Sanjay Purushotham

4.8k total citations · 1 hit paper
50 papers, 2.4k citations indexed

About

Sanjay Purushotham is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Sanjay Purushotham has authored 50 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Sanjay Purushotham's work include Machine Learning in Healthcare (7 papers), Multimodal Machine Learning Applications (5 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Sanjay Purushotham is often cited by papers focused on Machine Learning in Healthcare (7 papers), Multimodal Machine Learning Applications (5 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Sanjay Purushotham collaborates with scholars based in United States, Japan and China. Sanjay Purushotham's co-authors include Yan Liu, Zhengping Che, David Sontag, Chuizheng Meng, C.‐C. Jay Kuo, Robinder G. Khemani, Koji Matsuo, Rachel S. Mandelbaum, Lynda D. Roman and Edward Raff and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and American Journal of Obstetrics and Gynecology.

In The Last Decade

Sanjay Purushotham

43 papers receiving 2.3k citations

Hit Papers

Recurrent Neural Networks for Multivariate Time Series wi... 2018 2026 2020 2023 2018 400 800 1.2k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Recurrent Neural Networks for Multivariate Time Series with Missing Values
    2018 1.2k citations Zhengping Che, Sanjay Purushotham et al. Scientific Reports profile →

Peers

Sanjay Purushotham
Zhengping Che China
Marco A. F. Pimentel United Kingdom
Miloš Hauskrecht United States
Jinsung Yoon United States
Gang Luo United States
David Sontag United States
Wei Luo China
Edward Choi United States
Tamer Abuhmed South Korea
Pedro Henriques Abreu Portugal
Zhengping Che China
Sanjay Purushotham
Citations per year, relative to Sanjay Purushotham Sanjay Purushotham (= 1×) peers Zhengping Che

Countries citing papers authored by Sanjay Purushotham

Since Specialization
Citations

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

Fields of papers citing papers by Sanjay Purushotham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sanjay Purushotham

This figure shows the co-authorship network connecting the top 25 collaborators of Sanjay Purushotham. A scholar is included among the top collaborators of Sanjay Purushotham 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 Sanjay Purushotham. Sanjay Purushotham 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.
2.
Purushotham, Sanjay, et al.. (2024). Communication-Efficient Pseudo Value-Based Random Forests for Federated Survival Analysis. Proceedings of the AAAI Symposium Series. 2(1). 458–466. 1 indexed citations
3.
Tama, Bayu Adhi, et al.. (2024). Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result. Proceedings of the AAAI Symposium Series. 2(1). 85–88.
4.
Zheng, Jianyu, Chenxi Wang, Sanjay Purushotham, et al.. (2024). Influence of cloud retrieval errors due to three-dimensional radiative effects on calculations of broadband shortwave cloud radiative effect. Atmospheric chemistry and physics. 24(5). 3093–3114. 2 indexed citations
5.
Ahmed, Masud, et al.. (2023). An Online Continuous Semantic Segmentation Framework With Minimal Labeling Efforts. Maryland Shared Open Access Repository (USMAI Consortium). 116–123. 1 indexed citations
6.
7.
Raff, Edward, et al.. (2023). When Visible-to-Thermal Facial GAN Beats Conditional Diffusion. 33. 181–185.
8.
Ahmed, Masud, et al.. (2022). GADAN: Generative Adversarial Domain Adaptation Network For Debris Detection Using Drone. Maryland Shared Open Access Repository (USMAI Consortium). 277–282. 2 indexed citations
9.
Raff, Edward, et al.. (2021). Intelligent Sight and Sound: A Chronic Cancer Facial Pain Dataset. Neural Information Processing Systems. 1 indexed citations
10.
Raff, Edward, et al.. (2020). The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data. arXiv (Cornell University). 9 indexed citations
11.
Nocera, Luciano, et al.. (2019). Identification of Developmental Delay in Infants Using Wearable Sensors: Full-Day Leg Movement Statistical Feature Analysis. IEEE Journal of Translational Engineering in Health and Medicine. 7. 1–7. 34 indexed citations
12.
Tsang, Michael, Hanpeng Liu, Sanjay Purushotham, Pavankumar Murali, & Yan Liu. (2018). Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability. neural information processing systems. 31. 5804–5813. 15 indexed citations
13.
Che, Zhengping, et al.. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports. 8(1). 6085–6085. 1239 indexed citations breakdown →
14.
Purushotham, Sanjay, Chuizheng Meng, Zhengping Che, & Yan Liu. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of Biomedical Informatics. 83. 112–134. 238 indexed citations
15.
Che, Zhengping, et al.. (2018). Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series. International Conference on Machine Learning. 784–793. 17 indexed citations
16.
Matsuo, Koji, Sanjay Purushotham, Bo Jiang, et al.. (2018). Survival outcome prediction in cervical cancer: Cox models vs deep-learning model. American Journal of Obstetrics and Gynecology. 220(4). 381.e1–381.e14. 115 indexed citations
17.
Purushotham, Sanjay, et al.. (2017). Variational Recurrent Adversarial Deep Domain Adaptation. International Conference on Learning Representations. 49 indexed citations
18.
Purushotham, Sanjay, et al.. (2016). Normal / Abnormal Heart Sound Recordings Classification Using Convolutional Neural Network. Computing in cardiology. 43. 77 indexed citations
19.
Patel, Amrutlal K., et al.. (2014). The Selection of Appropriate Health State Utility Values (Hsuvs) for Health Technology Assessment (Hta): Lessons To Be Learnt. Value in Health. 17(7). A573–A573. 1 indexed citations
20.
Purushotham, Sanjay, et al.. (2012). Hierarchical bag-of-words model for joint multi-view object representation and classification. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1–6. 1 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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