Prasanna Sattigeri

3.7k total citations
53 papers, 1.1k citations indexed

About

Prasanna Sattigeri is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Safety Research. According to data from OpenAlex, Prasanna Sattigeri has authored 53 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 12 papers in Computer Vision and Pattern Recognition and 9 papers in Safety Research. Recurrent topics in Prasanna Sattigeri's work include Explainable Artificial Intelligence (XAI) (12 papers), Adversarial Robustness in Machine Learning (10 papers) and Ethics and Social Impacts of AI (9 papers). Prasanna Sattigeri is often cited by papers focused on Explainable Artificial Intelligence (XAI) (12 papers), Adversarial Robustness in Machine Learning (10 papers) and Ethics and Social Impacts of AI (9 papers). Prasanna Sattigeri collaborates with scholars based in United States, India and Israel. Prasanna Sattigeri's co-authors include Kush R. Varshney, Samuel C. Hoffman, Karthikeyan Natesan Ramamurthy, Andreas Spanias, Aleksandra Mojsilović, Stephanie Houde, Michael Hind, John T. Richards, Rachel Bellamy and Jayaraman J. Thiagarajan and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Journal of Machine Learning Research and IBM Journal of Research and Development.

In The Last Decade

Prasanna Sattigeri

50 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Prasanna Sattigeri United States 15 611 342 187 119 83 53 1.1k
Zhiwei Steven Wu China 17 424 0.7× 211 0.6× 67 0.4× 70 0.6× 65 0.8× 102 1.0k
Sameep Mehta India 17 483 0.8× 270 0.8× 168 0.9× 90 0.8× 312 3.8× 80 1.3k
Kuntal Dey India 15 638 1.0× 311 0.9× 141 0.8× 88 0.7× 175 2.1× 54 1.1k
Samuel C. Hoffman United States 10 369 0.6× 296 0.9× 61 0.3× 111 0.9× 62 0.7× 15 733
Etsuko Ishii Hong Kong 5 916 1.5× 107 0.3× 124 0.7× 296 2.5× 185 2.2× 11 1.6k
Rita Frieske Hong Kong 2 885 1.4× 106 0.3× 114 0.6× 291 2.4× 171 2.1× 3 1.5k
Jieyu Zhao China 14 804 1.3× 178 0.5× 328 1.8× 50 0.4× 65 0.8× 95 1.3k
Emily Reif United States 10 456 0.7× 72 0.2× 110 0.6× 91 0.8× 74 0.9× 17 802
Ziwei Ji Hong Kong 10 1.3k 2.1× 122 0.4× 170 0.9× 378 3.2× 259 3.1× 22 2.1k
Andrea Madotto Hong Kong 17 1.6k 2.7× 108 0.3× 280 1.5× 293 2.5× 214 2.6× 42 2.4k

Countries citing papers authored by Prasanna Sattigeri

Since Specialization
Citations

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

Fields of papers citing papers by Prasanna Sattigeri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prasanna Sattigeri

This figure shows the co-authorship network connecting the top 25 collaborators of Prasanna Sattigeri. A scholar is included among the top collaborators of Prasanna Sattigeri 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 Prasanna Sattigeri. Prasanna Sattigeri 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.
Sattigeri, Prasanna, et al.. (2024). Language Models in Dialogue: Conversational Maxims for Human-AI Interactions. 14420–14437. 3 indexed citations
2.
Baldini, Ioana, Djallel Bouneffouf, Maria Chang, et al.. (2024). Alignment Studio: Aligning Large Language Models to Particular Contextual Regulations. IEEE Internet Computing. 28(5). 28–36. 1 indexed citations
3.
Sattigeri, Prasanna, et al.. (2023). Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model. Proceedings of the AAAI Conference on Artificial Intelligence. 37(8). 9772–9781. 13 indexed citations
4.
Ghosh, Soumya K., et al.. (2023). Reliable Gradient-free and Likelihood-free Prompt Tuning. 2416–2429. 6 indexed citations
5.
Gero, Katy Ilonka, et al.. (2023). The incentive gap in data work in the era of large models. Nature Machine Intelligence. 5(6). 565–567. 6 indexed citations
6.
Lee, Joshua, Prasanna Sattigeri, Rameswar Panda, et al.. (2022). A Maximal Correlation Framework for Fair Machine Learning. Entropy. 24(4). 461–461. 2 indexed citations
7.
Sattigeri, Prasanna, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, et al.. (2021). Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors. 12(1). 1–17. 1 indexed citations
8.
Arya, Vijay, Rachel Bellamy, Pin‐Yu Chen, et al.. (2020). AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models. Journal of Machine Learning Research. 21(130). 1–6. 37 indexed citations
9.
Baldini, Ioana, et al.. (2020). A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications. Proceedings of the AAAI Conference on Artificial Intelligence. 34(8). 13369–13381. 13 indexed citations
10.
Arya, Vijay, Rachel Bellamy, Pin‐Yu Chen, et al.. (2020). AI Explainability 360 Toolkit. 376–379. 19 indexed citations
11.
Lee, Joshua K., Prasanna Sattigeri, & Gregory W. Wornell. (2019). Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks. DSpace@MIT (Massachusetts Institute of Technology). 32. 4370–4380. 11 indexed citations
12.
Thiagarajan, Jayaraman J., Deepta Rajan, & Prasanna Sattigeri. (2018). Can Deep Clinical Models Handle Real-World Domain Shifts?. arXiv (Cornell University). 1 indexed citations
13.
Kumar, Abhishek, Prasanna Sattigeri, Leonid Karlinsky, et al.. (2018). Co-regularized Alignment for Unsupervised Domain Adaptation. arXiv (Cornell University). 31. 9345–9356. 49 indexed citations
14.
Kumar, Abhishek, Prasanna Sattigeri, & P. Thomas Fletcher. (2017). Improved Semi-supervised Learning with GANs using Manifold Invariances. arXiv (Cornell University). 5538–5548. 2 indexed citations
15.
Kumar, Abhishek, Prasanna Sattigeri, & P. Thomas Fletcher. (2017). Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference. arXiv (Cornell University). 30. 5534–5544. 14 indexed citations
16.
Sattigeri, Prasanna & Jayaraman J. Thiagarajan. (2016). Sparsifying Word Representations for Deep Unordered Sentence Modeling. 206–214. 1 indexed citations
17.
Ramamurthy, Karthikeyan Natesan, Jayaraman J. Thiagarajan, Andreas Spanias, & Prasanna Sattigeri. (2013). Boosted dictionaries for image restoration based on sparse representations. 14. 1583–1587. 2 indexed citations
18.
Kim, Hyun‐Tae, Prasanna Sattigeri, Joseph Wang, et al.. (2011). Electronic-nose for detecting environmental pollutants: signal processing and analog front-end design. Analog Integrated Circuits and Signal Processing. 70(1). 15–32. 25 indexed citations
19.
Sattigeri, Prasanna, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, et al.. (2010). Signal processing for biologically inspired sensors. 1–5. 1 indexed citations
20.
Sattigeri, Prasanna, Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, et al.. (2010). Analysis of Coulter counting data from nanopores using clustering. 17–17. 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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