Advancing machine learning research
Our machine learning methods are inspired by challenges that arise when we deal with real-world data in which conventional machine learning assumptions are not satisfied. In real-world applications such as biomedicine, datasets often originate from different experiments. Thus, train and test data do not need to come from the same distribution or even from a single learning task. In addition, new categories that have never been encountered during training can appear during model deployment. We design methods that can generalize to new learning tasks with minimal supervision, e.g., only a few labeled training examples, or even fully unsupervised. Our research directions can be broadly categorized in the areas of meta-learning and few-shot learning, unsupervised learning, semi-supervised learning, open-world and open-set learning, domain adaptation and domain generalization, graph representation learning, explainable AI.