Nilaksh Das

880 total citations
11 papers, 287 citations indexed

About

Nilaksh Das is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Nilaksh Das has authored 11 papers receiving a total of 287 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Nilaksh Das's work include Adversarial Robustness in Machine Learning (5 papers), Natural Language Processing Techniques (4 papers) and Anomaly Detection Techniques and Applications (4 papers). Nilaksh Das is often cited by papers focused on Adversarial Robustness in Machine Learning (5 papers), Natural Language Processing Techniques (4 papers) and Anomaly Detection Techniques and Applications (4 papers). Nilaksh Das collaborates with scholars based in United States. Nilaksh Das's co-authors include Duen Horng Chau, Fred Hohman, Haekyu Park, Zijie J. Wang, Omar Shaikh, Minsuk Kahng, Michael E. Kounavis, Li Chen, Shang-Tse Chen and Madhuri Shanbhogue and has published in prestigious journals such as IEEE Transactions on Visualization and Computer Graphics, Journal of Data and Information Quality and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Nilaksh Das

9 papers receiving 279 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nilaksh Das United States	5	155	105	31	26	14	11	287
Xiaohu Cheng China	6	187 1.2×	104 1.0×	16 0.5×	16 0.6×	20 1.4×	9	273
Sudhir Sawarkar India	9	115 0.7×	84 0.8×	30 1.0×	21 0.8×	17 1.2×	60	299
Hiroki Ohashi Japan	9	113 0.7×	167 1.6×	23 0.7×	39 1.5×	19 1.4×	22	285
Bharti Khemani India	4	92 0.6×	56 0.5×	13 0.4×	31 1.2×	19 1.4×	7	305
Fangyuan Lei China	9	104 0.7×	91 0.9×	20 0.6×	20 0.8×	31 2.2×	39	271
Hailun Xie China	6	118 0.8×	64 0.6×	29 0.9×	32 1.2×	10 0.7×	11	293
Yunwen Chen China	10	152 1.0×	106 1.0×	14 0.5×	27 1.0×	19 1.4×	24	305
Wushour Silamu China	8	113 0.7×	37 0.4×	38 1.2×	14 0.5×	6 0.4×	28	245
Sjoerd de Jong Netherlands	3	120 0.8×	61 0.6×	17 0.5×	19 0.7×	18 1.3×	5	253
Brian Quanz United States	8	168 1.1×	78 0.7×	42 1.4×	20 0.8×	12 0.9×	20	272

Countries citing papers authored by Nilaksh Das

Since Specialization

Citations

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

Fields of papers citing papers by Nilaksh Das

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nilaksh Das

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

11 of 11 papers shown

1.

Niu, Xing, Prashant Mathur, Srikanth Ronanki, et al.. (2025). Zero-resource Speech Translation and Recognition with LLMs. 1–5.

2.

Ronanki, Srikanth, et al.. (2024). SpeechGuard: Exploring the Adversarial Robustness of Multi-modal Large Language Models. 10018–10035.

3.

Das, Nilaksh, et al.. (2023). Mask the Bias: Improving Domain-Adaptive Generalization of CTC-Based ASR with Internal Language Model Estimation. 1–5. 1 indexed citations

4.

Park, Haekyu, Benjamin Hoover, Omar Shaikh, et al.. (2023). Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries. 2044–2054. 1 indexed citations

5.

Hull, Matthew S., Zijie J. Wang, Nilaksh Das, et al.. (2022). DetectorDetective: Investigating the Effects of Adversarial Examples on Object Detectors. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 21452–21459. 4 indexed citations

6.

Das, Nilaksh, et al.. (2022). Listen, Know and Spell: Knowledge-Infused Subword Modeling for Improving ASR Performance of OOV Named Entities. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 7887–7891. 3 indexed citations

7.

Das, Nilaksh, et al.. (2022). A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling. Journal of Data and Information Quality. 14(3). 1–23. 4 indexed citations

8.

Wang, Zijie J., Omar Shaikh, Haekyu Park, et al.. (2020). CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization. IEEE Transactions on Visualization and Computer Graphics. 27(2). 1396–1406. 179 indexed citations

9.

Das, Nilaksh, Madhuri Shanbhogue, Shang-Tse Chen, et al.. (2018). Compression to the Rescue: Defending from Adversarial Attacks Across Modalities. Knowledge Discovery and Data Mining. 7 indexed citations

10.

Das, Nilaksh, Madhuri Shanbhogue, Shang-Tse Chen, et al.. (2018). SHIELD. 196–204. 75 indexed citations

11.

Das, Nilaksh, et al.. (2016). PASSAGE. 84–87. 13 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.

Explore authors with similar magnitude of impact