David Reichert
- Artificial Intelligence top 10%
- Biomedical Engineering
- Genetics
- Pulmonary and Respiratory Medicine
- Cognitive Neuroscience
- Co-authors
- Peggy SerièsAmos StorkeyGeorg WidhalmBarbara KieselAdelheid WöehrerRainer A. LeitgebLisa I. WadiuraJohanna Gesperger
- Topics
- Glioma Diagnosis and Treatment (9 papers)Nanoplatforms for cancer theranostics (9 papers)Photodynamic Therapy Research Studies (7 papers)
- Cited by
- GeneticsBiophysicsDermatology
- Partner nations
- AustriaUnited StatesUnited Kingdom
In The Last Decade
David Reichert
21 papers receiving 350 citations
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 109
- Biomedical Engineering 104
- Genetics 67
- Pulmonary and Respiratory Medicine 55
- Cognitive Neuroscience 54
Countries citing papers authored by David Reichert
This map shows the geographic impact of David Reichert'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 David Reichert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Reichert more than expected).
Fields of papers citing papers by David Reichert
This network shows the impact of papers produced by David Reichert. 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 David Reichert. The network helps show where David Reichert may publish in the future.
Co-authorship network of co-authors of David Reichert
This figure shows the co-authorship network connecting the top 25 collaborators of David Reichert. A scholar is included among the top collaborators of David Reichert 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 David Reichert. David Reichert is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 11 | |
| 5 | 3 | |
| 6 | 4 | |
| 7 | 44 | |
| 8 | 17 | |
| 9 | 5 | |
| 10 | Automated curriculum generation through setter-solver interactions | 7 |
| 11 | 13 | |
| 12 | 29 | |
| 13 | Deep reinforcement learning with relational inductive biases | 47 |
| 14 | Learning Dynamic State Abstractions for Model-Based Reinforcement Learning | 1 |
| 15 | The predictron: end-to-end learning and planning | 25 |
| 16 | Imagination-Augmented Agents for Deep Reinforcement Learning | 49 |
| 17 | 38 | |
| 18 | Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability | 5 |
| 19 | Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model | 13 |
| 20 | 35 |
About David Reichert
David Reichert is a scholar working on Genetics, Biophysics and Biomedical Engineering, having authored 24 papers that have together received 370 indexed citations. Recurring topics across this work include Glioma Diagnosis and Treatment (9 papers), Nanoplatforms for cancer theranostics (9 papers) and Photodynamic Therapy Research Studies (7 papers). The work is most often cited by research in Genetics (67 citations), Biophysics (33 citations) and Dermatology (34 citations). David Reichert has collaborated with scholars based in Austria, United States and United Kingdom. Frequent co-authors include Peggy Seriès, Amos Storkey, Georg Widhalm, Barbara Kiesel, Adelheid Wöehrer, Rainer A. Leitgeb, Lisa I. Wadiura, Johanna Gesperger, Arthur Guez and Marco Andreana. Their work appears in journals such as Scientific Reports, Journal of neurosurgery and PLoS Computational Biology.
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.