Mohit Sharma
- Information Systems top 5%
- Recommender Systems and Techniques 3
- Signal Processing top 10%
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics 4
- Sociology and Political Science top 10%
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- Semiconductor materials and devices 5
- Advanced Wireless Communication Technologies 4
- Advancements in Semiconductor Devices and Circuit Design 4
- PAPR reduction in OFDM 4
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- Robot Manipulation and Learning 4
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- Nanowire Synthesis and Applications 3
- Co-authors
- Shivangi SinghalRajiv Ratn ShahTanmoy ChakrabortyAnubha KabraPonnurangam KumaraguruАрун КумарKanika SharmaParikshit Sondhi
- Journals
- Journal of The Electrochemical Society (1 paper)The International Journal of Robotics Research (1 paper)IEEE Transactions on Nanotechnology (1 paper)
- Partner nations
- IndiaUnited StatesUnited Arab Emirates
In The Last Decade
Mohit Sharma
36 papers receiving 389 citations
Peers
Comparison fields: 5 of 71
- Information Systems 147
- Signal Processing 60
- Artificial Intelligence 142
- Computer Vision and Pattern Recognition 73
- Sociology and Political Science 132
Countries citing papers authored by Mohit Sharma
This map shows the geographic impact of Mohit Sharma'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 Mohit Sharma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohit Sharma more than expected).
Fields of papers citing papers by Mohit Sharma
This network shows the impact of papers produced by Mohit Sharma. 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 Mohit Sharma. The network helps show where Mohit Sharma may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mohit Sharma, 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 | 2025 | 0 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 1 | |
| 4 | 2024 | 33 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 1 | |
| 7 | 2023 | 6 | |
| 8 | 2021 | 4 | |
| 9 | 2020 | 134 | |
| 10 | 2019 | 14 | |
| 11 | 2019 | 5 | |
| 12 | Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information. | 2018 | 6 |
| 13 | 2018 | 5 | |
| 14 | 2018 | 1 | |
| 15 | 2018 | 3 | |
| 16 | 2017 | 19 | |
| 17 | 2016 | 2 | |
| 18 | 2016 | 5 | |
| 19 | 2015 | 6 | |
| 20 | 2002 | 2 |
About Mohit Sharma
Mohit Sharma is a scholar working on Information Systems, Signal Processing and Computer Networks and Communications, having authored 42 papers that have together received 402 indexed citations. Recurring topics across this work include Semiconductor materials and devices (5 papers), Robot Manipulation and Learning (4 papers), Advanced Wireless Communication Technologies (4 papers), Reinforcement Learning in Robotics (4 papers), Advancements in Semiconductor Devices and Circuit Design (4 papers), PAPR reduction in OFDM (4 papers), Recommender Systems and Techniques (3 papers) and Nanowire Synthesis and Applications (3 papers). The work is most often cited by research in Information Systems (147 citations), Signal Processing (60 citations) and Artificial Intelligence (142 citations). Mohit Sharma has collaborated with scholars based in India, United States and United Arab Emirates. Frequent co-authors include Shivangi Singhal, Rajiv Ratn Shah, Tanmoy Chakraborty, Anubha Kabra, Ponnurangam Kumaraguru, Арун Кумар, Kanika Sharma, Parikshit Sondhi, ChengXiang Zhai and Pranam Kolari. Their work appears in journals such as Journal of The Electrochemical Society, The International Journal of Robotics Research and IEEE Transactions on Nanotechnology.
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.