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, optimization, and human-centered artificial intelligence. 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

Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching
Rico Angell, Andrew McCallum
arXiv 2023
[pdf] [code]

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.
EURO Journal of Decision Processes, 2023
[pdf]

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization
Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, Andrew McCallum
EMNLP 2022
[pdf] [code]

Entity Linking via Explicit Mention-Mention Coreference Modeling
Dhruv Agarwal, Rico Angell, Nicholas Monath, Andrew McCallum
NAACL 2022
[pdf] [code]

Interactive Correlation Clustering with Existential Cluster Constraints
Rico Angell, Nicholas Monath, Nishant Yadav, Andrew McCallum
ICML 2022
[pdf] [code]

Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
Nicholas Monath, Nishant Yadav, Rico Angell, Andrew McCallum
EMNLP/CRAC 2021
[pdf]

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

Low Resource Recognition and Linking of Biomedical Concepts from a Large Ontology
Sunil Mohan, Rico Angell, Nick Monath, Andrew McCallum
BCB 2021
[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