Philip Kegelmeyer

1.6k total citations · 1 hit paper
9 papers, 527 citations indexed

About

Philip Kegelmeyer is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Philip Kegelmeyer has authored 9 papers receiving a total of 527 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 3 papers in Molecular Biology and 3 papers in Information Systems. Recurrent topics in Philip Kegelmeyer's work include Data Mining Algorithms and Applications (3 papers), Machine Learning in Bioinformatics (2 papers) and Anomaly Detection Techniques and Applications (1 paper). Philip Kegelmeyer is often cited by papers focused on Data Mining Algorithms and Applications (3 papers), Machine Learning in Bioinformatics (2 papers) and Anomaly Detection Techniques and Applications (1 paper). Philip Kegelmeyer collaborates with scholars based in United States. Philip Kegelmeyer's co-authors include David Buttler, David Andrzejewski, Keith Stevens, Chandrika Kamath, Robert L. Grossman, Vipin Kumar, Clayton Springer, Malin M. Young, Helgi Adalsteinsson and Diana C. Roe and has published in prestigious journals such as Journal of Medicinal Chemistry, Machine Vision and Applications and Empirical Methods in Natural Language Processing.

In The Last Decade

Philip Kegelmeyer

8 papers receiving 484 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Exploring Topic Coherence over Many Models and Many Topics

2012 289 citations Keith Stevens, Philip Kegelmeyer et al. Empirical Methods in Natural Language Processing profile →

Peers

Countries citing papers authored by Philip Kegelmeyer

Since Specialization

Citations

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

Fields of papers citing papers by Philip Kegelmeyer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philip Kegelmeyer

This figure shows the co-authorship network connecting the top 25 collaborators of Philip Kegelmeyer. A scholar is included among the top collaborators of Philip Kegelmeyer 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 Philip Kegelmeyer. Philip Kegelmeyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

#	Work	Indexed citations
1	A Review of Machine Learning Applications in Fuzzing 2019 ·arXiv (Cornell University) ·(unknown), (unknown), Daniel Dunlavy, Philip Kegelmeyer	13
2	Attacking DBSCAN for Fun and Profit 2015 ·OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) ·Jonathan Crussell, Philip Kegelmeyer	4
3	Exploring Topic Coherence over Many Models and Many Topics breakdown → 2012 ·Empirical Methods in Natural Language Processing ·Keith Stevens, Philip Kegelmeyer, David Andrzejewski, David Buttler	289
4	Matlab Cluster Ensemble Toolbox 2009 ·OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) ·Vincent De Sapio, Philip Kegelmeyer	0
5	Bagging is a small-data-set phenomenon 2005 ·Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence Hall, Clayton Springer, Philip Kegelmeyer	4
6	PostDOCK: A Structural, Empirical Approach to Scoring Protein Ligand Complexes 2005 ·Journal of Medicinal Chemistry ·Clayton Springer, Helgi Adalsteinsson, Malin M. Young, Philip Kegelmeyer, Diana C. Roe	31
7	Bagging-like effects for decision trees and neural nets in protein secondary structure prediction 2001 ·Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence Hall, Clayton Springer, Philip Kegelmeyer	5
8	Data Mining for Scientific and Engineering Applications 2001 ·Robert L. Grossman, Chandrika Kamath, Philip Kegelmeyer, Vipin Kumar	172
9	The computation of cloud base height from paired whole-sky imaging cameras 1997 ·Machine Vision and Applications ·(unknown), Philip Kegelmeyer	9

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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