Rohit Tripathy
Impact in
-
- Probabilistic and Robust Engineering Design
-
- Model Reduction and Neural Networks
Papers in
-
- Genomics and Chromatin Dynamics 2
- Gene expression and cancer classification 2
- Epigenetics and DNA Methylation 1
-
- Gaussian Processes and Bayesian Inference 2
- Co-authors
- Ilias Bilionis (3 shared papers)Marcial Gonzalez (1 shared paper)Jitesh H. Panchal (1 shared paper)Amber Tang (1 shared paper)Peter K. Koo (2 shared papers)Gregory A. Cary (1 shared paper)Antonio Majdandzic (1 shared paper)Gregory W. Carter (1 shared paper)
- Journals
- Journal of Computational Physics (3 papers)npj Systems Biology and Applications (1 paper)Briefings in Bioinformatics (1 paper)Applied Mechanics and Materials (1 paper)PubMed (1 paper)
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Rohit Tripathy
6 papers receiving 592 citations
Rohit Tripathy's Hit Papers
Peers
Comparison fields: 5 of 74
- Statistics, Probability and Uncertainty 254
- Statistical and Nonlinear Physics 246
- Computational Theory and Mathematics 111
- Computational Mathematics 3
- Computational Mechanics 93
Countries citing papers authored by Rohit Tripathy
This map shows the geographic impact of Rohit Tripathy'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 Rohit Tripathy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rohit Tripathy more than expected).
Fields of papers citing papers by Rohit Tripathy
This network shows the impact of papers produced by Rohit Tripathy. 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 Rohit Tripathy. The network helps show where Rohit Tripathy may publish in the future.
Co-authors
The 13 scholars most cited alongside Rohit Tripathy, 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 | Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification Hit paper breakdown → | 2018 | 331 |
| 2 | 2016 | 143 | |
| 3 | 2019 | 118 | |
| 4 | 2025 | 6 | |
| 5 | Selecting deep neural networks that yield consistent attribution-based interpretations for genomics. | 2022 | 5 |
| 6 | 2022 | 2 | |
| 7 | 2014 | 1 |
About Rohit Tripathy
Rohit Tripathy is a scholar working on Molecular Biology, Artificial Intelligence, Statistics, Probability and Uncertainty, Statistical and Nonlinear Physics and Computational Theory and Mathematics, having authored 7 papers that have together received 606 indexed citations. Recurring topics across this work include Probabilistic and Robust Engineering Design (3 papers), Genomics and Chromatin Dynamics (2 papers), Gene expression and cancer classification (2 papers), Advanced Multi-Objective Optimization Algorithms (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Model Reduction and Neural Networks (2 papers), Geothermal Energy Systems and Applications (1 paper) and Epigenetics and DNA Methylation (1 paper). The work is most often cited by research in Statistics, Probability and Uncertainty (254 citations), Statistical and Nonlinear Physics (246 citations), Computational Theory and Mathematics (111 citations), Computational Mathematics (3 citations) and Computational Mechanics (93 citations). Rohit Tripathy has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Ilias Bilionis, Marcial Gonzalez, Jitesh H. Panchal, Amber Tang, Peter K. Koo, Gregory A. Cary, Antonio Majdandzic, Gregory W. Carter, Hong Wang and Jesse Gillis. Their work appears in journals such as Journal of Computational Physics, npj Systems Biology and Applications, Briefings in Bioinformatics, Applied Mechanics and Materials and PubMed.
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.