age via stacked ensembles.. Psychology and Aging. 40(2). 159–177.

Liu, Changyong, et al.. (2023). Coercivity modulation of FeCoCrMoTi films by artificial magnetic phase defects engineering based on multilayer structure. Acta Materialia. 259. 119241–119241. 5 indexed citations

Liu, Samuel & Jean Anne C. Incorvia. (2023). Creating stochastic neural networks with the help of probabilistic bits. Nature Electronics. 6(12). 935–936. 1 indexed citations

Leonard, Thomas M., et al.. (2023). Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks. Applied Physics Letters. 122(26). 18 indexed citations

10.

Liu, Samuel, Christopher H. Bennett, Sapan Agarwal, et al.. (2023). Parallel Matrix Multiplication Using Voltage-Controlled Magnetic Anisotropy Domain Wall Logic. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 9(1). 65–73. 7 indexed citations

11.

Hu, Xuan, Can Cui, Samuel Liu, et al.. (2023). Magnetic skyrmions and domain walls for logical and neuromorphic computing. SHILAP Revista de lepidopterología. 3(2). 22003–22003. 14 indexed citations

12.

Lu, Naiyan, et al.. (2023). Preventive Effect of Arctium lappa Polysaccharides on Acute Lung Injury through Anti-Inflammatory and Antioxidant Activities. Nutrients. 15(23). 4946–4946. 12 indexed citations

13.

Liu, Samuel, Suma Cardwell, Catherine D. Schuman, et al.. (2022). Random Bitstream Generation Using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions. IEEE Journal on Exploratory Solid-State Computational Devices and Circuits. 8(2). 194–202. 16 indexed citations

14.

Cardwell, Suma, Catherine D. Schuman, J. Darby Smith, et al.. (2022). Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation.. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information).

15.

Liu, Samuel, Christopher H. Bennett, Joseph S. Friedman, et al.. (2021). Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation. IEEE Magnetics Letters. 12. 1–5. 13 indexed citations

16.

Liu, Samuel, T. Patrick Xiao, Can Cui, et al.. (2021). A domain wall-magnetic tunnel junction artificial synapse with notched geometry for accurate and efficient training of deep neural networks. Applied Physics Letters. 118(20). 47 indexed citations

17.

Nam, Jin, P. Perera, Robert T. Gordon, et al.. (2015). Follistatin-like 3 is a mediator of exercise-driven bone formation and strengthening. Bone. 78. 62–70. 25 indexed citations

18.

Blanchard, Peter E. R., Samuel Liu, Qingdi Zhou, et al.. (2013). Structural and magnetic studies of the electron doped manganites Sr_0.65Pr_0.35−xCe_xMnO₃(0.00 ≤x≤ 0.35). Journal of Physics Condensed Matter. 25(33). 335401–335401. 4 indexed citations

19.

Liu, Samuel, et al.. (2006). Research and Development of Bulk Anisotropic Nanograin Composite Rare Earth Permanent Magnets. Journal of Iron and Steel Research International. 13. 123–135. 14 indexed citations

20.

Goldfarb‐Rumyantzev, Alexander S., Michael H. Schwenk, Samuel Liu, Chaim Charytan, & Bruce Spinowitz. (2003). Prediction of Single‐Pool Kt/V Based on Clinical and Hemodialysis Variables Using Multilinear Regression, Tree‐Based Modeling, and Artificial Neural Networks. Artificial Organs. 27(6). 544–554. 6 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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