Samantha Kleinberg

1.3k total citations
60 papers, 821 citations indexed

About

Samantha Kleinberg is a scholar working on Artificial Intelligence, Endocrinology, Diabetes and Metabolism and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Samantha Kleinberg has authored 60 papers receiving a total of 821 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 11 papers in Endocrinology, Diabetes and Metabolism and 10 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Samantha Kleinberg's work include Bayesian Modeling and Causal Inference (13 papers), Diabetes Management and Research (11 papers) and Nutritional Studies and Diet (9 papers). Samantha Kleinberg is often cited by papers focused on Bayesian Modeling and Causal Inference (13 papers), Diabetes Management and Research (11 papers) and Nutritional Studies and Diet (9 papers). Samantha Kleinberg collaborates with scholars based in United States, China and France. Samantha Kleinberg's co-authors include George Hripcsak, Yuxiao Huang, Jan Claassen, Bud Mishra, Min Zheng, J. Michael Schmidt, David J. Albers, Nathaniel D. Heintzman, E. Sander Connolly and Hector Lantigua and has published in prestigious journals such as PLoS ONE, Annals of Neurology and Journal of Nutrition.

In The Last Decade

Samantha Kleinberg

54 papers receiving 785 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Samantha Kleinberg United States 14 218 114 107 102 80 60 821
Zhenxing Xu United States 17 320 1.5× 38 0.3× 175 1.6× 57 0.6× 36 0.5× 51 1.2k
Miguel Patrício Portugal 18 166 0.8× 29 0.3× 84 0.8× 27 0.3× 38 0.5× 40 897
Roberta Annicchiarico Italy 14 93 0.4× 19 0.2× 175 1.6× 59 0.6× 134 1.7× 26 610
Jiancheng Dong China 15 91 0.4× 39 0.3× 35 0.3× 45 0.4× 19 0.2× 58 781
Giuseppe Fico Spain 16 115 0.5× 61 0.5× 14 0.1× 81 0.8× 64 0.8× 85 878
Md. Maniruzzaman Bangladesh 24 495 2.3× 119 1.0× 23 0.2× 81 0.8× 72 0.9× 90 1.7k
N.D. Black United Kingdom 20 277 1.3× 66 0.6× 13 0.1× 242 2.4× 198 2.5× 90 1.2k
Sunita Tiwari India 17 89 0.4× 82 0.7× 15 0.1× 46 0.5× 25 0.3× 107 1.1k
Vincenzo Lagani Greece 22 205 0.9× 27 0.2× 85 0.8× 26 0.3× 25 0.3× 72 1.2k

Countries citing papers authored by Samantha Kleinberg

Since Specialization
Citations

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

Fields of papers citing papers by Samantha Kleinberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samantha Kleinberg

This figure shows the co-authorship network connecting the top 25 collaborators of Samantha Kleinberg. A scholar is included among the top collaborators of Samantha Kleinberg 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 Samantha Kleinberg. Samantha Kleinberg 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.
Claassen, Jan, et al.. (2025). Causal inference for time series datasets with partially overlapping variables. Journal of Biomedical Informatics. 166. 104828–104828.
2.
Shen, Yuan & Samantha Kleinberg. (2024). Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM. IEEE Transactions on Biomedical Engineering. 72(4). 1266–1277. 5 indexed citations
3.
Marsh, Jessecae K., Onur Asan, & Samantha Kleinberg. (2024). Perceived Penalties for Sharing Patient Beliefs with Health Care Providers. Medical Decision Making. 44(6). 617–626. 1 indexed citations
4.
Thomas, Diana M., Rob Knight, Jack A. Gilbert, et al.. (2024). Transforming Big Data into AI‐ready data for nutrition and obesity research. Obesity. 32(5). 857–870. 4 indexed citations
5.
Wang, Chan, Adam Hoover, David E. St‐Jules, et al.. (2023). Objective Determination of Eating Occasion Timing: Combining Self-Report, Wrist Motion, and Continuous Glucose Monitoring to Detect Eating Occasions in Adults With Prediabetes and Obesity. Journal of Diabetes Science and Technology. 18(2). 266–272. 4 indexed citations
6.
Kleinberg, Samantha, et al.. (2023). Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation. Journal of Diabetes Science and Technology. 19(1). 114–122. 2 indexed citations
7.
Shen, Qi, Kevin Doyle, Ángela Velázquez, et al.. (2022). Classification of Level of Consciousness in a Neurological ICU Using Physiological Data. Neurocritical Care. 38(1). 118–128. 4 indexed citations
8.
Huang, Jingtong, David G. Armstrong, Ashley N. Battarbee, et al.. (2022). Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. Journal of Diabetes Science and Technology. 17(1). 224–238. 29 indexed citations
9.
Fonnesbeck, Christopher, et al.. (2021). Using Momentary Assessment and Machine Learning to Identify Barriers to Self-management in Type 1 Diabetes: Observational Study. JMIR mhealth and uhealth. 10(3). e21959–e21959. 8 indexed citations
10.
Kleinberg, Samantha & Jessecae K. Marsh. (2020). Tell me something I don't know: How perceived knowledge influences the use of information during decision making.. Cognitive Science. 2 indexed citations
11.
Kleinberg, Samantha, et al.. (2020). Investigating Potentials and Pitfalls of Knowledge Distillation Across Datasets for Blood Glucose Forecasting.. 85–89. 6 indexed citations
12.
Zheng, Min & Samantha Kleinberg. (2019). Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series.. PubMed. 106. 474–489. 1 indexed citations
13.
Kleinberg, Samantha, et al.. (2017). A method for automating token causal explanation and discovery. The Florida AI Research Society. 176–181. 2 indexed citations
14.
Huang, Yuxiao & Samantha Kleinberg. (2015). Fast and Accurate Causal Inference from Time Series Data. The Florida AI Research Society. 49–54. 5 indexed citations
15.
Kleinberg, Samantha. (2013). Causal inference with rare events in large-scale time-series data. International Joint Conference on Artificial Intelligence. 1444–1450. 3 indexed citations
16.
Kleinberg, Samantha. (2011). A logic for causal inference in time series with discrete and continuous variables. International Joint Conference on Artificial Intelligence. 943–950. 13 indexed citations
17.
Kleinberg, Samantha & George Hripcsak. (2011). A review of causal inference for biomedical informatics. Journal of Biomedical Informatics. 44(6). 1102–1112. 108 indexed citations
18.
Kleinberg, Samantha & Bud Mishra. (2010). The temporal logic of token causes. Principles of Knowledge Representation and Reasoning. 575–577. 3 indexed citations
19.
Kleinberg, Samantha & Bud Mishra. (2009). Metamorphosis: the Coming Transformation of Translational Systems Biology. Queue. 7(9). 40–52. 2 indexed citations
20.
Kleinberg, Samantha, Marco Antoniotti, Naren Ramakrishnan, & Bharat Mishra. (2007). Modal Logic, Temporal Models and Neural Circuits: What Connects Them. BOA (University of Milano-Bicocca). 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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