Brendan Hogan Rappazzo
I am a second year PhD student in Computer Science at Cornell University advised by professor Carla Gomes in the lab of Computational Sustainability. I completed my undergraduate degree in Biomedical Engineering, with a minor in Physics at the University of Maryland, College Park.
To me, the most exciting prospect of powerful, scalable AI/ML methods is the ability to address sustainability issues. I believe that by studying real world problems we can discover interesting research questions that advance the state and understanding of AI methods.
Specifically, I am interested in the sub-fields of computer vision (primarily segmentation), self-supervised learning, understanding neural networks (calibration, compression, and initialization methods), active learning, reinforcement learning, and HCI.
Examples of my experience with AI applications include material discovery, monitoring of African elephants, large-scale tracking of seagrass disease, and generation of 1/f noise.
GitHub | Google Scholar | Email: firstname.lastname@example.org
Eelgrass Wasting Disease Monitoring
Remote Sensing of Invasive Species
CRYSTAL - Multi-Agent AI System for Materials Phase-Mapping
1/f Noise (Pink Noise) Generation
Image from http://www.eso.org/public/images/eso1122a/
Phase Mapper, HCI Platform for Materials Discovery
Yexiang Xue, Junwen Bai, Ronan Le Bras, Brendan Rappazzo, Richard Bernstein, Johan Bjorck, Liane Longpre, Santosh K. Suram, Robert Bruce van Dover, John M. Gregoire, Carla P. Gomes. Phase-Mapper: An AI Platform to Accelerate High Throughput Materials Discovery. AAAI 2017: 4635-4643 [PDF] Won Innovation Application Award