Ishan Durugkar
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Co-authors
- Mrinal KumarAnand J. KulkarniSridhar MahadevanLuke VilnisAkshay KrishnamurthyRajarshi DasAlexander J. SmolaAndrew McCallum
- Topics
- Generative Adversarial Networks and Image Synthesis (2 papers)Topic Modeling (2 papers)Reinforcement Learning in Robotics (2 papers)
- Journals
- arXiv (Cornell University)Neural Information Processing SystemsProceedings of the AAAI Conference on Artificial Intelligence
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Ishan Durugkar
8 papers receiving 117 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 73
- Computer Vision and Pattern Recognition 29
- Computational Theory and Mathematics 21
- Control and Systems Engineering 14
- Electrical and Electronic Engineering 14
Countries citing papers authored by Ishan Durugkar
This map shows the geographic impact of Ishan Durugkar'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 Ishan Durugkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ishan Durugkar more than expected).
Fields of papers citing papers by Ishan Durugkar
This network shows the impact of papers produced by Ishan Durugkar. 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 Ishan Durugkar. The network helps show where Ishan Durugkar may publish in the future.
Co-authorship network of co-authors of Ishan Durugkar
This figure shows the co-authorship network connecting the top 25 collaborators of Ishan Durugkar. A scholar is included among the top collaborators of Ishan Durugkar 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 Ishan Durugkar. Ishan Durugkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch. | 2 |
| 2 | 3 | |
| 3 | 0 | |
| 4 | Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning. | 4 |
| 5 | Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. | 20 |
| 6 | 9 | |
| 7 | 1 | |
| 8 | 25 | |
| 9 | 53 |
About Ishan Durugkar
Ishan Durugkar is a scholar working on Computer Graphics and Computer-Aided Design, Artificial Intelligence and Management Science and Operations Research, having authored 9 papers that have together received 117 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (2 papers), Topic Modeling (2 papers) and Reinforcement Learning in Robotics (2 papers). The work is most often cited by research in Artificial Intelligence (73 citations), Computational Mathematics (1 citation) and Computer Vision and Pattern Recognition (29 citations). Ishan Durugkar has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Mrinal Kumar, Anand J. Kulkarni, Sridhar Mahadevan, Luke Vilnis, Akshay Krishnamurthy, Rajarshi Das, Alexander J. Smola, Andrew McCallum, Manzil Zaheer and Shehzaad Dhuliawala. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and Proceedings of the AAAI Conference on Artificial Intelligence.
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.