Rico Angell

I am a Ph.D. candidate 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, automated knowledge base construction, human-centered artificial intelligence, clustering, and natural language processing. 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.

Publications

Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology
Sunil Mohan, Rico Angell, Nick Monath, Andrew McCallum
arXiv preprint
[pdf]

Fairkit, Fairkit, on the Wall, Who’s the Fairest of Them All? Supporting Data Scientists in Training Fair Models
Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith, Sam Witty, Stephen J Giguere, Yuriy Brun.
arXiv preprint
[pdf]

Clustering-based Inference for Biomedical Entity Linking
Rico Angell, Nicholas Monath, Sunil Mohan, Nishant Yadav, Andrew McCallum
To appear NAACL 2021 (long paper)
[pdf]

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+)
[pdf]

Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
Rico Angell and Daniel Sheldon
Conference on Neural Information Processing Systems (NeurIPS) 2018
[pdf]

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
[pdf]

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
[pdf]

A Topological Approach to Hardware Bug Triage
Rico Angell, Ben Oztalay, Andrew DeOrio
Microprocessor and SOC Test and Verification (MTV) 2015
[pdf]

Contact

rangell [at] cs [dot] umass [dot] edu
LinkedIn

College of Information and Computer Science
University of Massachusetts
140 Governors Dr
Amherst, MA 01002