Rahul Dodhia

1.4k total citations
48 papers, 750 citations indexed

About

Rahul Dodhia is a scholar working on Ecology, Developmental Biology and Artificial Intelligence. According to data from OpenAlex, Rahul Dodhia has authored 48 papers receiving a total of 750 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Ecology, 8 papers in Developmental Biology and 8 papers in Artificial Intelligence. Recurrent topics in Rahul Dodhia's work include Marine animal studies overview (9 papers), Animal Vocal Communication and Behavior (8 papers) and Music and Audio Processing (4 papers). Rahul Dodhia is often cited by papers focused on Marine animal studies overview (9 papers), Animal Vocal Communication and Behavior (8 papers) and Music and Audio Processing (4 papers). Rahul Dodhia collaborates with scholars based in United States, United Kingdom and Canada. Rahul Dodhia's co-authors include Juan Lavista Ferres, Ming Zhong, R. Key Dismukes, Andrew Gelman, Marconi Campos‐Cerqueira, Jack LeBien, T. Mitchell Aide, Julian Velev, Janet Metcalfe and Mandy Keogh and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Rahul Dodhia

41 papers receiving 717 citations

Peers

Rahul Dodhia
Maximilian Eibl Germany
Oliver Boisseau United Kingdom
Libby Kinsey United Kingdom
Francesco Renna Portugal
Eric P. Kasten United States
Hongxiao Zhu United States
Yihui Xie China
Randal S. Olson United States
Romain François France
Joshua Garland United States
Maximilian Eibl Germany
Rahul Dodhia
Citations per year, relative to Rahul Dodhia Rahul Dodhia (= 1×) peers Maximilian Eibl

Countries citing papers authored by Rahul Dodhia

Since Specialization
Citations

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

Fields of papers citing papers by Rahul Dodhia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rahul Dodhia

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

All Works

20 of 20 papers shown
1.
Gholami, Shahrzad, Lea Scheppke, Meghana Kshirsagar, et al.. (2025). Enhanced Macular Telangiectasia Type 2 Detection: Leveraging Self-Supervised Learning and Ensemble Models. Ophthalmology Science. 5(4). 100710–100710. 2 indexed citations
2.
Oviedo, Felipe, Yixi Xu, Robert A. Vandermeulen, et al.. (2025). Cancer Detection in Breast MRI Screening via Explainable AI Anomaly Detection. Radiology. 316(1). e241629–e241629. 2 indexed citations
3.
Tadesse, Girmaw Abebe, Laura Ferguson, Caleb Robinson, et al.. (2025). Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators. PLoS ONE. 20(5). e0322959–e0322959.
4.
Miao, Zhongqi, Yuanhan Zhang, Sara Beery, et al.. (2025). New frontiers in artificial intelligence for biodiversity research and conservation with multimodal language models. Methods in Ecology and Evolution. 17(2). 238–256. 1 indexed citations
5.
Gholami, Shahrzad, Stephen J. Page, Rahul Dodhia, et al.. (2025). Bridging gaps in ophthalmology education through large language models. SHILAP Revista de lepidopterología. 2(4). 100166–100166. 1 indexed citations
6.
Miao, Zhongqi, Benjamin Elizalde, Soham Deshmukh, et al.. (2025). Multi-modal Language models in bioacoustics with zero-shot transfer: a case study. Scientific Reports. 15(1). 7242–7242. 3 indexed citations
7.
Gholami, Shahrzad, Stephen J. Page, Rahul Dodhia, et al.. (2025). Leveraging Large Language Models to Generate Multiple-Choice Questions for Ophthalmology Education. JAMA Ophthalmology. 143(11). 955–955.
8.
Oviedo, Felipe, Florent Tixier, Satomi Kawamoto, et al.. (2025). Benchmarking robustness of automated CT pancreas segmentation: achieving human-level reliability through human-in-the-loop optimization. PubMed. 2(6). umaf040–umaf040. 1 indexed citations
9.
Ferres, Juan Lavista, Felipe Oviedo, Caleb Robinson, et al.. (2024). Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst. Pancreatology. 24(7). 1182–1191. 3 indexed citations
10.
Sledzieski, Samuel, Meghana Kshirsagar, Minkyung Baek, et al.. (2024). Democratizing protein language models with parameter-efficient fine-tuning. Proceedings of the National Academy of Sciences. 121(26). e2405840121–e2405840121. 25 indexed citations
11.
Greene, Kevin, et al.. (2024). Current engagement with unreliable sites from web search driven by navigational search. Science Advances. 10(44). eadn3750–eadn3750.
12.
Robinson, Caleb, et al.. (2024). Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters. 3162–3172. 9 indexed citations
13.
Ferriss, Bridget E., et al.. (2023). A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA. Scientific Data. 10(1). 799–799. 1 indexed citations
14.
Fobi, Simone, M. Cardona, Elliott J. Collins, et al.. (2023). Poverty rate prediction using multi-modal survey and earth observation data. 23–29. 1 indexed citations
15.
Kiesecker, Joseph M., K. Nagaraju Shivaprakash, James R. Oakleaf, et al.. (2023). The Road to India’s Renewable Energy Transition Must Pass through Crowded Lands. Land. 12(11). 2049–2049. 1 indexed citations
16.
Xu, Yixi, Ivan S. Klyuzhin, Sara Harsini, et al.. (2023). Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss. Computers in Biology and Medicine. 158. 106882–106882. 20 indexed citations
17.
Habib, Al‐Rahim, Yixi Xu, William B. Weeks, et al.. (2023). Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy. Scientific Reports. 13(1). 5368–5368. 9 indexed citations
18.
Kshirsagar, Meghana, Md Nasir, Sumit Mukherjee, et al.. (2022). The Risk of Hospitalization and Mortality After Breakthrough SARS-CoV-2 Infection by Vaccine Type: Observational Study of Medical Claims Data. JMIR Public Health and Surveillance. 8(11). e38898–e38898. 6 indexed citations
19.
Robinson, Caleb, Marian Blazes, Anthony Ortiz, et al.. (2022). Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement. PLoS ONE. 17(10). e0274098–e0274098. 11 indexed citations
20.
Dodhia, Rahul, et al.. (2005). Is Pilots’ Visual Scanning Adequate to Avoid Mid-Air Collisions?. Journal of Bioresource Management. 141. 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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