Jim Huang
- Artificial Intelligence top 1%
- Aerospace Engineering top 2%
- Molecular Biology
- Cancer Research top 5%
- Electrical and Electronic Engineering
- Co-authors
- Fred DaumYaakov Bar‐ShalomBrendan J. FreyQuaid MorrisBenjamin J. BlencoweSofia KhanBrenda L. GallieTimothy W. Corson
- Topics
- Target Tracking and Data Fusion in Sensor Networks (14 papers)Hydrology and Drought Analysis (7 papers)Statistical Mechanics and Entropy (7 papers)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Jim Huang
76 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Artificial Intelligence 1.0k
- Aerospace Engineering 508
- Molecular Biology 445
- Cancer Research 356
- Electrical and Electronic Engineering 212
Countries citing papers authored by Jim Huang
This map shows the geographic impact of Jim Huang'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 Jim Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jim Huang more than expected).
Fields of papers citing papers by Jim Huang
This network shows the impact of papers produced by Jim Huang. 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 Jim Huang. The network helps show where Jim Huang may publish in the future.
Co-authorship network of co-authors of Jim Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Jim Huang. A scholar is included among the top collaborators of Jim Huang 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 Jim Huang. Jim Huang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 3 | |
| 3 | 21 | |
| 4 | 1 | |
| 5 | Renormalization group flow in k-space for nonlinear filters, Bayesian decisions and transport | 5 |
| 6 | Particle flow for nonlinear filters, Bayesian decisions and transport | 17 |
| 7 | Particle flow and Monge-Kantorovich transport | 17 |
| 8 | 1 | |
| 9 | 3 | |
| 10 | 25 | |
| 11 | Maximum-likelihood learning of cumulative distribution functions on graphs | 4 |
| 12 | 101 | |
| 13 | Exact inference and learning for cumulative distribution functions on loopy graphs | 1 |
| 14 | 33 | |
| 15 | 1 | |
| 16 | 54 | |
| 17 | Structured ranking learning using cumulative distribution networks | 7 |
| 18 | 338 | |
| 19 | 3 | |
| 20 | H ∞ control design for an adaptive optics system. | 1 |
About Jim Huang
Jim Huang is a scholar working on Computational Mathematics, Aging and Statistics and Probability, having authored 77 papers that have together received 2.2k indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (14 papers), Hydrology and Drought Analysis (7 papers) and Statistical Mechanics and Entropy (7 papers). The work is most often cited by research in Artificial Intelligence (1.0k citations), Cancer Research (356 citations) and Aerospace Engineering (508 citations). Jim Huang has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Fred Daum, Yaakov Bar‐Shalom, Brendan J. Frey, Quaid Morris, Benjamin J. Blencowe, Sofia Khan, Brenda L. Gallie, Timothy W. Corson, Tomas Babak and Gordon Chua. Their work appears in journals such as Bioinformatics, Nature Methods and Journal of Virology.
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.