Wasifur Rahman

1.0k total citations · 1 hit paper
13 papers, 480 citations indexed

About

Wasifur Rahman is a scholar working on Neurology, Social Psychology and Clinical Psychology. According to data from OpenAlex, Wasifur Rahman has authored 13 papers receiving a total of 480 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Neurology, 3 papers in Social Psychology and 3 papers in Clinical Psychology. Recurrent topics in Wasifur Rahman's work include Parkinson's Disease Mechanisms and Treatments (3 papers), Multimodal Machine Learning Applications (3 papers) and Emotion and Mood Recognition (2 papers). Wasifur Rahman is often cited by papers focused on Parkinson's Disease Mechanisms and Treatments (3 papers), Multimodal Machine Learning Applications (3 papers) and Emotion and Mood Recognition (2 papers). Wasifur Rahman collaborates with scholars based in United States, Bangladesh and Germany. Wasifur Rahman's co-authors include Ehsan Hoque, Louis–Philippe Morency, Sangwu Lee, Md. Kamrul Hasan, AmirAli Bagher Zadeh, Chengfeng Mao, Amir Zadeh, Rada Mihalcea, K. M. Azharul Hasan and Md. Saiful Islam and has published in prestigious journals such as npj Digital Medicine, IEEE Transactions on Affective Computing and Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies.

In The Last Decade

Wasifur Rahman

12 papers receiving 466 citations

Hit Papers

Integrating Multimodal Information in Large Pretrained Tr... 2020 2026 2022 2024 2020 100 200 300

Peers

Wasifur Rahman
Sangwu Lee United States
Suman Deb India
Zhaojiang Lin Hong Kong
Timo Baumann Germany
Sujoy Roy Singapore
Sangwu Lee United States
Wasifur Rahman
Citations per year, relative to Wasifur Rahman Wasifur Rahman (= 1×) peers Sangwu Lee

Countries citing papers authored by Wasifur Rahman

Since Specialization
Citations

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

Fields of papers citing papers by Wasifur Rahman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wasifur Rahman

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

All Works

13 of 13 papers shown
1.
Islam, Md. Saiful, Sangwu Lee, Wasifur Rahman, et al.. (2025). AI-Enabled Parkinson’s Disease Screening Using Smile Videos. NEJM AI. 2(7).
2.
Rahman, Wasifur, Md. Saiful Islam, Henry L. Paulson, et al.. (2023). Auto-Gait. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 7(1). 1–19. 5 indexed citations
3.
Rahman, Wasifur, et al.. (2023). A User-Centered Framework to Empower People with Parkinson's Disease. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 7(4). 1–29. 4 indexed citations
4.
Islam, Md. Saiful, Wasifur Rahman, Sangwu Lee, et al.. (2023). Using AI to measure Parkinson’s disease severity at home. npj Digital Medicine. 6(1). 156–156. 26 indexed citations
5.
6.
Rahman, Wasifur, et al.. (2021). DBATES: Dataset for Discerning Benefits of Audio, Textual, and Facial Expression Features in Competitive Debate Speeches. IEEE Transactions on Affective Computing. 14(2). 1028–1043. 3 indexed citations
8.
Lee, Sangwu, Wasifur Rahman, Amir Zadeh, et al.. (2021). Humor Knowledge Enriched Transformer for Understanding Multimodal Humor. Proceedings of the AAAI Conference on Artificial Intelligence. 35(14). 12972–12980. 45 indexed citations
9.
Rahman, Wasifur, Md. Kamrul Hasan, Sangwu Lee, et al.. (2020). Integrating Multimodal Information in Large Pretrained Transformers. PubMed. 2020. 2359–2369. 351 indexed citations breakdown →
10.
Shahid, Farhana, Wasifur Rahman, Mohammad Saifur Rahman, et al.. (2020). Leveraging Free-Hand Sketches for Potential Screening of PTSD. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 4(3). 1–22. 6 indexed citations
11.
Rahman, Wasifur, K. M. Azharul Hasan, Amir Zadeh, Louis–Philippe Morency, & Mohammed Ehsan Hoque. (2019). M-BERT: Injecting Multimodal Information in the BERT Structure. arXiv (Cornell University). 17 indexed citations
12.
Hasan, K. M. Azharul, et al.. (2019). Facial Expression Based Imagination Index and a Transfer Learning Approach to Detect Deception. 634–640. 6 indexed citations
13.
Shahid, Farhana, et al.. (2019). Two Tell-tale Perspectives of PTSD. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 3(3). 1–45. 11 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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