Scholar
Edwin D. de Jong
Google Scholar ID: l9w80gcAAAAJ
Principal ML Scientist at Aignostics
Citations & Impact
Citations
469
H-index
11
i10-index
11
Publications
20
Co-authors
16
list available
Publications
Resume (English only)
Academic Achievements
- Published 'Enhancing Pathology Foundation Models with Transcriptomics' and presented at the Genomics England Research Summit 2024; Participated in the 'Towards Large-Scale Training of Pathology Foundation Models' project; Published breast cancer detection research in The Lancet; Presented a paper on incremental sequence learning at NIPS 2016 Workshop.
Research Experience
- Researcher at Kaiko.ai, responsible for training, evaluating, and applying medical foundation models; Improved breast cancer detection product performance with the research team at ScreenPoint Medical; Conducted postdoctoral research at the DEMO Lab.
Education
- PhD from VUB AI Lab; Postdoc at Prof. Jordan Pollack's DEMO Lab at Brandeis University.
Background
- A medical AI researcher with extensive experience who actively contributes to the AI revolution in healthcare that is currently taking place. At Kaiko.ai, he trains, evaluates, and applies medical foundation models. With the research team at ScreenPoint Medical, he improved the breast cancer detection performance of ScreenPoint's Transpara product. One of the areas he focused on is measuring and improving the robustness of medical AI models.
Miscellany
- Personal interests include enhancing pathology foundation models with RNA data, large-scale training of pathology foundation models, analyzing the robustness of foundation models, and incremental sequence learning.
