I am a third year Ph.D. student in computer science at University of Massachusetts Amherst working with Andrew McCallum on machine learning and natural language processing. My research interests include machine learning, natural language processing, learning with limited data, and information extraction. I am generously supported by the NSF Graduate Research Fellowship. My CV can be found here.
I completed my undergraduate degree at University of Michigan in Ann Arbor in computer science and engineering with a minor in math. I worked with Andrew DeOrio applying machine learning to hardware verification and Grant Schoenebeck on a heuristic for the influence maximization problem.
Relation-Dependent Sampling for Multi-Relational Link Prediction
Arthur Feeney*, Rishabh Gupta*, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari and Tengfei Ma
ICML 2020 Workshop on Graph Representation Learning and Beyond (GRL+)
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
Rico Angell and Daniel Sheldon
Conference on Neural Information Processing Systems (NeurIPS) 2018.
Themis: Automatically Testing Software for Discrimination
Rico Angell, Brittany Johnson, Yuriy Brun and Alexandra Meliou
Demonstrations Track, Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2018.
Don’t Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization Rico Angell and Grant Schoenebeck International Conference on Web and Internet Economics (WINE) 2017.
A Topological Approach to Hardware Bug Triage Rico Angell, Ben Oztalay, Andrew DeOrio Microprocessor and SOC Test and Verification (MTV) 2015.