Chad Lieberman

406 total citations
9 papers, 273 citations indexed

About

Chad Lieberman is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Statistics, Probability and Uncertainty. According to data from OpenAlex, Chad Lieberman has authored 9 papers receiving a total of 273 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Statistical and Nonlinear Physics, 5 papers in Artificial Intelligence and 3 papers in Statistics, Probability and Uncertainty. Recurrent topics in Chad Lieberman's work include Model Reduction and Neural Networks (5 papers), Gaussian Processes and Bayesian Inference (4 papers) and Probabilistic and Robust Engineering Design (3 papers). Chad Lieberman is often cited by papers focused on Model Reduction and Neural Networks (5 papers), Gaussian Processes and Bayesian Inference (4 papers) and Probabilistic and Robust Engineering Design (3 papers). Chad Lieberman collaborates with scholars based in United States. Chad Lieberman's co-authors include Karen Willcox, Omar Ghattas, Mikhail Zaslavsky, Vladimir Druskin, John W. M. Bush, Krzysztof Fidkowski, Jeffrey M. Aristoff and Bart van Bloemen Waanders and has published in prestigious journals such as Physics of Fluids, SIAM Review and SIAM Journal on Scientific Computing.

In The Last Decade

Chad Lieberman

9 papers receiving 251 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Chad Lieberman United States	7	126	102	51	48	47	9	273
Harbir Antil United States	13	124 1.0×	52 0.5×	18 0.4×	33 0.7×	77 1.6×	61	422
Alessandro Alla Italy	9	331 2.6×	107 1.0×	57 1.1×	38 0.8×	51 1.1×	27	469
Alexander Litvinenko Germany	10	94 0.7×	226 2.2×	44 0.9×	16 0.3×	50 1.1×	30	367
Yiping Lu China	5	260 2.1×	67 0.7×	101 2.0×	27 0.6×	18 0.4×	17	386
Mohammad Motamed United States	10	40 0.3×	82 0.8×	13 0.3×	66 1.4×	50 1.1×	22	306
Tong Qin United States	7	203 1.6×	82 0.8×	68 1.3×	26 0.5×	48 1.0×	11	387
Timothy Wildey United States	16	112 0.9×	233 2.3×	69 1.4×	61 1.3×	240 5.1×	52	644
Qifeng Liao China	11	197 1.6×	108 1.1×	55 1.1×	96 2.0×	39 0.8×	34	368
Mario De Florio United States	12	273 2.2×	32 0.3×	103 2.0×	58 1.2×	13 0.3×	19	460

Countries citing papers authored by Chad Lieberman

Since Specialization

Citations

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

Fields of papers citing papers by Chad Lieberman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chad Lieberman

This figure shows the co-authorship network connecting the top 25 collaborators of Chad Lieberman. A scholar is included among the top collaborators of Chad Lieberman 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 Chad Lieberman. Chad Lieberman 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

1.

Lieberman, Chad & Karen Willcox. (2014). Nonlinear Goal-Oriented Bayesian Inference: Application to Carbon Capture and Storage. SIAM Journal on Scientific Computing. 36(3). B427–B449. 7 indexed citations

2.

Lieberman, Chad & Karen Willcox. (2013). Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion. SIAM Review. 55(3). 493–519. 11 indexed citations

3.

Lieberman, Chad, Krzysztof Fidkowski, Karen Willcox, & Bart van Bloemen Waanders. (2012). Hessian‐based model reduction: large‐scale inversion and prediction. International Journal for Numerical Methods in Fluids. 71(2). 135–150. 12 indexed citations

4.

Lieberman, Chad & Karen Willcox. (2012). Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion. SIAM Journal on Scientific Computing. 34(4). A1880–A1904. 7 indexed citations

5.

Lieberman, Chad, Karen Willcox, & Omar Ghattas. (2010). Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems. SIAM Journal on Control and Optimization. 2 indexed citations

6.

Druskin, Vladimir, Chad Lieberman, & Mikhail Zaslavsky. (2010). On Adaptive Choice of Shifts in Rational Krylov Subspace Reduction of Evolutionary Problems. SIAM Journal on Control and Optimization. 2 indexed citations

7.

Druskin, Vladimir, Chad Lieberman, & Mikhail Zaslavsky. (2010). On Adaptive Choice of Shifts in Rational Krylov Subspace Reduction of Evolutionary Problems. SIAM Journal on Scientific Computing. 32(5). 2485–2496. 62 indexed citations

8.

Lieberman, Chad, Karen Willcox, & Omar Ghattas. (2010). Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems. SIAM Journal on Scientific Computing. 32(5). 2523–2542. 160 indexed citations

9.

Aristoff, Jeffrey M., et al.. (2006). Water bell and sheet instabilities. Physics of Fluids. 18(9). 10 indexed citations

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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