Research:

• Perception in-the-Wild: Advancing visual understanding for robust place recognition and open-vocabulary object detection in challenging, unconstrained environments.
• Neural Time Series Analysis: Developing novel deep learning methods for insightful analysis of complex multi-modal time series data, including zero-shot learning applications.
• Uncertainty Estimation of Deep Learning Models: Investigating methods to rigorously quantify and calibrate prediction uncertainty in deep learning, enhancing model reliability for critical applications.
• Deep Learning for Genomics/Proteomics: Applying and developing deep learning models to accelerate discovery and analysis in the fields of genomics and proteomics.