Alan S. Willsky

437 papers receiving 19.3k citations

Hit Papers

A sparse signal reconstruction pers...19762026199220092005197619762003200150010001.5k

Peers

Alan S. Willsky
Comparison fields: 5 of 185
  • Computer Vision and Pattern Recognition 5.9k
  • Artificial Intelligence 5.6k
  • Control and Systems Engineering 5.2k
  • Signal Processing 3.0k
  • Computer Networks and Communications 2.6k
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Citations per year

Countries citing papers authored by Alan S. Willsky

Since Specialization
Citations

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

Fields of papers citing papers by Alan S. Willsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alan S. Willsky. 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 Alan S. Willsky. The network helps show where Alan S. Willsky may publish in the future.

Co-authorship network of co-authors of Alan S. Willsky

This figure shows the co-authorship network connecting the top 25 collaborators of Alan S. Willsky. A scholar is included among the top collaborators of Alan S. Willsky 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 Alan S. Willsky. Alan S. Willsky 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
#WorkIndexed citations
1
New graph-based and MCMC approaches to multi-INT surveillance
10
2
MCMC and MHT Approaches to Multi-INT surveillance
4
3
Stochastic Variational Inference for Bayesian Time Series Models
37
4
High-Dimensional Gaussian Graphical Model Selection: Tractable Graph Families
5
5
High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions
8
6 1
7
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
98
8
Learning Markov Structure by Maximum Entropy Relaxation
9
9 160
10
Inference with Minimal Communication: a Decision-Theoretic Variational Approach
8
11
Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation
32
12 188
13
Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation
60
14
Source localization by enforcing sparsity through a Laplacian prior: an SVD-based approach
26
15
Exact MAP Estimates by (Hyper)tree Agreement
21
16
Tree-based reparameterization for approximate inference on loopy graphs
43
17 1
18 2
19 1
20 89

About Alan S. Willsky

Alan S. Willsky is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistics and Probability, having authored 459 papers that have together received 20.7k indexed citations. Recurring topics across this work include Target Tracking and Data Fusion in Sensor Networks (64 papers), Image and Signal Denoising Methods (64 papers) and Medical Image Segmentation Techniques (52 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (5.9k citations), Signal Processing (3.0k citations) and Control and Systems Engineering (5.2k citations). Alan S. Willsky has collaborated with scholars based in United States, France and Türkiye. Frequent co-authors include Müjdat Çetin, Dmitry Malioutov, Andy Tsai, Anthony Yezzi, Erik B. Sudderth, Martin J. Wainwright, Harold E. Jones, Alexander Ihler, Tommi Jaakkola and W.C. Karl. Their work appears in journals such as Journal of Geophysical Research Atmospheres, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Automatic Control.

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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