Jeevana Priya Inala
- Information Systems top 10%
- Software top 5%
- Artificial Intelligence
- Computational Mechanics
- Computer Vision and Pattern Recognition
- Co-authors
- Shuvendu K. LahiriSiddhartha SenCaroline LemieuxArmando Solar-LezamaWojciech MatusikAndrew SpielbergAdriana SchulzDaniela Rus
- Topics
- Software Engineering Research (3 papers)Reinforcement Learning in Robotics (2 papers)Modular Robots and Swarm Intelligence (1 paper)
- Journals
- ACM Transactions on Graphics2021 IEEE/CVF International Conference on Computer Vision (ICCV)arXiv (Cornell University)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
Jeevana Priya Inala
8 papers receiving 307 citations
Hit Papers
Peers
Comparison fields: 5 of 48
- Information Systems 102
- Software 95
- Artificial Intelligence 81
- Computational Mechanics 56
- Computer Vision and Pattern Recognition 50
Countries citing papers authored by Jeevana Priya Inala
This map shows the geographic impact of Jeevana Priya Inala'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 Jeevana Priya Inala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeevana Priya Inala more than expected).
Fields of papers citing papers by Jeevana Priya Inala
This network shows the impact of papers produced by Jeevana Priya Inala. 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 Jeevana Priya Inala. The network helps show where Jeevana Priya Inala may publish in the future.
Co-authorship network of co-authors of Jeevana Priya Inala
This figure shows the co-authorship network connecting the top 25 collaborators of Jeevana Priya Inala. A scholar is included among the top collaborators of Jeevana Priya Inala 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 Jeevana Priya Inala. Jeevana Priya Inala is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 27 | |
| 2 | CodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Modelsbreakdown → | 146 |
| 3 | 0 | |
| 4 | 21 | |
| 5 | 5 | |
| 6 | Synthesizing Programmatic Policies that Inductively Generalize | 5 |
| 7 | 10 | |
| 8 | 77 | |
| 9 | 27 |
About Jeevana Priya Inala
Jeevana Priya Inala is a scholar working on Computer Graphics and Computer-Aided Design, Software and Hardware and Architecture, having authored 9 papers that have together received 318 indexed citations. Recurring topics across this work include Software Engineering Research (3 papers), Reinforcement Learning in Robotics (2 papers) and Modular Robots and Swarm Intelligence (1 paper). The work is most often cited by research in Software (95 citations), Computer Graphics and Computer-Aided Design (28 citations) and Information Systems (102 citations). Jeevana Priya Inala has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Shuvendu K. Lahiri, Siddhartha Sen, Caroline Lemieux, Armando Solar-Lezama, Wojciech Matusik, Andrew Spielberg, Adriana Schulz, Daniela Rus, Tao Du and Yewen Pu. Their work appears in journals such as ACM Transactions on Graphics, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and arXiv (Cornell University).
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.