Padhraic Smyth

29.3k citations
271 papers · 17.6k indexed · 6 hit papers · h-index 62

Impact in

Papers in

    • Bayesian Methods and Mixture Models 39
    • Bayesian Modeling and Causal Inference 26
    • Topic Modeling 23
    • Machine Learning and Algorithms 20
    • Neural Networks and Applications 19
    • Data Management and Algorithms 21

Padhraic Smyth

261 papers receiving 15.8k citations

Hit Papers

What large language models know and what people think they know 2025 · 29 citations
29199620262006201650010001.5k

Peers

Padhraic Smyth
Comparison fields: 5 of 219
  • Signal Processing 2.8k
  • Artificial Intelligence 8.3k
  • Information Systems 4.6k
  • Statistical and Nonlinear Physics 1.6k
  • Computer Science Applications 714
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Citations per field
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Citations per year

Countries citing papers authored by Padhraic Smyth

Since Specialization
Citations

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

Fields of papers citing papers by Padhraic Smyth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Padhraic Smyth, 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 Padhraic Smyth Line = papers co-authored together Padhraic Smyth links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20244
3 20237
4 202332
5 20232
6 202310
7 202240
8 202016
9
A VISION FOR THE DEVELOPMENT OF BENCHMARKS TO BRIDGE GEOSCIENCE AND DATA SCIENCE
201714
10
Analyzing NIH Funding Patterns over Time with Statistical Text Analysis.
20162
11 2012185
12 200428
13
Gene Expression Clustering with Functional Mixture Models
20037
14
Curve Clustering with Random Effects Regression Mixtures
200349
15
Discovering Chinese Words from Unsegmented Text.
199937
16
From data mining to knowledge discovery: an overview
Hit paper breakdown →
19961317
17
Trainable Cataloging for Digital Image Libraries with Applications to Volcano Detection
19955
18
Knowledge discovery in large image databases: dealing with uncertainties in ground truth
199426
19
Probabilistic Anomaly Detection in Dynamic Systems
19931
20
Multiresolution pattern recognition of small volcanos in Magellan data
19921

About Padhraic Smyth

Padhraic Smyth is a scholar working on Artificial Intelligence, Signal Processing, Statistics and Probability, Statistical and Nonlinear Physics and Information Systems, having authored 271 papers that have together received 17.6k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (39 papers), Data Mining Algorithms and Applications (26 papers), Bayesian Modeling and Causal Inference (26 papers), Topic Modeling (23 papers), Data Management and Algorithms (21 papers), Machine Learning and Algorithms (20 papers), Neural Networks and Applications (19 papers) and Climate variability and models (16 papers). The work is most often cited by research in Signal Processing (2.8k citations), Artificial Intelligence (8.3k citations), Information Systems (4.6k citations), Statistical and Nonlinear Physics (1.6k citations) and Computer Science Applications (714 citations). Padhraic Smyth has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Usama M. Fayyad, Gregory Piatetsky-Shapiro, Heikki Mannila, David J. Hand, Mark Steyvers, Scott Gaffney, Michal Rosen‐Zvi, Arthur Asuncion, R.M. Goodman and Max Welling. Their work appears in journals such as Machine Learning, Journal of Climate, Communications of the ACM, Proceedings of the National Academy of Sciences and Data Mining and Knowledge Discovery.

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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