My area of research is AI, and particularly, my research concentrates on understanding and demystifying deep learning. I work to improve the security, transparency, and generality of deep neural networks, with a focus on applications in computer vision and data privacy. My work fits primarily under the sub-fields of explainable AI and ML security. Explainable AI aims to bring interpretability and transparency to otherwise opaque deep learning methods, giving us a richer understanding of their inner workings. ML security addresses concerns including attacks that compromise data privacy and that fool even state-of-the-art models. Currently, I am most interested in topics with three major themes; namely, explaining black-box neural network behavior, creating a theory of network generalization, and designing generative models for high-level feature interpretation and counterfactual reasoning.
Explaining Black-box Neural Network Behavior
In the recent years, deep neural networks have become increasingly powerful at tasks previously only humans had mastered. Deep learning has become widely used, and while it has many practitioners, its inner workings are far from well-understood. As the application of ML has increased, so has the need for algorithmic transparency, the ability to understand why algorithms deployed in the real world make the decisions they do. Much of my work has addressed the problem of determining which aspects of a network influence particular decisions, in addition to interpreting the identified influential components. Influence can be used to increase model trust, to uncover insights discovered by ML models, and as a building block for debugging arbitrary network behavior.
A Theory of Network Generalization
Despite having the capacity to significantly overfit, or moreover, memorize the training data, deep neural networks demonstrate an ability to generalize reasonably well in practice. Present hypotheses have failed to explain why this is the case. In fact, it is not well understood how exactly overfitting is manifested in a model. One aspect of my work tries to understand what phenomena give rise to misclassifications, overfitting, and bias in DNNs. Understanding the causes for these problems will also shed light on what leads models to generalize; and may suggest ways of improving generalization. Furthermore, as overfitting presents a threat to the security of a model, understanding overfitting more fundamentally may help protect the privacy of the data involved in training a model, and improve the model's robustness to adversarial manipulation. I develop explanations for these problems that have direct applications to membership inference, misclassification prediction, and bias amplification.
It is often difficult to interpret the high-level concepts learned by neural networks. I am interested in the use of generative models to explore the semantic space of deep networks. This would enable interpreting features that cannot be easily understood via their existence in a single instance. Furthermore, it may allow automated, interpretable counterfactual reasoning for DNNs.