Jed Irvine

575 citations
16 papers · 198 · h-index 8

Impact in

Papers in

    • Explainable Artificial Intelligence (XAI) 6
    • Reinforcement Learning in Robotics 2
    • Adversarial Robustness in Machine Learning 2
    • Ethics and Social Impacts of AI 4

Jed Irvine

15 papers receiving 196 citations

Peers

Jed Irvine
Comparison fields: 5 of 50
  • Health Informatics 16
  • Ecological Modeling 32
  • Developmental Biology 13
  • Information Systems and Management 27
  • Safety Research 30
Replace Christoph Kinkeldey with:
Christoph Kinkeldey Germany
Naman Goel United States
Sean McGregor United States
Jens Ingensand Switzerland
Reyna Jenkyns Canada
J. L. Zhao United States
Thiago Vieira de Souza Brazil
Mark Servilla United States
Albert Y. Kim United States
Fernando Aparicio Spain
Jed Irvine relative to Christoph Kinkeldey Germany Christoph Kinkeldey's profile →
Citations per field
00.5×10×13×
Christoph Kinkeldey · 1×
Citations per year

Countries citing papers authored by Jed Irvine

Since Specialization
Citations

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

Fields of papers citing papers by Jed Irvine

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 20 scholars most cited alongside Jed Irvine, 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 Jed Irvine Line = papers co-authored together Jed Irvine links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 201651
2 202036
3 201327
4 200917
5 201412
6 202011
7 202111
8 20228
9 20156
10 20215
11
Predicting task-specific webpages for revisiting
20064
12
Learning Rules from Incomplete Examples via Implicit Mention Models
20114
13 20213
14 20212
15 20221
16 19730

About Jed Irvine

Jed Irvine is a scholar working on Artificial Intelligence, Safety Research, Information Systems, Ecology and Information Systems and Management, having authored 16 papers that have together received 198 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (6 papers), Ethics and Social Impacts of AI (4 papers), Personal Information Management and User Behavior (2 papers), Reinforcement Learning in Robotics (2 papers), Adversarial Robustness in Machine Learning (2 papers), Species Distribution and Climate Change (2 papers), Avian ecology and behavior (2 papers) and Web Data Mining and Analysis (2 papers). The work is most often cited by research in Health Informatics (16 citations), Ecological Modeling (32 citations), Developmental Biology (13 citations), Information Systems and Management (27 citations) and Safety Research (30 citations). Jed Irvine has collaborated with scholars based in United States. Frequent co-authors include Andrew Farnsworth, Daniel Sheldon, Benjamin M. Van Doren, Steve Kelling, Thomas G. Dietterich, Margaret Burnett, Jonathan Dodge, Alan Fern, Kevin Winner and Wesley M. Hochachka. Their work appears in journals such as ACM Transactions on Interactive Intelligent Systems, Ecological Applications, AI Magazine, SHILAP Revista de lepidopterología and Information Storage and Retrieval.

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.

Explore authors with similar magnitude of impact