Nam Khanh Tran

586 total citations
15 papers, 328 citations indexed

About

Nam Khanh Tran is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nam Khanh Tran has authored 15 papers receiving a total of 328 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nam Khanh Tran's work include Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Web Data Mining and Analysis (3 papers). Nam Khanh Tran is often cited by papers focused on Topic Modeling (9 papers), Natural Language Processing Techniques (6 papers) and Web Data Mining and Analysis (3 papers). Nam Khanh Tran collaborates with scholars based in Germany, United States and Italy. Nam Khanh Tran's co-authors include Marco Baroni, Elia Bruni, Gemma Boleda, Claudia Niederée, Nattiya Kanhabua, Behnam Dezfouli, Yuhong Liu, Jie Ying Wu, Anton Deguet and Peter Kazanzides and has published in prestigious journals such as Sensors, Proceedings of the VLDB Endowment and Institutional Research Information System (Università degli Studi di Trento).

In The Last Decade

Nam Khanh Tran

15 papers receiving 306 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nam Khanh Tran Germany 7 259 66 46 18 17 15 328
Daniel Hládek Slovakia 9 194 0.7× 40 0.6× 47 1.0× 2 0.1× 30 1.8× 50 295
Qing He China 9 214 0.8× 46 0.7× 41 0.9× 2 0.1× 44 2.6× 41 306
Noris Mohd Norowi Malaysia 6 77 0.3× 60 0.9× 51 1.1× 5 0.3× 58 3.4× 47 222
Austin Matthews United States 5 355 1.4× 82 1.2× 30 0.7× 2 0.1× 9 0.5× 7 401
B. Premjith India 10 293 1.1× 58 0.9× 50 1.1× 2 0.1× 32 1.9× 39 387
Pratiksha Thaker United States 7 136 0.5× 79 1.2× 37 0.8× 8 0.4× 12 0.7× 15 299
Wieland Eckert Germany 12 807 3.1× 69 1.0× 43 0.9× 2 0.1× 41 2.4× 18 870
Joachim Bingel Denmark 12 376 1.5× 107 1.6× 21 0.5× 2 0.1× 12 0.7× 21 449
Eliyahu Kiperwasser Israel 4 320 1.2× 56 0.8× 37 0.8× 3 0.2× 7 0.4× 4 368
Annette Rios Switzerland 9 271 1.0× 129 2.0× 23 0.5× 2 0.1× 17 1.0× 24 395

Countries citing papers authored by Nam Khanh Tran

Since Specialization
Citations

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

Fields of papers citing papers by Nam Khanh Tran

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nam Khanh Tran. 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 Nam Khanh Tran. The network helps show where Nam Khanh Tran may publish in the future.

Co-authorship network of co-authors of Nam Khanh Tran

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

All Works

15 of 15 papers shown
1.
2.
Tran, Nam Khanh, et al.. (2020). PLATICA: Personalized Language Acquisition Training & Instruction Chatbot Assistant. Scholar Commons (Santa Clara University). 1 indexed citations
3.
Tran, Nam Khanh, Jie Ying Wu, Anton Deguet, & Peter Kazanzides. (2020). A Deep Learning Approach to Intrinsic Force Sensing on the da Vinci Surgical Robot. 18 indexed citations
4.
Tran, Nam Khanh, et al.. (2019). A Comprehensive Empirical Analysis of TLS Handshake and Record Layer on IoT Platforms. 61–70. 6 indexed citations
5.
Tran, Nam Khanh & Claudia Niederée. (2018). A Neural Network-based Framework for Non-factoid Question Answering. 1979–1983. 4 indexed citations
6.
Tran, Nam Khanh & Claudia Niederée. (2018). Multihop Attention Networks for Question Answer Matching. 325–334. 31 indexed citations
7.
Tran, Nam Khanh, et al.. (2018). Analyzing the Resource Utilization of AES Encryption on IoT Devices. 1200–1207. 24 indexed citations
8.
Tran, Nam Khanh & Weiwei Cheng. (2018). Multiplicative Tree-Structured Long Short-Term Memory Networks for Semantic Representations. 276–286. 5 indexed citations
9.
Nguyen, Dat Ba, Abdalghani Abujabal, Nam Khanh Tran, Martin Theobald, & Gerhard Weikum. (2017). Query-driven on-the-fly knowledge base construction. Proceedings of the VLDB Endowment. 11(1). 66–79. 19 indexed citations
10.
Tran, Nam Khanh, et al.. (2015). Time-travel Translator. 247–250. 6 indexed citations
11.
Tran, Nam Khanh, et al.. (2015). Back to the Past. 339–348. 16 indexed citations
12.
Tran, Nam Khanh. (2014). Time-aware topic-based contextualization. 15–20. 1 indexed citations
13.
Tran, Nam Khanh, et al.. (2014). Bridging temporal context gaps using time-aware re-contextualization. 1127–1130. 4 indexed citations
14.
Tran, Anh, et al.. (2013). A Pipeline Tweet Contextualization System at INEX 2013.. CLEF (Working Notes). 3 indexed citations
15.
Bruni, Elia, Gemma Boleda, Marco Baroni, & Nam Khanh Tran. (2012). Distributional Semantics in Technicolor. Institutional Research Information System (Università degli Studi di Trento). 136–145. 189 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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