T. Madams
Impact in
- Health Informatics top 0.05%
- Artificial Intelligence in Healthcare and Education
- Ophthalmology top 0.2%
- Retinal Diseases and Treatments
- Retinal and Optic Conditions
Papers in
-
- Magnetic confinement fusion research 4
-
- Ionosphere and magnetosphere dynamics 2
- Solar and Space Plasma Dynamics 1
- Co-authors
- Dale R. WebsterDerek WuSubhashini VenugopalanArunachalam NarayanaswamyMartin C. StumpeKim RamasamyJorge CuadrosPhilip Nelson
- Journals
- JAMA (1 paper)Physics of Plasmas (1 paper)APS Division of Plasma Physics Meeting Abstracts (1 paper)International Conference on Learning Representations (1 paper)Bulletin of the American Physical Society (2 papers)
- Partner nations
- United StatesIndia
In The Last Decade
T. Madams
6 papers receiving 4.3k citations
Hit Papers
Peers
Comparison fields: 5 of 161
- Health Informatics 627
- Ophthalmology 1.4k
- Health Information Management 566
- Radiology, Nuclear Medicine and Imaging 2.8k
- Artificial Intelligence 1.1k
Countries citing papers authored by T. Madams
This map shows the geographic impact of T. Madams'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 T. Madams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Madams more than expected).
Fields of papers citing papers by T. Madams
This network shows the impact of papers produced by T. Madams. 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 T. Madams. The network helps show where T. Madams may publish in the future.
Co-authorship network
The 25 scholars most cited alongside T. Madams, 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 | 2021 | 4 | |
| 2 | High-fidelity Bayesian inference of transient FRC plasma perturbations in C-2W | 2019 | 1 |
| 3 | Minigo: A Case Study in Reproducing Reinforcement Learning Research. | 2019 | 7 |
| 4 | Application of Bayesian inference for reconstruction of FRC plasma state in C-2W | 2018 | 1 |
| 5 | Reconstruction of fusion plasma state with a Plasma Debugger | 2018 | 1 |
| 6 | Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs Hit paper breakdown → | 2016 | 4490 |
About T. Madams
T. Madams is a scholar working on Nuclear and High Energy Physics, Astronomy and Astrophysics, Radiation, Ophthalmology and Radiology, Nuclear Medicine and Imaging, having authored 6 papers that have together received 4.5k indexed citations. Recurring topics across this work include Magnetic confinement fusion research (4 papers), Ionosphere and magnetosphere dynamics (2 papers), Artificial Intelligence in Games (1 paper), Nuclear Physics and Applications (1 paper), Retinal Imaging and Analysis (1 paper), Retinal Diseases and Treatments (1 paper), Retinal and Optic Conditions (1 paper) and Solar and Space Plasma Dynamics (1 paper). The work is most often cited by research in Health Informatics (627 citations), Ophthalmology (1.4k citations), Health Information Management (566 citations), Radiology, Nuclear Medicine and Imaging (2.8k citations) and Artificial Intelligence (1.1k citations). T. Madams has collaborated with scholars based in United States and India. Frequent co-authors include Dale R. Webster, Derek Wu, Subhashini Venugopalan, Arunachalam Narayanaswamy, Martin C. Stumpe, Kim Ramasamy, Jorge Cuadros, Philip Nelson, Lily Peng and Marc Coram. Their work appears in journals such as JAMA, Physics of Plasmas, APS Division of Plasma Physics Meeting Abstracts, International Conference on Learning Representations and Bulletin of the American Physical Society.
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.