Aditya V. Nori
- Software top 0.5%
- Software Testing and Debugging Techniques 26
- Software Reliability and Analysis Research 19
- Information Systems top 1%
- Software Engineering Research 19
- Hardware and Architecture top 5%
- Signal Processing top 2%
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- Formal Methods in Verification 10
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- Bayesian Modeling and Causal Inference 7
- Logic, programming, and type systems 7
- Machine Learning and Algorithms 6
- Topic Modeling 3
- Co-authors
- Sriram K. RajamaniKapil VaswaniTrishul ChilimbiThomas A. HenzingerAndrew D. GordonNels E. BeckmanPatrice GodefroidBen Liblit
- Journals
- IEEE Transactions on Information Theory (1 paper)IEEE Transactions on Software Engineering (1 paper)IEEE Software (1 paper)
- Partner nations
- United StatesIndiaUnited Kingdom
In The Last Decade
Aditya V. Nori
45 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 57
- Software 901
- Information Systems 642
- Hardware and Architecture 174
- Signal Processing 271
- Computational Theory and Mathematics 319
Countries citing papers authored by Aditya V. Nori
This map shows the geographic impact of Aditya V. Nori'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 Aditya V. Nori with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aditya V. Nori more than expected).
Fields of papers citing papers by Aditya V. Nori
This network shows the impact of papers produced by Aditya V. Nori. 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 Aditya V. Nori. The network helps show where Aditya V. Nori may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Aditya V. Nori, 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 | 2015 | 15 | |
| 2 | 2015 | 43 | |
| 3 | 2014 | 16 | |
| 4 | 2014 | 17 | |
| 5 | Efficiently Sampling Probabilistic Programs via Program Analysis | 2013 | 23 |
| 6 | Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages | 2013 | 7 |
| 7 | 2013 | 41 | |
| 8 | 2012 | 1 | |
| 9 | 2011 | 8 | |
| 10 | 2010 | 15 | |
| 11 | 2010 | 1 | |
| 12 | 2010 | 16 | |
| 13 | 2009 | 197 | |
| 14 | 2009 | 107 | |
| 15 | 2009 | 19 | |
| 16 | 2008 | 63 | |
| 17 | 2007 | 36 | |
| 18 | 2007 | 36 | |
| 19 | 2007 | 5 | |
| 20 | A Technique for Model-Based Testing of Classes | 2001 | 2 |
About Aditya V. Nori
Aditya V. Nori is a scholar working on Software, Information Systems and Artificial Intelligence, having authored 45 papers that have together received 1.4k indexed citations. Recurring topics across this work include Software Testing and Debugging Techniques (26 papers), Software Reliability and Analysis Research (19 papers), Software Engineering Research (19 papers), Formal Methods in Verification (10 papers), Bayesian Modeling and Causal Inference (7 papers), Logic, programming, and type systems (7 papers), Machine Learning and Algorithms (6 papers) and Topic Modeling (3 papers). The work is most often cited by research in Software (901 citations), Information Systems (642 citations) and Hardware and Architecture (174 citations). Aditya V. Nori has collaborated with scholars based in United States, India and United Kingdom. Frequent co-authors include Sriram K. Rajamani, Kapil Vaswani, Trishul Chilimbi, Thomas A. Henzinger, Andrew D. Gordon, Nels E. Beckman, Patrice Godefroid, Ben Liblit, Sai Deep Tetali and Anindya Banerjee. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Software Engineering and IEEE Software.
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.