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Deep learning applied to data augmentation and segmentation tasks in medical image analysis with a primary focus on skin lesion images. 

Weakly supervised object localization and counting, with a focus on agricultural applications.  

Explainable AI with a focus on medical imaging tasks.

Deep learning applied to fluorescence microscopy to help explain and quantify morphological changes to cellular domains, with a focus on discovering drug- and virus-induced biomarkers. 

Detection and segmentation of tumors in 3D PET/CT scans with domain knowledge inspired deep learning. 

Graph neural networks for super-resolution microscopy point cloud data analysis for understanding biological structures.

Super-resolution microscopy analysis of the endoplasmic reticulum. 

3D textured mesh analysis via deep learning for dermatology. 

Investigating a time-to-harvest application of deep neural nets for cannabis cultivation.