Amgen Scholars: Announcements of Opportunity
Below are Announcements of Opportunity posted by Caltech faculty for the Amgen Scholars program.
Announcements of Opportunity are posted as they are received. Please check back regularly for new AO submissions! Remember: This is just one way that you can go about identifying a suitable project and/or mentor. For additional tips on identifying a mentor click here.
Please remember:
- Students pursuing Amgen must be U.S. citizens, U.S. permanent residents, or students with DACA status.
- Students pursuing Amgen must complete the 10-week program from June 18 - August 23, 2024. Students must commit to these dates. No exceptions will be made.
- Accepted students must live in provided Caltech housing.
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| Project: | Machine learning for multimodal multiscale cancer image analysis | ||||||||
| Disciplines: | Computer Science, Engineering, Mathematics, Data Science, Bioinformatics, Physics | ||||||||
| Mentor: |
Xubo Song,
Professor, (EAS),
songx@ohsu.edu, |
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| Mentor URL: | https://www.ohsu.edu/people/xubo-song-phd (opens in new window) | ||||||||
| Background: |
NOTE: This project is being offered by a Caltech alum and is open only to Caltech students. The project will be conducted at Oregon Health and Science University in Portland, Oregon. Microscopic imaging is an important tool to study cancer, as it can reveal cellular or molecular structures, spatial relations, cell-cell interactions, and the tissue microenvironment, and shed light on the mechanism of cancer initiation and progression. Such imaging modalities including histopathology imaging, immunofluorescent imaging, and light and electron microscopy imaging. The rate-limiting factor in utilizing these imaging technologies is automated processing and analysis tools for image quality harmonization, image characterization, biomarker discovery, and modeling of the spatial relations of cellular structures and temporal changes. This will require development of techniques for tasks such as image normalization, enhancement, segmentation, feature extraction, clustering, classification, using machine learning and computer vision methods. The student will be developing machine learning algorithms for the above-mentioned tasks. |
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| Description: | The student will work with the mentor to define the topic and scope of the project appropriate for the student’s skill level and the internship time frame. With the mentor’s guidance, the student will explore the algorithms relevant for the project, implement them or adapt existing tools to the specific cancer imaging data. | ||||||||
| References: |
https://arxiv.org/abs/2211.07804 https://arxiv.org/abs/2312.07353 https://arxiv.org/pdf/2304.12210.pdf |
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| Student Requirements: | Proficiency in Python, knowledge of machine learning platforms such as Scikit-learn and PyTorch; Knowledge of machine learning or computer vision; Interest in biomedical imaging; Interest in exploring and implementing advanced analysis techniques for cancer imaging data. | ||||||||
| Programs: |
This AO can be done under the following programs:
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