Debora Sujono
deborausujono -at- gmail -dot- com
ABOUT
I am a PhD candidate in Computer Science at the University of California, Irvine, advised by Prof. Erik Sudderth. My research interests are in variational inference and probabilistic programming languages. Previously, I worked as a junior data scientist at MassMutual. I received my MS in Computer Science from the University of Massachusetts, Amherst, where I worked with Prof. Daniel Sheldon, and my BA in Computer Science and Linguistics from Hampshire College. Outside of research, I enjoy yoga, rock climbing, and traveling to tropical diving destinations.
PUBLICATIONS
Variational Inference for Soil Biogeochemical Models. Debora Sujono, Hua Wally Xie, Steven Allison, and Erik Sudderth. In AI for Science Workshop, International Conference on Machine Learning (ICML), 2022.
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference. Geng Ji, Debora Sujono, Erik Sudderth. In International Conference on Machine Learning (ICML), 2021.
Learning in Integer Latent Variable Models with Nested Automatic Differentiation. Daniel Sheldon, Kevin Winner, Debora Sujono. In International Conference on Machine Learning (ICML), 2018.
Exact Inference for Integer Latent-Variable Models. Kevin Winner, Debora Sujono, Daniel Sheldon. In International Conference on Machine Learning (ICML), 2017.