Afroz Mohiuddin
Impact in
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- Reinforcement Learning in Robotics
- Natural Language Processing Techniques
- Topic Modeling
- Domain Adaptation and Few-Shot Learning
- Artificial Intelligence in Games
Papers in
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- Reinforcement Learning in Robotics 2
- Natural Language Processing Techniques 1
- Evolutionary Algorithms and Applications 1
- Artificial Intelligence in Games 1
- Topic Modeling 1
- Adversarial Robustness in Machine Learning 1
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- Biomedical Text Mining and Ontologies 1
- Co-authors
- Błażej Osiński (1 shared paper)Henryk Michalewski (2 shared papers)Dumitru Erhan (1 shared paper)Łukasz Kaiser (2 shared papers)George Tucker (1 shared paper)Piotr Miłoś (1 shared paper)Piotr Kozakowski (2 shared papers)Chelsea Finn (1 shared paper)
- Journals
- Nature Communications (1 paper)International Conference on Learning Representations (1 paper)2022 International Joint Conference on Neural Networks (IJCNN) (1 paper)
- Partner nations
- United StatesPoland
In The Last Decade
Afroz Mohiuddin
3 papers receiving 34 citations
Peers
Comparison fields: 5 of 24
- Health Informatics 2
- Artificial Intelligence 26
- Issues, ethics and legal aspects 1
- Computer Vision and Pattern Recognition 7
- Automotive Engineering 4
Countries citing papers authored by Afroz Mohiuddin
This map shows the geographic impact of Afroz Mohiuddin'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 Afroz Mohiuddin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Afroz Mohiuddin more than expected).
Fields of papers citing papers by Afroz Mohiuddin
This network shows the impact of papers produced by Afroz Mohiuddin. 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 Afroz Mohiuddin. The network helps show where Afroz Mohiuddin may publish in the future.
Co-authors
The 18 scholars most cited alongside Afroz Mohiuddin, 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 | Model Based Reinforcement Learning for Atari | 2020 | 23 |
| 2 | 2022 | 10 | |
| 3 | 2022 | 2 |
About Afroz Mohiuddin
Afroz Mohiuddin is a scholar working on Artificial Intelligence, Molecular Biology, Management Science and Operations Research, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 35 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (2 papers), Natural Language Processing Techniques (1 paper), Evolutionary Algorithms and Applications (1 paper), Artificial Intelligence in Games (1 paper), Advanced Bandit Algorithms Research (1 paper), Biomedical Text Mining and Ontologies (1 paper), Topic Modeling (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (2 citations), Artificial Intelligence (26 citations), Issues, ethics and legal aspects (1 citation), Computer Vision and Pattern Recognition (7 citations) and Automotive Engineering (4 citations). Afroz Mohiuddin has collaborated with scholars based in United States and Poland. Frequent co-authors include Błażej Osiński, Henryk Michalewski, Dumitru Erhan, Łukasz Kaiser, George Tucker, Piotr Miłoś, Piotr Kozakowski, Chelsea Finn, Mohammad Babaeizadeh and Konrad Czechowski. Their work appears in journals such as Nature Communications, International Conference on Learning Representations and 2022 International Joint Conference on Neural Networks (IJCNN).
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.