Throughout my academic career, I’ve been fortunate to contribute to a wide variety of research directions, including graph representation learning, computational biology, medical imaging, cross-modal learning, reinforcement learning, and deep learning on low-power/embedded systems. A detailed list of relevant publications is provided below.
Currently, my main research interests are on graph representation learning for algorithmic reasoning. This new and exciting direction seeks to understand and employ the expressive power of GNNs for modelling classical algorithms. The implications are potentially massive: enabling scalable solutions to tasks such as meta-learning and search, aiding novel discoveries in theoretical computer science, and rigorously benchmarking GNNs. For more details, you may consult these papers, and my talk on the area.
I would be happy to discuss any project idea at the intersection of the above areas; feel free to reach out if you are interested!