2026 Lance Kaplan: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	42	8326	8090	3572	3437	274	8197

Research.com Recognitions

2016 - IEEE Fellow For contributions to signal processing and information fusion for situational awareness

Overview

Lance Kaplan is affiliated with the United States Army Research Laboratory in the United States. Their research primarily focuses on the field of Computer Science, with a significant body of work in Artificial Intelligence, as well as contributions to Signal Processing, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, and Management Science and Operations Research.

Their work includes exploration in several core topics such as:

Bayesian Modeling and Causal Inference
Explainable Artificial Intelligence (XAI)
Anomaly Detection Techniques and Applications
Adversarial Robustness in Machine Learning
Logic, Reasoning, and Knowledge
Time Series Analysis and Forecasting
Complex Network Analysis Techniques

Lance Kaplan has published extensively in reputable venues, including:

IEEE Aerospace and Electronic Systems Magazine
arXiv (Cornell University)
IEEE Transactions on Aerospace and Electronic Systems
IEEE Transactions on Signal Processing
Expert Systems with Applications

Frequent collaborators have included:

Daniel O'hagan
Michael Rice
Mary Randall
Dawn Melley
Kevin Lisankie

Representative recent publications by Lance Kaplan are:

"Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI," 2020, Patterns
"Uncertainty-Aware Deep Classifiers Using Generative Models," 2020, Proceedings of the AAAI Conference on Artificial Intelligence
"Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection," 2020, EPJ Data Science
"Non-Bayesian Social Learning With Uncertain Models," 2020, IEEE Transactions on Signal Processing
"A survey on uncertainty reasoning and quantification in belief theory and its application to deep learning," 2023, Information Fusion

In recognition of contributions to their fields, Lance Kaplan was awarded the IEEE Fellow distinction in 2016 for work related to signal processing and information fusion for situational awareness.

Best Publications

Evidential Deep Learning to Quantify Classification Uncertainty

Murat Sensoy;Lance M. Kaplan;Melih Kandemir

586
Citations
TOPS: new DOA estimator for wideband signals

Yeo-Sun Yoon;L.M. Kaplan;J.H. McClellan

410
Citations
On truth discovery in social sensing: a maximum likelihood estimation approach

Dong Wang;Lance Kaplan;Hieu Le;Tarek Abdelzaher

408
Citations
Extended fractal analysis for texture classification and segmentation

L.M. Kaplan

340
Citations
Global node selection for localization in a distributed sensor network

L.M. Kaplan

293
Citations
Using humans as sensors: an estimation-theoretic perspective

Dong Wang;Tanvir Amin;Shen Li;Tarek Abdelzaher

218
Citations
Maximum likelihood methods for bearings-only target localization

L.M. Kaplan;Qiang Le;N. Molnar

208
Citations
Improved SAR target detection via extended fractal features

L.M. Kaplan

177
Citations
The Age of Social Sensing

Dong Wang;Boleslaw K. Szymanski;Tarek Abdelzaher;Heng Ji

173
Citations
Performance Measures of Covariance and Information Matrices in Resource Management for Target State Estimation

Chun Yang;L. Kaplan;E. Blasch

172
Citations
GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams

Chao Zhang;Guangyu Zhou;Quan Yuan;Honglei Zhuang

169
Citations
On the quality and value of information in sensor networks

Chatschik Bisdikian;Lance M. Kaplan;Mani B. Srivastava

164
Citations
Social Sensing: Building Reliable Systems on Unreliable Data

Dong Wang;Tarek Abdelzaher;Lance Kaplan

159
Citations
Fractal estimation from noisy data via discrete fractional Gaussian noise (DFGN) and the Haar basis

L.M. Kaplan;C.-C.J. Kuo

156
Citations
Recursive Fact-Finding: A Streaming Approach to Truth Estimation in Crowdsourcing Applications

Dong Wang;Tarek Abdelzaher;Lance Kaplan;Charu C. Aggarwal

155
Citations
Assignment costs for multiple sensor track-to-track association

L. Kaplan;Y. Bar-Shalom;W. Blair

143
Citations
Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks

Jingbo Shang;Meng Qu;Jialu Liu;Lance M. Kaplan

142
Citations
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI.

Richard Tomsett;Alun D. Preece;Dave Braines;Federico Cerutti;Federico Cerutti

142
Citations
Texture roughness analysis and synthesis via extended self-similar (ESS) model

L.M. Kaplan;C.-C.J. Kuo

131
Citations
Extending self-similarity for fractional Brownian motion

L.M. Kaplan;C.-C.J. Kuo

121
Citations
Building principles for a quality of information specification for sensor information

Chatschik Bisdikian;Lance M. Kaplan;Mani B. Srivastava;David J. Thornley

27
Citations

Frequent Co-Authors

Tarek Abdelzaher University of Illinois at Urbana-Champaign

Dong Wang Peking University

Jiawei Han University of Illinois at Urbana-Champaign

Mani Srivastava University of California, Los Angeles

James H. McClellan Georgia Institute of Technology

Chatschik Bisdikian IBM (United States)

Alun Preece Cardiff University

Rick S. Blum Lehigh University

Lu Su Purdue University West Lafayette

Timothy J. Norman University of Southampton

External Links

Personal website of Lance Kaplan Google Scholar page

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