Minjoo Hwang

473 total citations
10 papers, 291 citations indexed

About

Minjoo Hwang is a scholar working on Artificial Intelligence, Signal Processing and Biomedical Engineering. According to data from OpenAlex, Minjoo Hwang has authored 10 papers receiving a total of 291 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Signal Processing and 2 papers in Biomedical Engineering. Recurrent topics in Minjoo Hwang's work include Speech Recognition and Synthesis (7 papers), Music and Audio Processing (5 papers) and Speech and Audio Processing (4 papers). Minjoo Hwang is often cited by papers focused on Speech Recognition and Synthesis (7 papers), Music and Audio Processing (5 papers) and Speech and Audio Processing (4 papers). Minjoo Hwang collaborates with scholars based in United States, South Korea and Germany. Minjoo Hwang's co-authors include Xuedong Huang, F. Alleva, Hsiao-Wuen Hon, K.-F. Lee, Sanjoy Mahajan, R. Reddy, Jae Youn Hwang, Jihun Kim, Seong Jin Jo and Jae Eun Jang and has published in prestigious journals such as npj Digital Medicine, Biomedical Optics Express and Computer Speech & Language.

In The Last Decade

Minjoo Hwang

9 papers receiving 236 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Minjoo Hwang United States	8	235	186	45	9	8	10	291
H. S. Jayanna India	12	251 1.1×	282 1.5×	59 1.3×		7 0.9×	54	362
Laurent El Shafey Switzerland	7	96 0.4×	159 0.9×	110 2.4×		5 0.6×	21	233
Tomáš Kupka Norway	10	52 0.2×	67 0.4×	145 3.2×	3 0.3×	4 0.5×	28	245
Upendra V. Chaudhari United States	11	280 1.2×	206 1.1×	14 0.3×		5 0.6×	27	299
Jia-Hong Huang Netherlands	10	156 0.7×	67 0.4×	178 4.0×	5 0.6×	5 0.6×	19	299
B. Duc Switzerland	5	28 0.1×	123 0.7×	193 4.3×	2 0.2×	5 0.6×	9	244
Jake Grigsby United States	2	268 1.1×	66 0.4×	33 0.7×		2 0.3×	2	313
Amirsina Torfi United States	4	98 0.4×	50 0.3×	57 1.3×	1 0.1×	4 0.5×	8	169
Jiska Classen Germany	9	62 0.3×	47 0.3×	7 0.2×	3 0.3×	16 2.0×	24	272
María Rigaki Czechia	5	164 0.7×	91 0.5×	19 0.4×		2 0.3×	7	201

Countries citing papers authored by Minjoo Hwang

Since Specialization

Citations

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

Fields of papers citing papers by Minjoo Hwang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Minjoo Hwang

This figure shows the co-authorship network connecting the top 25 collaborators of Minjoo Hwang. A scholar is included among the top collaborators of Minjoo Hwang 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 Minjoo Hwang. Minjoo Hwang 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.

Hwang, Minjoo, et al.. (2025). Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality. npj Digital Medicine. 8(1). 612–612.

2.

Kim, Sewoong, Jihun Kim, Minjoo Hwang, et al.. (2019). Smartphone-based multispectral imaging and machine-learning based analysis for discrimination between seborrheic dermatitis and psoriasis on the scalp. Biomedical Optics Express. 10(2). 879–879. 33 indexed citations

3.

Kim, Sewoong, Minjoo Hwang, Jihun Kim, et al.. (2017). Multispectral imaging based on a Smartphone with an external C-MOS camera for detection of seborrheic dermatitis on the scalp. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10068. 100681S–100681S. 1 indexed citations

4.

Hoffmeister, Björn, Minjoo Hwang, Detang Lu, et al.. (2008). Recent improvements of the RWTH GALE Mandarin LVCSR system. 16 indexed citations

5.

Lee, K.-F., Hsiao-Wuen Hon, Minjoo Hwang, Sanjoy Mahajan, & R. Reddy. (2003). The SPHINX speech recognition system. International Conference on Acoustics, Speech, and Signal Processing. 445–448. 24 indexed citations

6.

Hwang, Minjoo, Xuedong Huang, & F. Alleva. (1993). Predicting unseen triphones with senones. IEEE International Conference on Acoustics Speech and Signal Processing. 311–314 vol.2. 69 indexed citations

7.

Huang, Xuedong, et al.. (1993). A comparative study of discrete, semicontinuous, and continuous hidden Markov models. Computer Speech & Language. 7(4). 359–368. 21 indexed citations

8.

Alleva, F., Xuedong Huang, & Minjoo Hwang. (1993). An improved search algorithm using incremental knowledge for continuous speech recognition. IEEE International Conference on Acoustics Speech and Signal Processing. 1. 307–310 vol.2. 37 indexed citations

9.

Hwang, Minjoo & Xuedong Huang. (1992). Subphonetic modeling with Markov states-Senone. 33–36 vol.1. 52 indexed citations

10.

Huang, Xuedong, et al.. (1991). Improved acoustic modeling with the SPHINX speech recognition system. 345–348 vol.1. 38 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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