Chad Lieberman
Impact in
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- Probabilistic and Robust Engineering Design
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- Model Reduction and Neural Networks
Papers in
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- Model Reduction and Neural Networks 5
- Scientific Research and Discoveries 1
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- Gaussian Processes and Bayesian Inference 4
- Co-authors
- Karen Willcox (6 shared papers)Omar Ghattas (2 shared papers)Mikhail Zaslavsky (2 shared papers)Vladimir Druskin (2 shared papers)Bart van Bloemen Waanders (1 shared paper)John W. M. Bush (1 shared paper)Krzysztof Fidkowski (1 shared paper)Jeffrey M. Aristoff (1 shared paper)
- Journals
- SIAM Journal on Scientific Computing (4 papers)SIAM Journal on Control and Optimization (2 papers)SIAM Review (1 paper)International Journal for Numerical Methods in Fluids (1 paper)Physics of Fluids (1 paper)
- Partner nations
- United States
In The Last Decade
Chad Lieberman
9 papers receiving 265 citations
Peers
Comparison fields: 5 of 47
- Statistics, Probability and Uncertainty 107
- Statistical and Nonlinear Physics 130
- Numerical Analysis 44
- Computational Theory and Mathematics 47
- Statistics and Probability 23
Countries citing papers authored by Chad Lieberman
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
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-authors
The 8 scholars most cited alongside Chad Lieberman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 169 | |
| 2 | 2010 | 62 | |
| 3 | 2012 | 12 | |
| 4 | 2013 | 12 | |
| 5 | 2006 | 10 | |
| 6 | 2014 | 8 | |
| 7 | 2012 | 7 | |
| 8 | On Adaptive Choice of Shifts in Rational Krylov Subspace Reduction of Evolutionary Problems | 2010 | 2 |
| 9 | Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems | 2010 | 2 |
About Chad Lieberman
Chad Lieberman is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Statistics, Probability and Uncertainty, Ocean Engineering and Computational Theory and Mathematics, having authored 9 papers that have together received 284 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (5 papers), Gaussian Processes and Bayesian Inference (4 papers), Probabilistic and Robust Engineering Design (3 papers), Reservoir Engineering and Simulation Methods (2 papers), Matrix Theory and Algorithms (2 papers), Scientific Research and Discoveries (1 paper), Advanced Numerical Methods in Computational Mathematics (1 paper) and Markov Chains and Monte Carlo Methods (1 paper). The work is most often cited by research in Statistics, Probability and Uncertainty (107 citations), Statistical and Nonlinear Physics (130 citations), Numerical Analysis (44 citations), Computational Theory and Mathematics (47 citations) and Statistics and Probability (23 citations). Chad Lieberman has collaborated with scholars based in United States. Frequent co-authors include Karen Willcox, Omar Ghattas, Mikhail Zaslavsky, Vladimir Druskin, Bart van Bloemen Waanders, John W. M. Bush, Krzysztof Fidkowski and Jeffrey M. Aristoff. Their work appears in journals such as SIAM Journal on Scientific Computing, SIAM Journal on Control and Optimization, SIAM Review, International Journal for Numerical Methods in Fluids and Physics of Fluids.
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.