Jinseok Lee

818 total citations
10 papers, 651 citations indexed

About

Jinseok Lee is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, Jinseok Lee has authored 10 papers receiving a total of 651 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Electrical and Electronic Engineering, 4 papers in Biomedical Engineering and 2 papers in Cellular and Molecular Neuroscience. Recurrent topics in Jinseok Lee's work include Advanced Memory and Neural Computing (3 papers), Ferroelectric and Negative Capacitance Devices (2 papers) and Analog and Mixed-Signal Circuit Design (2 papers). Jinseok Lee is often cited by papers focused on Advanced Memory and Neural Computing (3 papers), Ferroelectric and Negative Capacitance Devices (2 papers) and Analog and Mixed-Signal Circuit Design (2 papers). Jinseok Lee collaborates with scholars based in South Korea and United States. Jinseok Lee's co-authors include Choong Kim, Hyeongdo Choi, Se Hwan Lim, Byung Jin Cho, Yong Jun Kim, Gyu Soup Lee, Naveen Verma, Rakshit Pathak, Hossein Valavi and Yinqi Tang and has published in prestigious journals such as ACS Energy Letters, IEEE Journal of Solid-State Circuits and IEEE Transactions on Circuits and Systems I Regular Papers.

In The Last Decade

Jinseok Lee

9 papers receiving 635 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jinseok Lee South Korea	7	369	270	199	83	83	10	651
Kensuke Kanda Japan	14	619 1.7×	386 1.4×	107 0.5×	29 0.3×	209 2.5×	119	919
Mohammad Alhawari United Arab Emirates	14	477 1.3×	244 0.9×	107 0.5×	20 0.2×	268 3.2×	53	658
Lun Wang China	11	312 0.8×	61 0.2×	175 0.9×	17 0.2×	82 1.0×	46	487
Qingying Ren China	11	351 1.0×	170 0.6×	107 0.5×	54 0.7×	104 1.3×	55	594
Namyun Kim South Korea	13	243 0.7×	355 1.3×	37 0.2×	11 0.1×	69 0.8×	24	618
Suat U. Ay United States	12	591 1.6×	282 1.0×	68 0.3×	24 0.3×	112 1.3×	58	742
Ziyun Li United States	16	460 1.2×	161 0.6×	81 0.4×	15 0.2×	43 0.5×	55	729
Mingkun Xu China	14	336 0.9×	75 0.3×	113 0.6×	50 0.6×	45 0.5×	36	639
Cheng Chi China	9	110 0.3×	268 1.0×	108 0.5×	9 0.1×	41 0.5×	31	690
Di Fu China	12	212 0.6×	153 0.6×	156 0.8×	72 0.9×	44 0.5×	19	497

Countries citing papers authored by Jinseok Lee

Since Specialization

Citations

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

Fields of papers citing papers by Jinseok Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinseok Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Jinseok Lee. A scholar is included among the top collaborators of Jinseok Lee 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 Jinseok Lee. Jinseok Lee 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:

10 of 10 papers shown

1.

Lee, Jinseok, et al.. (2024). Training Neural Networks With In-Memory-Computing Hardware and Multi-Level Radix-4 Inputs. IEEE Transactions on Circuits and Systems I Regular Papers. 71(4). 1781–1793. 1 indexed citations

2.

Kang, Youn‐Bae & Jinseok Lee. (2023). Mechanism of Initial Nozzle Clog Deposit Growth and Its Countermeasure during Continuous Casting of Ti-Added Ultralow-C Steel.

3.

Jia, Hongyang, Yinqi Tang, Hossein Valavi, et al.. (2021). 15.1 A Programmable Neural-Network Inference Accelerator Based on Scalable In-Memory Computing. 236–238. 117 indexed citations

4.

Jia, Hongyang, Yinqi Tang, Hossein Valavi, et al.. (2021). Scalable and Programmable Neural Network Inference Accelerator Based on In-Memory Computing. IEEE Journal of Solid-State Circuits. 57(1). 198–211. 61 indexed citations

5.

Lee, Joon-Hyun, et al.. (2021). Mathematical Modeling and Simulation of a Two-stage Reciprocating Air Compressor Considering Heat Transfer Effect. 2021 21st International Conference on Control, Automation and Systems (ICCAS). 2240–2242. 2 indexed citations

6.

Lee, Jinseok, et al.. (2018). An Ultra-High Input Impedance Analog Front End Using Self-Calibrated Positive Feedback. IEEE Journal of Solid-State Circuits. 53(8). 2252–2262. 50 indexed citations

7.

Kim, Choong, Jinseok Lee, Gyu Soup Lee, et al.. (2018). Self-Powered Wearable Electrocardiography Using a Wearable Thermoelectric Power Generator. ACS Energy Letters. 3(3). 501–507. 278 indexed citations

8.

Lee, Jinseok, et al.. (2018). A Low-Power Photoplethysmogram-Based Heart Rate Sensor Using Heartbeat Locked Loop. IEEE Transactions on Biomedical Circuits and Systems. 12(6). 1220–1229. 35 indexed citations

9.

Lee, Jinseok, et al.. (2017). A 255nW ultra-high input impedance analog front-end for non-contact ECG monitoring. 1–4. 12 indexed citations

10.

Lee, Hyunjin, Seong‐Wan Ryu, Kun-Rok Jeon, et al.. (2006). Sub-5nm All-Around Gate FinFET for Ultimate Scaling. 58–59. 95 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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