Jacob Bien
- Artificial Intelligence top 5%
- Molecular Biology
- Statistics and Probability top 2%
- Computer Vision and Pattern Recognition top 10%
- Computational Mechanics top 10%
- Co-authors
- Robert TibshiraniRyan J. TibshiraniNoah SimonJerome H. FriedmanJonathan TaylorTrevor HastieDaniela WittenLucy L. Gao
- Topics
- Statistical Methods and Inference (14 papers)Sparse and Compressive Sensing Techniques (4 papers)Gene expression and cancer classification (4 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaJournal of the American Statistical Association
- Partner nations
- United StatesCanadaGermany
In The Last Decade
Jacob Bien
33 papers receiving 946 citations
Hit Papers
Peers
Comparison fields: 5 of 155
- Artificial Intelligence 316
- Molecular Biology 195
- Statistics and Probability 189
- Computer Vision and Pattern Recognition 95
- Computational Mechanics 86
Countries citing papers authored by Jacob Bien
This map shows the geographic impact of Jacob Bien'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 Jacob Bien with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob Bien more than expected).
Fields of papers citing papers by Jacob Bien
This network shows the impact of papers produced by Jacob Bien. 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 Jacob Bien. The network helps show where Jacob Bien may publish in the future.
Co-authorship network of co-authors of Jacob Bien
This figure shows the co-authorship network connecting the top 25 collaborators of Jacob Bien. A scholar is included among the top collaborators of Jacob Bien 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 Jacob Bien. Jacob Bien is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 2 | |
| 4 | 6 | |
| 5 | 0 | |
| 6 | 21 | |
| 7 | 50 | |
| 8 | 22 | |
| 9 | 13 | |
| 10 | 19 | |
| 11 | Dimension Reduction Methods for Multivariate Time Series [R package BigVAR version 1.0.6] | 2 |
| 12 | 3 | |
| 13 | 21 | |
| 14 | Sparse Identification and Estimation of High-Dimensional Vector AutoRegressive Moving Averages | 2 |
| 15 | Learning local dependence in ordered data | 5 |
| 16 | 25 | |
| 17 | 26 | |
| 18 | 30 | |
| 19 | A lasso for hierarchical testing of interactions | 4 |
| 20 | 95 |
About Jacob Bien
Jacob Bien is a scholar working on Statistics and Probability, Computational Mathematics and Modeling and Simulation, having authored 35 papers that have together received 976 indexed citations. Recurring topics across this work include Statistical Methods and Inference (14 papers), Sparse and Compressive Sensing Techniques (4 papers) and Gene expression and cancer classification (4 papers). The work is most often cited by research in Computational Mathematics (15 citations), Statistics and Probability (189 citations) and Health Informatics (18 citations). Jacob Bien has collaborated with scholars based in United States, Canada and Germany. Frequent co-authors include Robert Tibshirani, Ryan J. Tibshirani, Noah Simon, Jerome H. Friedman, Jonathan Taylor, Trevor Hastie, Daniela Witten, Lucy L. Gao, Shuxiao Chen and Florentina Bunea. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Journal of the American Statistical Association.
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.