I am interested in all aspects of learning from data: computational, inferential, decisional, and mathematical. Learning from data helps addressing scientific questions from ecology to neuroscience, and tackling technical challenges to build AI technology. 

I have recently been working on learning under distributional shift, and more broadly on learning to transform data.  I have also been working on developing new tools to address trade-off challenges arising from upscaling machine learning and AI.