Mark A. Paskin

1.5k citations
15 papers · 990 · h-index 11

Impact in

Papers in

Journals
Uncertainty in Artificial Intelligence (1 paper)Information Processing in Sensor Networks (1 paper)Neural Information Processing Systems (2 papers)UC Berkeley (2 papers)

In The Last Decade

Mark A. Paskin

15 papers receiving 913 citations

Peers

Mark A. Paskin
Comparison fields: 5 of 72
  • Computer Networks and Communications 487
  • Computer Vision and Pattern Recognition 274
  • Artificial Intelligence 405
  • Signal Processing 132
  • Aerospace Engineering 217
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Zhen Zuo China
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Countries citing papers authored by Mark A. Paskin

Since Specialization
Citations

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

Fields of papers citing papers by Mark A. Paskin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark A. Paskin. 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 Mark A. Paskin. The network helps show where Mark A. Paskin may publish in the future.

Co-authors

The 8 scholars most cited alongside Mark A. Paskin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mark A. Paskin Line = papers co-authored together Mark A. Paskin links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 2004301
2
Thin junction tree filters for simultaneous localization and mapping
2003146
3
Linear-time inference in Hierarchical HMMs
2001110
4 200592
5 200688
6 200677
7 200456
8 200549
9 199721
10
Maximum-Entropy Probabilistic Logics
200111
11
Junction tree algorithms for solving sparse linear systems
200311
12
Sample Propagation
20038
13
Thin Junction Tree Filtering for Simultaneous Localization and Mapping
20028
14
Exploiting locality in probabilistic inference
20046
15
Cubic-time Parsing and Learning Algorithms for Grammatical Bigram
20016

About Mark A. Paskin

Mark A. Paskin is a scholar working on Artificial Intelligence, Computer Networks and Communications, Aerospace Engineering, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 15 papers that have together received 990 indexed citations. Recurring topics across this work include Distributed Sensor Networks and Detection Algorithms (5 papers), Robotics and Sensor-Based Localization (5 papers), Energy Efficient Wireless Sensor Networks (4 papers), Bayesian Modeling and Causal Inference (4 papers), Indoor and Outdoor Localization Technologies (3 papers), Target Tracking and Data Fusion in Sensor Networks (3 papers), Machine Learning and Algorithms (3 papers) and Data Management and Algorithms (2 papers). The work is most often cited by research in Computer Networks and Communications (487 citations), Computer Vision and Pattern Recognition (274 citations), Artificial Intelligence (405 citations), Signal Processing (132 citations) and Aerospace Engineering (217 citations). Mark A. Paskin has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Carlos Guestrin, Peter Bodík, Samuel Madden, Kevin P. Murphy, Rahul Sukthankar, Stanislav Funiak, Praveen Seshadri and Stuart Russell. Their work appears in journals such as Uncertainty in Artificial Intelligence, Information Processing in Sensor Networks, Neural Information Processing Systems and UC Berkeley.

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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