I am a PhD student in the Image and Video Computing Laboratory at Boston University, working under the supervision of Professor Kate Saenko. My current research is in the field of Explainable Artificial Intelligence. In particular, I am interested in explainable Computer Vision models.
Before my PhD I have studied Computer Science at the Faculty of Applied Mathematics and Computer Science, Belarusian State University. There I have worked on graph theory and lung image segmentation.
Black-box explanation of object detectors via saliency maps
RISE: Randomized Input Sampling for Explanation of Black-box Models
Our paper Black-box explanation of object detectors via saliency maps has been accepted to CVPR'2021 as an oral!
Our paper Guided Zoom: Zooming into Network Evidence to Refine Fine-grained Model Decisions has been accepted to TPAMI.
I will be returnning to Adobe for a Summer internship in Machine Learning.
Check out great practicum series on interpretable machine learning at MIT that I am a part of.
Our paper Guided Zoom: Questioning Network Evidence for Fine-grained Classification has been accepted to BMVC'2019 as an oral!
I am excited to spend the coming summer as a Machine Learning Intern at Adobe working on Explainable AI.
Our paper RISE: Randomized Input Sampling for Explanation of Black-box Models has been accepted to BMVC'2018 as an oral!