Daby Sow

1.2k total citations
58 papers, 693 citations indexed

About

Daby Sow is a scholar working on Artificial Intelligence, Computer Networks and Communications and Signal Processing. According to data from OpenAlex, Daby Sow has authored 58 papers receiving a total of 693 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 17 papers in Computer Networks and Communications and 16 papers in Signal Processing. Recurrent topics in Daby Sow's work include Machine Learning in Healthcare (14 papers), Time Series Analysis and Forecasting (12 papers) and Healthcare Technology and Patient Monitoring (8 papers). Daby Sow is often cited by papers focused on Machine Learning in Healthcare (14 papers), Time Series Analysis and Forecasting (12 papers) and Healthcare Technology and Patient Monitoring (8 papers). Daby Sow collaborates with scholars based in United States, Canada and Israel. Daby Sow's co-authors include Maria Ebling, Jimeng Sun, John Davis, Jianying Hu, Shahram Ebadollahi, Marion Blount, Hui Lei, Guruduth Banavar, Carolyn McGregor and Andrew James and has published in prestigious journals such as IEEE Transactions on Information Theory, Archives of Disease in Childhood and Journal of the Neurological Sciences.

In The Last Decade

Daby Sow

54 papers receiving 646 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daby Sow United States 14 235 191 176 102 101 58 693
S. Pavlopoulos Greece 18 199 0.8× 524 2.7× 158 0.9× 113 1.1× 84 0.8× 69 1.3k
Edmon Begoli United States 13 282 1.2× 46 0.2× 105 0.6× 50 0.5× 38 0.4× 38 654
Mengshu Hou China 14 178 0.8× 115 0.6× 211 1.2× 28 0.3× 45 0.4× 80 653
Junaid Rashid Pakistan 15 347 1.5× 194 1.0× 80 0.5× 119 1.2× 22 0.2× 71 967
Zhicheng Cui United States 8 794 3.4× 271 1.4× 93 0.5× 54 0.5× 63 0.6× 13 1.2k
Smita Sharma India 17 142 0.6× 79 0.4× 162 0.9× 27 0.3× 43 0.4× 71 652
Samrat Kumar Dey Bangladesh 17 467 2.0× 164 0.9× 126 0.7× 173 1.7× 60 0.6× 51 1.0k
Amir H. Payberah Sweden 14 215 0.9× 142 0.7× 290 1.6× 91 0.9× 46 0.5× 46 665
M. Rubaiyat Hossain Mondal Bangladesh 18 518 2.2× 123 0.6× 109 0.6× 86 0.8× 54 0.5× 75 1.2k
Enrique Puertas Spain 12 359 1.5× 64 0.3× 152 0.9× 89 0.9× 88 0.9× 52 794

Countries citing papers authored by Daby Sow

Since Specialization
Citations

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

Fields of papers citing papers by Daby Sow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daby Sow

This figure shows the co-authorship network connecting the top 25 collaborators of Daby Sow. A scholar is included among the top collaborators of Daby Sow 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 Daby Sow. Daby Sow 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.
Bauer, Gregory H., Brett Bode, Saurabh Jha, et al.. (2025). Story of Two GPUs: Characterizing the Resilience of Hopper H100 and Ampere A100 GPUs. arXiv (Cornell University). 1145–1164.
2.
Yang, Xi, Saurabh W. Jha, Chandra Narayanaswami, et al.. (2024). SAM: Subseries Augmentation-Based Meta-Learning for Generalizing AIOps Models in Multi-Cloud Migration. 291–301. 1 indexed citations
3.
Dey, Sanjoy Kumer, Prithwish Chakraborty, Bum Chul Kwon, et al.. (2022). Human-centered explainability for life sciences, healthcare, and medical informatics. Patterns. 3(5). 100493–100493. 21 indexed citations
4.
Tang, Zefang, et al.. (2021). Disease network delineates the disease progression profile of cardiovascular diseases. Journal of Biomedical Informatics. 115. 103686–103686. 7 indexed citations
5.
Lu, Mingyu, et al.. (2020). Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Sepsis Treatment. arXiv (Cornell University). 1 indexed citations
6.
Bai, Yong, Daby Sow, Paul Vespa, & Xiao Hu. (2016). Real-Time Processing of Continuous Physiological Signals in a Neurocritical Care Unit on a Stream Data Analytics Platform. Acta neurochirurgica. Supplementum. 122. 75–80. 5 indexed citations
7.
Xu, Jie, Daby Sow, Deepak S. Turaga, & Mihaela van der Schaar. (2015). Online transfer learning for differential diagnosis determination. National Conference on Artificial Intelligence. 25–29. 3 indexed citations
8.
Qi, Guo-Jun, Charų C. Aggarwal, Deepak S. Turaga, Daby Sow, & Phil D. Anno. (2015). State-Driven Dynamic Sensor Selection and Prediction with State-Stacked Sparseness. Journal of International Crisis and Risk Communication Research. 945–954. 6 indexed citations
9.
Schmidt, J. Michael, Daby Sow, David J. Albers, et al.. (2014). Heart Rate Variability for Preclinical Detection of Secondary Complications After Subarachnoid Hemorrhage. Neurocritical Care. 20(3). 382–389. 30 indexed citations
10.
Sun, Jimeng, et al.. (2014). IBM’s Health Analytics and Clinical Decision Support. Yearbook of Medical Informatics. 23(1). 154–162. 54 indexed citations
11.
Beygelzimer, Alina, Anton Riabov, Daby Sow, Deepak S. Turaga, & Octavian Udrea. (2013). Big Data Exploration Via Automated Orchestration of Analytic Workflows.. 153–158. 1 indexed citations
12.
Sow, Daby, Jimeng Sun, Alain Biem, et al.. (2012). Real-time analysis for short-term prognosis in intensive care. IBM Journal of Research and Development. 56(5). 3:1–3:10. 6 indexed citations
13.
Sow, Daby, Alain Biem, Jimeng Sun, Jianying Hu, & Shahram Ebadollahi. (2010). Real-time prognosis of ICU physiological data streams. PubMed. 2010. 6785–6788. 8 indexed citations
14.
Blount, Marion, Maria Ebling, Johan Eklund, et al.. (2010). Real-Time Analysis for Intensive Care: Development and Deployment of the Artemis Analytic System. IEEE Engineering in Medicine and Biology Magazine. 29(2). 110–118. 92 indexed citations
15.
Blount, Marion, John Davis, Maria Ebling, et al.. (2007). Century: Automated Aspects of Patient Care. 504–509. 5 indexed citations
16.
Sow, Daby, David P. Olshefski, Mandis Beigi, & Guruduth Banavar. (2003). Prefetching based on web usage mining. 262–281. 7 indexed citations
17.
Davis, John, Daby Sow, Marion Blount, & Maria Ebling. (2003). Context Tailor: Towards a Programming Model for Context-Aware Computing.. 68–75. 3 indexed citations
18.
Sow, Daby & Alexandros Eleftheriadis. (2002). Representing information with computational resource bounds. 1. 452–456. 2 indexed citations
19.
Sow, Daby, Guruduth Banavar, John Davis, Jeremy B. Sussman, & Mugizi Robert Rwebangira. (2001). Preparing the Edge of the Network for Pervasive Content Delivery. Journal of the Neurological Sciences. 154(1). 101–5. 1 indexed citations
20.
Diop, Ibrahima, et al.. (2000). [Cri-du-chat syndrome. A case report].. PubMed. 45(1). 95–7.

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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