My name is Donald Loveland and I am a first year PhD student in the CSE department at University of Michigan, Ann Arbor, advised by Danai Koutra. My work is supported by a Rackham Merit Fellowship award.


My research focuses on improving the interpretability, robustness, and fairness of graph neural networks with hopes to mitigate harmful bias and identify practical limitations of current methods.

Previously, I was a machine learning research scientist at Lawrence Livermore National Lab where I broadly worked on graph deep learning applications. Some of my specific research included graph classification/regression for molecules, inverse design of molecular graphs, post-hoc insight extraction for graph neural networks (GNNs), and adversarial training for GNNs.

In the past, I have also worked heavily in the computer vision space developing methods to extract actionable insights from convolution neural networks through generative counterfactual models.

Before I was a researcher, I worked as a software engineer at Chevron where I developed an automated API based deployment strategy for machine learning applications to Azure.


As the first member of my family to go to college, I am grateful for the opportunities I have recieved during both my academic and professional career. While I am still figuring out how to properly navigate the world of academia, I have worked to make myself a resource for others who come from similar backgrounds. During my undergraduate degree, I acted as a peer mentor for younger first generation students and have continued these efforts by participating in undergraduate mentoring during my graduate studies. While at Lawrence Livermore, I also helped facilitate a two week long machine learning challenge geared towards teaching students how to perform applied research. I am always happy to help others who are looking for advice, please feel free to reach out!