Design and development of computational methods to facilitate the analysis of single-cell multi-omics and single-cell perturbational data
Amir Ali Moinfar (Ph.D. Student)
Single-cell omics allow us to study biological processes at single-cell resolution. The applications of single-cell omic studies range from understanding cellular processes to drug design. We need to design and develop computational models that can handle the complexity and magnitude of data generated by these studies. Machine learning models have revolutionized the way we analyze these data. However, there are still challenges to the transferability, reliability, and interpretability of the current models. My work focuses on designing novel deep-learning and machine-learning models to share and transfer knowledge between different single-cell studies. In addition, we aim to target other challenges such as interpretability and reliability at the same time.
|Primary Host:||Fabian Theis (Helmholtz Center Munich)|
|Exchange Host:||Gunnar Rätsch (ETH Zürich)|
|PhD Duration:||25 April 2022 - 25 April 2025|
|Exchange Duration:||01 June 2024 - 01 January 2025 - Ongoing|