VPUE Stipend for Undergrad Research (Summer Quarter) - Single Cell Analysis and Deep Learning
VPUE Stipend for Undergrad Research - Single Cell Analysis and Deep Learning
This opportunity offers a 10 week research project supported by a VPUE stipend [1] at the intersection of CyTOF-based single cell analysis and deep learning at the Aghaeepour Lab for Artificial Intelligence, Machine Learning, and Multiomics Integration for Translational Medicine [2].
Modern technologies allow characterization of millions of individual cells per patient, and provide for an accurate understanding of complex biological systems by providing insights into cellular heterogeneity and novel cellular subsets. However, these high-dimensional data are traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through this manual process. This can adversely affect downstream analyses and predictions. To address this, deep learning methods have shown potential to directly work on single cell data to produce highly accurate predictions in application scenarios like diagnosing the latent cytomegalovirus (CMV) in healthy individuals. Nevertheless, these approaches are only recently emerging and it is necessary to assess their performance on the multitude of available problem settings.
In this project, the goal is to evaluate the performance of deep learning methods for single-cell data in multiple predictive settings. This includes existing algorithms as well as novel approaches based on geometric deep learning. Tasks for evaluation include pregnancy related settings like preterm and preeclampsia prediction, healing processes, and more. Possible extensions of the project are novel, task-specific visualization algorithms as well as the integration of background knowledge for better predictive performance and deeper insights into the underlying biology.
Throughout the project, the student will learn to work in a rigorous scientific manner by assessing the performance of machine learning and artificial intelligence methods, and how to effectively transfer novel algorithms between fields. This will heavily focus on state-of-the-art deep learning methodology as well as cutting-edge single-cell data covering a multitude of high-impact medical applications.
Recommended Profile: Curiosity as well as willingness and dedication to learn are the most important requirements. Optionally, programming skills in Python and previous experience with deep learning using PyTorch and Tensorflow would be helpful.
Note: The computational nature of the project allows for a fully remote project in case of pandemic related restrictions.
Deadline: Please contact us early as possible, we will start interviewing candidates as early as April 14
Lab: Aghaeepour Lab for Artificial Intelligence, Machine Learning, and Multiomics Integration for Translational Medicine
Department of Anesthesiology, Perioperative and Pain Medicine
Stanford Medicine
Mentors:
- Martin Becker (mgbckr@stanford.edu)
- Nima Aghaeepour (naghaeep@stanford.edu)
Links:
- Apply state-of-the-art deep learning and graph neural networks
- Get to know and work with cutting-edge single-cell CyTOF data
- Learn to work in a rigorous scientific manner
Eligibility: Current Stanford undergraduate student. For more information on eligibility, see: https://undergrad.stanford.edu/opportunities/research/information-faculty-staff/faculty-department-grant-administration
Requirements: Curiosity as well as willingness and dedication to learn are the most important requirements. Optionally, programming skills in Python and previous experience with deep learning using PyTorch and Tensorflow would be helpful.
