Ishan Durugkar

705 total citations
9 papers, 117 citations indexed

About

Ishan Durugkar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Ishan Durugkar has authored 9 papers receiving a total of 117 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Management Science and Operations Research. Recurrent topics in Ishan Durugkar's work include Generative Adversarial Networks and Image Synthesis (2 papers), Topic Modeling (2 papers) and Reinforcement Learning in Robotics (2 papers). Ishan Durugkar is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (2 papers), Topic Modeling (2 papers) and Reinforcement Learning in Robotics (2 papers). Ishan Durugkar collaborates with scholars based in United States and United Kingdom. Ishan Durugkar's 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 and has published in prestigious journals such as arXiv (Cornell University), Neural Information Processing Systems and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Ishan Durugkar

8 papers receiving 117 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ishan Durugkar United States	5	73	29	21	14	14	9	117
Liam Li	2	65 0.9×	30 1.0×	10 0.5×	8 0.6×	12 0.9×	3	127
Jinwei Zhao China	6	40 0.5×	35 1.2×	19 0.9×	15 1.1×	10 0.7×	20	100
Tengyu Ma China	8	44 0.6×	17 0.6×	7 0.3×	15 1.1×	5 0.4×	21	103
Kavosh Asadi United States	5	192 2.6×	38 1.3×	11 0.5×	14 1.0×	15 1.1×	8	215
Prafulla Dhariwal	3	92 1.3×	54 1.9×	7 0.3×	6 0.4×	5 0.4×	3	127
Farzan Farnia United States	8	77 1.1×	40 1.4×	5 0.2×	6 0.4×	67 4.8×	19	162
Bettina Könighofer Austria	7	72 1.0×	11 0.4×	68 3.2×	25 1.8×	4 0.3×	15	134
Jérôme Durand-Lose France	6	49 0.7×	13 0.4×	54 2.6×	16 1.1×	17 1.2×	19	135
Nils Quetschlich Germany	7	130 1.8×	30 1.0×	37 1.8×	32 2.3×	33 2.4×	14	198
Jean‐François Dufourd France	8	52 0.7×	53 1.8×	33 1.6×	24 1.7×	6 0.4×	23	183

Countries citing papers authored by Ishan Durugkar

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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9 of 9 papers shown

1.

Durugkar, Ishan, et al.. (2020). An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch.. Neural Information Processing Systems. 33. 3917–3929. 2 indexed citations

2.

Durugkar, Ishan, et al.. (2020). Balancing Individual Preferences and Shared Objectives in Multiagent Reinforcement Learning. 2505–2511. 3 indexed citations

3.

Durugkar, Ishan & Peter Stone. (2018). Adversarial Goal Generation for Intrinsic Motivation. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1).

4.

Das, Rajarshi, Shehzaad Dhuliawala, Manzil Zaheer, et al.. (2017). Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning.. Neural Information Processing Systems. 4 indexed citations

5.

Das, Rajarshi, Shehzaad Dhuliawala, Manzil Zaheer, et al.. (2017). Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.. arXiv (Cornell University). 20 indexed citations

6.

Thomas, Philip S., Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, & Emma Brunskill. (2017). Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. Proceedings of the AAAI Conference on Artificial Intelligence. 31(2). 4740–4745. 9 indexed citations

7.

Parente, M., et al.. (2017). Unmixing in the presence of nuisances with deep generative models. 5189–5192. 1 indexed citations

8.

Durugkar, Ishan, et al.. (2016). Generative Multi-Adversarial Networks. arXiv (Cornell University). 25 indexed citations

9.

Kulkarni, Anand J., Ishan Durugkar, & Mrinal Kumar. (2013). Cohort Intelligence: A Self Supervised Learning Behavior. 1396–1400. 53 indexed citations

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.

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