AIRC Team

I am a Ph.D. Student in Computer Science at ML Labs based at TU Dublin. My research falls under the broad topic of Uncertainty and Robustness in Machine Learning. There has been growing interest in ensuring that deep learning systems are robust and reliable. The dynamic nature of behavior and the physical world necessitate assessing uncertainty and adapting to changing environments for intelligent systems. Likewise, a big challenge for current deep learning models is the generalization to unseen and worst-case inputs and being robust to distributional shift. Being robust and reliably assessing uncertainty is critical to safely deploy deep learning models in open environments for many machine learning applications, such as self-driving vehicles, medical diagnosis systems, and safety-critical systems. My research investigates key applications of robust and uncertainty-aware deep learning (e.g., computer vision, robotics, self-driving vehicles, medical imaging), as well as broader machine learning tasks to deepen technical understanding of (1) mechanisms to estimate and calibrate confidence produced by neural networks in typical and unforeseen scenarios; (2) guide learning towards an understanding of the underlying causal mechanisms for improving robustness and generalization and enforcing distributional invariances.