Kristjan Greenewald

1.3k total citations
24 papers, 314 citations indexed

About

Kristjan Greenewald is a scholar working on Artificial Intelligence, Statistics and Probability and Computer Networks and Communications. According to data from OpenAlex, Kristjan Greenewald has authored 24 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 8 papers in Statistics and Probability and 2 papers in Computer Networks and Communications. Recurrent topics in Kristjan Greenewald's work include Statistical Methods and Inference (6 papers), Machine Learning and Algorithms (4 papers) and Bayesian Methods and Mixture Models (3 papers). Kristjan Greenewald is often cited by papers focused on Statistical Methods and Inference (6 papers), Machine Learning and Algorithms (4 papers) and Bayesian Methods and Mixture Models (3 papers). Kristjan Greenewald collaborates with scholars based in United States, Netherlands and France. Kristjan Greenewald's co-authors include Alfred O. Hero, Keeheon Lee, Neil Thompson, Susan A. Murphy, Predrag Klasnja, Peng Liao, Trong Nghia Hoang, Soumya Ghosh, Mayank Agarwal and Mikhail Yurochkin and has published in prestigious journals such as IEEE Transactions on Signal Processing, Entropy and Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies.

In The Last Decade

Kristjan Greenewald

24 papers receiving 305 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kristjan Greenewald United States	7	151	45	33	32	31	24	314
Sinead A. Williamson United States	11	258 1.7×	9 0.2×	5 0.2×	12 0.4×	81 2.6×	29	393
Dennis L. Sun United States	8	69 0.5×	10 0.2×	8 0.2×	17 0.5×	10 0.3×	18	405
NhatHai Phan United States	12	461 3.1×	31 0.7×	26 0.8×	9 0.3×	2 0.1×	37	661
Eirini Ntoutsi Germany	10	312 2.1×	12 0.3×	6 0.2×	6 0.2×	4 0.1×	52	465
Stephen Verzi United States	13	189 1.3×	100 2.2×	34 1.0×	17 0.5×	2 0.1×	47	506
Aristodemos Pnevmatikakis Greece	11	89 0.6×	107 2.4×	14 0.4×	12 0.4×	4 0.1×	58	423
Jacky Casas Switzerland	7	183 1.2×	15 0.3×	30 0.9×	14 0.4×		11	323
Lihua Cai United States	10	69 0.5×	18 0.4×	54 1.6×	15 0.5×		24	342
Michele Donini Italy	13	230 1.5×	14 0.3×	9 0.3×	4 0.1×	4 0.1×	36	417
Pedro Sousa Portugal	10	164 1.1×	38 0.8×	4 0.1×	73 2.3×	3 0.1×	49	494

Countries citing papers authored by Kristjan Greenewald

Since Specialization

Citations

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

Fields of papers citing papers by Kristjan Greenewald

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kristjan Greenewald

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

All Works

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20 of 20 papers shown

Goldfeld, Ziv, et al.. (2025). Gradient Flows and Riemannian Structure in the Gromov-Wasserstein Geometry. Foundations of Computational Mathematics. 2 indexed citations

Zhou, Shuheng & Kristjan Greenewald. (2024). Sharper Rates of Convergence for the Tensor Graphical Lasso Estimator. 533–538. 1 indexed citations

Greenewald, Kristjan, et al.. (2024). Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training. 125702–125728. 1 indexed citations

Subramanian, Shivaram, et al.. (2024). PresAIse, a prescriptive AI solution for enterprise. INFOR Information Systems and Operational Research. 62(4). 629–645. 1 indexed citations

Solomon, Justin, Kristjan Greenewald, & Haikady N. Nagaraja. (2022). $k$-Variance: A Clustered Notion of Variance. SIAM Journal on Mathematics of Data Science. 4(3). 957–978. 1 indexed citations

Thompson, Neil, et al.. (2021). Deep Learning's Diminishing Returns: The Cost of Improvement is Becoming Unsustainable. IEEE Spectrum. 58(10). 50–55. 77 indexed citations

Magliacane, Sara, et al.. (2020). Active Structure Learning of Causal DAGs via Directed Clique Trees. arXiv (Cornell University). 33. 21500–21511. 2 indexed citations

Goldfeld, Ziv & Kristjan Greenewald. (2020). Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency. International Conference on Artificial Intelligence and Statistics. 3327–3337. 9 indexed citations

Liao, Peng, Kristjan Greenewald, Predrag Klasnja, & Susan A. Murphy. (2020). Personalized HeartSteps. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. 4(1). 1–22. 73 indexed citations

10.

Greenewald, Kristjan, et al.. (2019). Sample Efficient Active Learning of Causal Trees. DSpace@MIT (Massachusetts Institute of Technology). 32. 14279–14289. 3 indexed citations

11.

Yurochkin, Mikhail, Mayank Agarwal, Soumya Ghosh, et al.. (2019). Bayesian Nonparametric Federated Learning of Neural Networks. arXiv (Cornell University). 7252–7261. 71 indexed citations

12.

Yurochkin, Mikhail, Mayank Agarwal, Soumya Ghosh, et al.. (2018). Probabilistic Federated Neural Matching. 5 indexed citations

13.

Moon, Kevin R., Kumar Sricharan, Kristjan Greenewald, & Alfred O. Hero. (2018). Ensemble Estimation of Information Divergence †. Entropy. 20(8). 560–560. 14 indexed citations

14.

Greenewald, Kristjan, et al.. (2017). Time-dependent spatially varying graphical models, with application to brain fMRI data analysis. Neural Information Processing Systems. 30. 5832–5840. 1 indexed citations

15.

Moon, Kevin R., Kumar Sricharan, Kristjan Greenewald, & Alfred O. Hero. (2016). Improving convergence of divergence functional ensemble estimators. 1133–1137. 8 indexed citations

16.

Greenewald, Kristjan, Stephen Kelley, & Alfred O. Hero. (2016). Dynamic metric learning from pairwise comparisons. 2. 1327–1334. 3 indexed citations

17.

Greenewald, Kristjan & Alfred O. Hero. (2015). Robust Kronecker Product PCA for Spatio-Temporal Covariance Estimation. IEEE Transactions on Signal Processing. 63(23). 6368–6378. 20 indexed citations

18.

Greenewald, Kristjan & Alfred O. Hero. (2014). Regularized block Toeplitz covariance matrix estimation via Kronecker product expansions. 11. 9–12. 6 indexed citations

19.

Greenewald, Kristjan & Alfred O. Hero. (2014). Kronecker PCA based spatio-temporal modeling of video for dismount classification. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9093. 90930V–90930V. 3 indexed citations

20.

Greenewald, Kristjan. (2012). Prediction of Optimal Bayesian Classification Performance for LADAR ATR. Journal of Bioresource Management. 2 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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